importance of qualitative research in science technology engineering and mathematics

• Open access
• Published: 10 March 2020

Research and trends in STEM education: a systematic review of journal publications

• Yeping Li 1 ,
• Ke Wang 2 ,
• Yu Xiao 1 &
• Jeffrey E. Froyd 3  

International Journal of STEM Education volume  7 , Article number:  11 ( 2020 ) Cite this article

173k Accesses

177 Citations

5 Altmetric

Metrics details

With the rapid increase in the number of scholarly publications on STEM education in recent years, reviews of the status and trends in STEM education research internationally support the development of the field. For this review, we conducted a systematic analysis of 798 articles in STEM education published between 2000 and the end of 2018 in 36 journals to get an overview about developments in STEM education scholarship. We examined those selected journal publications both quantitatively and qualitatively, including the number of articles published, journals in which the articles were published, authorship nationality, and research topic and methods over the years. The results show that research in STEM education is increasing in importance internationally and that the identity of STEM education journals is becoming clearer over time.

Introduction

A recent review of 144 publications in the International Journal of STEM Education ( IJ - STEM ) showed how scholarship in science, technology, engineering, and mathematics (STEM) education developed between August 2014 and the end of 2018 through the lens of one journal (Li, Froyd, & Wang, 2019 ). The review of articles published in only one journal over a short period of time prompted the need to review the status and trends in STEM education research internationally by analyzing articles published in a wider range of journals over a longer period of time.

With global recognition of the growing importance of STEM education, we have witnessed the urgent need to support research and scholarship in STEM education (Li, 2014 , 2018a ). Researchers and educators have responded to this on-going call and published their scholarly work through many different publication outlets including journals, books, and conference proceedings. A simple Google search with the term “STEM,” “STEM education,” or “STEM education research” all returned more than 450,000,000 items. Such voluminous information shows the rapidly evolving and vibrant field of STEM education and sheds light on the volume of STEM education research. In any field, it is important to know and understand the status and trends in scholarship for the field to develop and be appropriately supported. This applies to STEM education.

Conducting systematic reviews to explore the status and trends in specific disciplines is common in educational research. For example, researchers surveyed the historical development of research in mathematics education (Kilpatrick, 1992 ) and studied patterns in technology usage in mathematics education (Bray & Tangney, 2017 ; Sokolowski, Li, & Willson, 2015 ). In science education, Tsai and his colleagues have conducted a sequence of reviews of journal articles to synthesize research trends in every 5 years since 1998 (i.e., 1998–2002, 2003–2007, 2008–2012, and 2013–2017), based on publications in three main science education journals including, Science Education , the International Journal of Science Education , and the Journal of Research in Science Teaching (e.g., Lin, Lin, Potvin, & Tsai, 2019 ; Tsai & Wen, 2005 ). Erduran, Ozdem, and Park ( 2015 ) reviewed argumentation in science education research from 1998 to 2014 and Minner, Levy, and Century ( 2010 ) reviewed inquiry-based science instruction between 1984 and 2002. There are also many literature reviews and syntheses in engineering and technology education (e.g., Borrego, Foster, & Froyd, 2015 ; Xu, Williams, Gu, & Zhang, 2019 ). All of these reviews have been well received in different fields of traditional disciplinary education as they critically appraise and summarize the state-of-art of relevant research in a field in general or with a specific focus. Both types of reviews have been conducted with different methods for identifying, collecting, and analyzing relevant publications, and they differ in terms of review aim and topic scope, time period, and ways of literature selection. In this review, we systematically analyze journal publications in STEM education research to overview STEM education scholarship development broadly and globally.

The complexity and ambiguity of examining the status and trends in STEM education research

A review of research development in a field is relatively straight forward, when the field is mature and its scope can be well defined. Unlike discipline-based education research (DBER, National Research Council, 2012 ), STEM education is not a well-defined field. Conducting a comprehensive literature review of STEM education research require careful thought and clearly specified scope to tackle the complexity naturally associated with STEM education. In the following sub-sections, we provide some further discussion.

Diverse perspectives about STEM and STEM education

STEM education as explicated by the term does not have a long history. The interest in helping students learn across STEM fields can be traced back to the 1990s when the US National Science Foundation (NSF) formally included engineering and technology with science and mathematics in undergraduate and K-12 school education (e.g., National Science Foundation, 1998 ). It coined the acronym SMET (science, mathematics, engineering, and technology) that was subsequently used by other agencies including the US Congress (e.g., United States Congress House Committee on Science, 1998 ). NSF also coined the acronym STEM to replace SMET (e.g., Christenson, 2011 ; Chute, 2009 ) and it has become the acronym of choice. However, a consensus has not been reached on the disciplines included within STEM.

To clarify its intent, NSF published a list of approved fields it considered under the umbrella of STEM (see http://bit.ly/2Bk1Yp5 ). The list not only includes disciplines widely considered under the STEM tent (called “core” disciplines, such as physics, chemistry, and materials research), but also includes disciplines in psychology and social sciences (e.g., political science, economics). However, NSF’s list of STEM fields is inconsistent with other federal agencies. Gonzalez and Kuenzi ( 2012 ) noted that at least two US agencies, the Department of Homeland Security and Immigration and Customs Enforcement, use a narrower definition that excludes social sciences. Researchers also view integration across different disciplines of STEM differently using various terms such as, multidisciplinary, interdisciplinary, and transdisciplinary (Vasquez, Sneider, & Comer, 2013 ). These are only two examples of the ambiguity and complexity in describing and specifying what constitutes STEM.

Multiple perspectives about the meaning of STEM education adds further complexity to determining the extent to which scholarly activity can be categorized as STEM education. For example, STEM education can be viewed with a broad and inclusive perspective to include education in the individual disciplines of STEM, i.e., science education, technology education, engineering education, and mathematics education, as well as interdisciplinary or cross-disciplinary combinations of the individual STEM disciplines (English, 2016 ; Li, 2014 ). On the other hand, STEM education can be viewed by others as referring only to interdisciplinary or cross-disciplinary combinations of the individual STEM disciplines (Honey, Pearson, & Schweingruber, 2014 ; Johnson, Peters-Burton, & Moore, 2015 ; Kelley & Knowles, 2016 ; Li, 2018a ). These multiple perspectives allow scholars to publish articles in a vast array and diverse journals, as long as journals are willing to take the position as connected with STEM education. At the same time, however, the situation presents considerable challenges for researchers intending to locate, identify, and classify publications as STEM education research. To tackle such challenges, we tried to find out what we can learn from prior reviews related to STEM education.

Guidance from prior reviews related to STEM education

A search for reviews of STEM education research found multiple reviews that could suggest approaches for identifying publications (e.g., Brown, 2012 ; Henderson, Beach, & Finkelstein, 2011 ; Kim, Sinatra, & Seyranian, 2018 ; Margot & Kettler, 2019 ; Minichiello, Hood, & Harkness, 2018 ; Mizell & Brown, 2016 ; Thibaut et al., 2018 ; Wu & Rau, 2019 ). The review conducted by Brown ( 2012 ) examined the research base of STEM education. He addressed the complexity and ambiguity by confining the review with publications in eight journals, two in each individual discipline, one academic research journal (e.g., the Journal of Research in Science Teaching ) and one practitioner journal (e.g., Science Teacher ). Journals were selected based on suggestions from some faculty members and K-12 teachers. Out of 1100 articles published in these eight journals from January 1, 2007, to October 1, 2010, Brown located 60 articles that authors self-identified as connected to STEM education. He found that the vast majority of these 60 articles focused on issues beyond an individual discipline and there was a research base forming for STEM education. In a follow-up study, Mizell and Brown ( 2016 ) reviewed articles published from January 2013 to October 2015 in the same eight journals plus two additional journals. Mizell and Brown used the same criteria to identify and include articles that authors self-identified as connected to STEM education, i.e., if the authors included STEM in the title or author-supplied keywords. In comparison to Brown’s findings, they found that many more STEM articles were published in a shorter time period and by scholars from many more different academic institutions. Taking together, both Brown ( 2012 ) and Mizell and Brown ( 2016 ) tended to suggest that STEM education mainly consists of interdisciplinary or cross-disciplinary combinations of the individual STEM disciplines, but their approach consisted of selecting a limited number of individual discipline-based journals and then selecting articles that authors self-identified as connected to STEM education.

In contrast to reviews on STEM education, in general, other reviews focused on specific issues in STEM education (e.g., Henderson et al., 2011 ; Kim et al., 2018 ; Margot & Kettler, 2019 ; Minichiello et al., 2018 ; Schreffler, Vasquez III, Chini, & James, 2019 ; Thibaut et al., 2018 ; Wu & Rau, 2019 ). For example, the review by Henderson et al. ( 2011 ) focused on instructional change in undergraduate STEM courses based on 191 conceptual and empirical journal articles published between 1995 and 2008. Margot and Kettler ( 2019 ) focused on what is known about teachers’ values, beliefs, perceived barriers, and needed support related to STEM education based on 25 empirical journal articles published between 2000 and 2016. The focus of these reviews allowed the researchers to limit the number of articles considered, and they typically used keyword searches of selected databases to identify articles on STEM education. Some researchers used this approach to identify publications from journals only (e.g., Henderson et al., 2011 ; Margot & Kettler, 2019 ; Schreffler et al., 2019 ), and others selected and reviewed publications beyond journals (e.g., Minichiello et al., 2018 ; Thibaut et al., 2018 ; Wu & Rau, 2019 ).

The discussion in this section suggests possible reasons contributing to the absence of a general literature review of STEM education research and development: (1) diverse perspectives in existence about STEM and STEM education that contribute to the difficulty of specifying a scope of literature review, (2) its short but rapid development history in comparison to other discipline-based education (e.g., science education), and (3) difficulties in deciding how to establish the scope of the literature review. With respect to the third reason, prior reviews have used one of two approaches to identify and select articles: (a) identifying specific journals first and then searching and selecting specific articles from these journals (e.g., Brown, 2012 ; Erduran et al., 2015 ; Mizell & Brown, 2016 ) and (b) conducting selected database searches with keywords based on a specific focus (e.g., Margot & Kettler, 2019 ; Thibaut et al., 2018 ). However, neither the first approach of selecting a limited number of individual discipline-based journals nor the second approach of selecting a specific focus for the review leads to an approach that provides a general overview of STEM education scholarship development based on existing journal publications.

Current review

Two issues were identified in setting the scope for this review.

What time period should be considered?

What publications will be selected for review?

Time period

We start with the easy one first. As discussed above, the acronym STEM did exist until the early 2000s. Although the existence of the acronym does not generate scholarship on student learning in STEM disciplines, it is symbolic and helps focus attention to efforts in STEM education. Since we want to examine the status and trends in STEM education, it is reasonable to start with the year 2000. Then, we can use the acronym of STEM as an identifier in locating specific research articles in a way as done by others (e.g., Brown, 2012 ; Mizell & Brown, 2016 ). We chose the end of 2018 as the end of the time period for our review that began during 2019.

Focusing on publications beyond individual discipline-based journals

As mentioned before, scholars responded to the call for scholarship development in STEM education with publications that appeared in various outlets and diverse languages, including journals, books, and conference proceedings. However, journal publications are typically credited and valued as one of the most important outlets for research exchange (e.g., Erduran et al., 2015 ; Henderson et al., 2011 ; Lin et al., 2019 ; Xu et al., 2019 ). Thus, in this review, we will also focus on articles published in journals in English.

The discourse above on the complexity and ambiguity regarding STEM education suggests that scholars may publish their research in a wide range of journals beyond individual discipline-based journals. To search and select articles from a wide range of journals, we thought about the approach of searching selected databases with keywords as other scholars used in reviewing STEM education with a specific focus. However, existing journals in STEM education do not have a long history. In fact, IJ-STEM is the first journal in STEM education that has just been accepted into the Social Sciences Citation Index (SSCI) (Li, 2019a ). Publications in many STEM education journals are practically not available in several important and popular databases, such as the Web of Science and Scopus. Moreover, some journals in STEM education were not normalized due to a journal’s name change or irregular publication schedule. For example, the Journal of STEM Education was named as Journal of SMET Education when it started in 2000 in a print format, and the journal’s name was not changed until 2003, Vol 4 (3 and 4), and also went fully on-line starting 2004 (Raju & Sankar, 2003 ). A simple Google Scholar search with keywords will not be able to provide accurate information, unless you visit the journal’s website to check all publications over the years. Those added complexities prevented us from taking the database search as a viable approach. Thus, we decided to identify journals first and then search and select articles from these journals. Further details about the approach are provided in the “ Method ” section.

Research questions

Given a broader range of journals and a longer period of time to be covered in this review, we can examine some of the same questions as the IJ-STEM review (Li, Froyd, & Wang, 2019 ), but we do not have access to data on readership, articles accessed, or articles cited for the other journals selected for this review. Specifically, we are interested in addressing the following six research questions:

What were the status and trends in STEM education research from 2000 to the end of 2018 based on journal publications?

What were the patterns of publications in STEM education research across different journals?

Which countries or regions, based on the countries or regions in which authors were located, contributed to journal publications in STEM education?

What were the patterns of single-author and multiple-author publications in STEM education?

What main topics had emerged in STEM education research based on the journal publications?

What research methods did authors tend to use in conducting STEM education research?

Based on the above discussion, we developed the methods for this literature review to follow careful sequential steps to identify journals first and then identify and select STEM education research articles published in these journals from January 2000 to the end of 2018. The methods should allow us to obtain a comprehensive overview about the status and trends of STEM education research based on a systematic analysis of related publications from a broad range of journals and over a longer period of time.

Identifying journals

We used the following three steps to search and identify journals for inclusion:

We assumed articles on research in STEM education have been published in journals that involve more than one traditional discipline. Thus, we used Google to search and identify all education journals with their titles containing either two, three, or all four disciplines of STEM. For example, we did Google search of all the different combinations of three areas of science, mathematics, technology Footnote 1 , and engineering as contained in a journal’s title. In addition, we also searched possible journals containing the word STEAM in the title.

Since STEM education may be viewed as encompassing discipline-based education research, articles on STEM education research may have been published in traditional discipline-based education journals, such as the Journal of Research in Science Teaching . However, there are too many such journals. Yale’s Poorvu Center for Teaching and Learning has listed 16 journals that publish articles spanning across undergraduate STEM education disciplines (see https://poorvucenter.yale.edu/FacultyResources/STEMjournals ). Thus, we selected from the list some individual discipline-based education research journals, and also added a few more common ones such as the Journal of Engineering Education .

Since articles on research in STEM education have appeared in some general education research journals, especially those well-established ones. Thus, we identified and selected a few of those journals that we noticed some publications in STEM education research.

Following the above three steps, we identified 45 journals (see Table  1 ).

Identifying articles

In this review, we will not discuss or define the meaning of STEM education. We used the acronym STEM (or STEAM, or written as the phrase of “science, technology, engineering, and mathematics”) as a term in our search of publication titles and/or abstracts. To identify and select articles for review, we searched all items published in those 45 journals and selected only those articles that author(s) self-identified with the acronym STEM (or STEAM, or written as the phrase of “science, technology, engineering, and mathematics”) in the title and/or abstract. We excluded publications in the sections of practices, letters to editors, corrections, and (guest) editorials. Our search found 798 publications that authors self-identified as in STEM education, identified from 36 journals. The remaining 9 journals either did not have publications that met our search terms or published in another language other than English (see the two separate lists in Table 1 ).

Data analysis

To address research question 3, we analyzed authorship to examine which countries/regions contributed to STEM education research over the years. Because each publication may have either one or multiple authors, we used two different methods to analyze authorship nationality that have been recognized as valuable from our review of IJ-STEM publications (Li, Froyd, & Wang, 2019 ). The first method considers only the corresponding author’s (or the first author, if no specific indication is given about the corresponding author) nationality and his/her first institution affiliation, if multiple institution affiliations are listed. Method 2 considers every author of a publication, using the following formula (Howard, Cole, & Maxwell, 1987 ) to quantitatively assign and estimate each author’s contribution to a publication (and thus associated institution’s productivity), when multiple authors are included in a publication. As an example, each publication is given one credit point. For the publication co-authored by two, the first author would be given 0.6 and the second author 0.4 credit point. For an article contributed jointly by three authors, the three authors would be credited with scores of 0.47, 0.32, and 0.21, respectively.

After calculating all the scores for each author of each paper, we added all the credit scores together in terms of each author’s country/region. For brevity, we present only the top 10 countries/regions in terms of their total credit scores calculated using these two different methods, respectively.

To address research question 5, we used the same seven topic categories identified and used in our review of IJ-STEM publications (Li, Froyd, & Wang, 2019 ). We tested coding 100 articles first to ensure the feasibility. Through test-coding and discussions, we found seven topic categories could be used to examine and classify all 798 items.

K-12 teaching, teacher, and teacher education in STEM (including both pre-service and in-service teacher education)

Post-secondary teacher and teaching in STEM (including faculty development, etc.)

K-12 STEM learner, learning, and learning environment

Post-secondary STEM learner, learning, and learning environments (excluding pre-service teacher education)

Policy, curriculum, evaluation, and assessment in STEM (including literature review about a field in general)

Culture and social and gender issues in STEM education

History, epistemology, and perspectives about STEM and STEM education

To address research question 6, we coded all 798 publications in terms of (1) qualitative methods, (2) quantitative methods, (3) mixed methods, and (4) non-empirical studies (including theoretical or conceptual papers, and literature reviews). We assigned each publication to only one research topic and one method, following the process used in the IJ-STEM review (Li, Froyd, & Wang, 2019 ). When there was more than one topic or method that could have been used for a publication, a decision was made in choosing and assigning a topic or a method. The agreement between two coders for all 798 publications was 89.5%. When topic and method coding discrepancies occurred, a final decision was reached after discussion.

Results and discussion

In the following sections, we report findings as corresponding to each of the six research questions.

The status and trends of journal publications in STEM education research from 2000 to 2018

Figure  1 shows the number of publications per year. As Fig.  1 shows, the number of publications increased each year beginning in 2010. There are noticeable jumps from 2015 to 2016 and from 2017 to 2018. The result shows that research in STEM education had grown significantly since 2010, and the most recent large number of STEM education publications also suggests that STEM education research gained its own recognition by many different journals for publication as a hot and important topic area.

The distribution of STEM education publications over the years

Among the 798 articles, there were 549 articles with the word “STEM” (or STEAM, or written with the phrase of “science, technology, engineering, and mathematics”) included in the article’s title or both title and abstract and 249 articles without such identifiers included in the title but abstract only. The results suggest that many scholars tended to include STEM in the publications’ titles to highlight their research in or about STEM education. Figure  2 shows the number of publications per year where publications are distinguished depending on whether they used the term STEM in the title or only in the abstract. The number of publications in both categories had significant increases since 2010. Use of the acronym STEM in the title was growing at a faster rate than using the acronym only in the abstract.

The trends of STEM education publications with vs. without STEM included in the title

Not all the publications that used the acronym STEM in the title and/or abstract reported on a study involving all four STEM areas. For each publication, we further examined the number of the four areas involved in the reported study.

Figure  3 presents the number of publications categorized by the number of the four areas involved in the study, breaking down the distribution of these 798 publications in terms of the content scope being focused on. Studies involving all four STEM areas are the most numerous with 488 (61.2%) publications, followed by involving one area (141, 17.7%), then studies involving both STEM and non-STEM (84, 10.5%), and finally studies involving two or three areas of STEM (72, 9%; 13, 1.6%; respectively). Publications that used the acronym STEAM in either the title or abstract were classified as involving both STEM and non-STEM. For example, both of the following publications were included in this category.

Dika and D’Amico ( 2016 ). “Early experiences and integration in the persistence of first-generation college students in STEM and non-STEM majors.” Journal of Research in Science Teaching , 53 (3), 368–383. (Note: this article focused on early experience in both STEM and Non-STEM majors.)

Sochacka, Guyotte, and Walther ( 2016 ). “Learning together: A collaborative autoethnographic exploration of STEAM (STEM+ the Arts) education.” Journal of Engineering Education , 105 (1), 15–42. (Note: this article focused on STEAM (both STEM and Arts).)

Publication distribution in terms of content scope being focused on. (Note: 1=single subject of STEM, 2=two subjects of STEM, 3=three subjects of STEM, 4=four subjects of STEM, 5=topics related to both STEM and non-STEM)

Figure  4 presents the number of publications per year in each of the five categories described earlier (category 1, one area of STEM; category 2, two areas of STEM; category 3, three areas of STEM; category 4, four areas of STEM; category 5, STEM and non-STEM). The category that had grown most rapidly since 2010 is the one involving all four areas. Recent growth in the number of publications in category 1 likely reflected growing interest of traditional individual disciplinary based educators in developing and sharing multidisciplinary and interdisciplinary scholarship in STEM education, as what was noted recently by Li and Schoenfeld ( 2019 ) with publications in IJ-STEM.

Publication distribution in terms of content scope being focused on over the years

Patterns of publications across different journals

Among the 36 journals that published STEM education articles, two are general education research journals (referred to as “subject-0”), 12 with their titles containing one discipline of STEM (“subject-1”), eight with journal’s titles covering two disciplines of STEM (“subject-2”), six covering three disciplines of STEM (“subject-3”), seven containing the word STEM (“subject-4”), and one in STEAM education (“subject-5”).

Table  2 shows that both subject-0 and subject-1 journals were usually mature journals with a long history, and they were all traditional subscription-based journals, except the Journal of Pre - College Engineering Education Research , a subject-1 journal established in 2011 that provided open access (OA). In comparison to subject-0 and subject-1 journals, subject-2 and subject-3 journals were relatively newer but still had quite many years of history on average. There are also some more journals in these two categories that provided OA. Subject-4 and subject-5 journals had a short history, and most provided OA. The results show that well-established journals had tended to focus on individual disciplines or education research in general. Multidisciplinary and interdisciplinary education journals were started some years later, followed by the recent establishment of several STEM or STEAM journals.

Table 2 also shows that subject-1, subject-2, and subject-4 journals published approximately a quarter each of the publications. The number of publications in subject-1 journals is interested, because we selected a relatively limited number of journals in this category. There are many other journals in the subject-1 category (as well as subject-0 journals) that we did not select, and thus it is very likely that we did not include some STEM education articles published in subject-0 or subject-1 journals that we did not include in our study.

Figure  5 shows the number of publications per year in each of the five categories described earlier (subject-0 through subject-5). The number of publications per year in subject-5 and subject-0 journals did not change much over the time period of the study. On the other hand, the number of publications per year in subject-4 (all 4 areas), subject-1 (single area), and subject-2 journals were all over 40 by the end of the study period. The number of publications per year in subject-3 journals increased but remained less than 30. At first sight, it may be a bit surprising that the number of publications in STEM education per year in subject-1 journals increased much faster than those in subject-2 journals over the past few years. However, as Table 2 indicates these journals had long been established with great reputations, and scholars would like to publish their research in such journals. In contrast to the trend in subject-1 journals, the trend in subject-4 journals suggests that STEM education journals collectively started to gain its own identity for publishing and sharing STEM education research.

STEM education publication distribution across different journal categories over the years. (Note: 0=subject-0; 1=subject-1; 2=subject-2; 3=subject-3; 4=subject-4; 5=subject-5)

Figure  6 shows the number of STEM education publications in each journal where the bars are color-coded (yellow, subject-0; light blue, subject-1; green, subject-2; purple, subject-3; dark blue, subject-4; and black, subject-5). There is no clear pattern shown in terms of the overall number of STEM education publications across categories or journals, but very much individual journal-based performance. The result indicates that the number of STEM education publications might heavily rely on the individual journal’s willingness and capability of attracting STEM education research work and thus suggests the potential value of examining individual journal’s performance.

Publication distribution across all 36 individual journals across different categories with the same color-coded for journals in the same subject category

The top five journals in terms of the number of STEM education publications are Journal of Science Education and Technology (80 publications, journal number 25 in Fig.  6 ), Journal of STEM Education (65 publications, journal number 26), International Journal of STEM Education (64 publications, journal number 17), International Journal of Engineering Education (54 publications, journal number 12), and School Science and Mathematics (41 publications, journal number 31). Among these five journals, two journals are specifically on STEM education (J26, J17), two on two subjects of STEM (J25, J31), and one on one subject of STEM (J12).

Figure  7 shows the number of STEM education publications per year in each of these top five journals. As expected, based on earlier trends, the number of publications per year increased over the study period. The largest increase was in the International Journal of STEM Education (J17) that was established in 2014. As the other four journals were all established in or before 2000, J17’s short history further suggests its outstanding performance in attracting and publishing STEM education articles since 2014 (Li, 2018b ; Li, Froyd, & Wang, 2019 ). The increase was consistent with the journal’s recognition as the first STEM education journal for inclusion in SSCI starting in 2019 (Li, 2019a ).

Publication distribution of selected five journals over the years. (Note: J12: International Journal of Engineering Education; J17: International Journal of STEM Education; J25: Journal of Science Education and Technology; J26: Journal of STEM Education; J31: School Science and Mathematics)

Top 10 countries/regions where scholars contributed journal publications in STEM education

Table  3 shows top countries/regions in terms of the number of publications, where the country/region was established by the authorship using the two different methods presented above. About 75% (depending on the method) of contributions were made by authors from the USA, followed by Australia, Canada, Taiwan, and UK. Only Africa as a continent was not represented among the top 10 countries/regions. The results are relatively consistent with patterns reported in the IJ-STEM study (Li, Froyd, & Wang, 2019 )

Further examination of Table 3 reveals that the two methods provide not only fairly consistent results but also yield some differences. For example, Israel and Germany had more publication credit if only the corresponding author was considered, but South Korea and Turkey had more publication credit when co-authors were considered. The results in Table 3 show that each method has value when analyzing and comparing publications by country/region or institution based on authorship.

Recognizing that, as shown in Fig. 1 , the number of publications per year increased rapidly since 2010, Table  4 shows the number of publications by country/region over a 10-year period (2009–2018) and Table 5 shows the number of publications by country/region over a 5-year period (2014–2018). The ranks in Tables  3 , 4 , and 5 are fairly consistent, but that would be expected since the larger numbers of publications in STEM education had occurred in recent years. At the same time, it is interesting to note in Table 5 some changes over the recent several years with Malaysia, but not Israel, entering the top 10 list when either method was used to calculate author's credit.

Patterns of single-author and multiple-author publications in STEM education

Since STEM education differs from traditional individual disciplinary education, we are interested in determining how common joint co-authorship with collaborations was in STEM education articles. Figure  8 shows that joint co-authorship was very common among these 798 STEM education publications, with 83.7% publications with two or more co-authors. Publications with two, three, or at least five co-authors were highest, with 204, 181, and 157 publications, respectively.

Number of publications with single or different joint authorship. (Note: 1=single author; 2=two co-authors; 3=three co-authors; 4=four co-authors; 5=five or more co-authors)

Figure  9 shows the number of publications per year using the joint authorship categories in Fig.  8 . Each category shows an increase consistent with the increase shown in Fig. 1 for all 798 publications. By the end of the time period, the number of publications with two, three, or at least five co-authors was the largest, which might suggest an increase in collaborations in STEM education research.

Publication distribution with single or different joint authorship over the years. (Note: 1=single author; 2=two co-authors; 3=three co-authors; 4=four co-authors; 5=five or more co-authors)

Co-authors can be from the same or different countries/regions. Figure  10 shows the number of publications per year by single authors (no collaboration), co-authors from the same country (collaboration in a country/region), and co-authors from different countries (collaboration across countries/regions). Each year the largest number of publications was by co-authors from the same country, and the number increased dramatically during the period of the study. Although the number of publications in the other two categories increased, the numbers of publications were noticeably fewer than the number of publications by co-authors from the same country.

Publication distribution in authorship across different categories in terms of collaboration over the years

Published articles by research topics

Figure  11 shows the number of publications in each of the seven topic categories. The topic category of goals, policy, curriculum, evaluation, and assessment had almost half of publications (375, 47%). Literature reviews were included in this topic category, as providing an overview assessment of education and research development in a topic area or a field. Sample publications included in this category are listed as follows:

DeCoito ( 2016 ). “STEM education in Canada: A knowledge synthesis.” Canadian Journal of Science , Mathematics and Technology Education , 16 (2), 114–128. (Note: this article provides a national overview of STEM initiatives and programs, including success, criteria for effective programs and current research in STEM education.)

Ring-Whalen, Dare, Roehrig, Titu, and Crotty ( 2018 ). “From conception to curricula: The role of science, technology, engineering, and mathematics in integrated STEM units.” International Journal of Education in Mathematics Science and Technology , 6 (4), 343–362. (Note: this article investigates the conceptions of integrated STEM education held by in-service science teachers through the use of photo-elicitation interviews and examines how those conceptions were reflected in teacher-created integrated STEM curricula.)

Schwab et al. ( 2018 ). “A summer STEM outreach program run by graduate students: Successes, challenges, and recommendations for implementation.” Journal of Research in STEM Education , 4 (2), 117–129. (Note: the article details the organization and scope of the Foundation in Science and Mathematics Program and evaluates this program.)

Frequencies of publications’ research topic distributions. (Note: 1=K-12 teaching, teacher and teacher education; 2=Post-secondary teacher and teaching; 3=K-12 STEM learner, learning, and learning environment; 4=Post-secondary STEM learner, learning, and learning environments; 5=Goals and policy, curriculum, evaluation, and assessment (including literature review); 6=Culture, social, and gender issues; 7=History, philosophy, Epistemology, and nature of STEM and STEM education)

The topic with the second most publications was “K-12 teaching, teacher and teacher education” (103, 12.9%), followed closely by “K-12 learner, learning, and learning environment” (97, 12.2%). The results likely suggest the research community had a broad interest in both teaching and learning in K-12 STEM education. The top three topics were the same in the IJ-STEM review (Li, Froyd, & Wang, 2019 ).

Figure  11 also shows there was a virtual tie between two topics with the fourth most cumulative publications, “post-secondary STEM learner & learning” (76, 9.5%) and “culture, social, and gender issues in STEM” (78, 9.8%), such as STEM identity, students’ career choices in STEM, and inclusion. This result is different from the IJ-STEM review (Li, Froyd, & Wang, 2019 ), where “post-secondary STEM teacher & teaching” and “post-secondary STEM learner & learning” were tied as the fourth most common topics. This difference is likely due to the scope of journals and the length of the time period being reviewed.

Figure  12 shows the number of publications per year in each topic category. As expected from the results in Fig.  11 the number of publications in topic category 5 (goals, policy, curriculum, evaluation, and assessment) was the largest each year. The numbers of publications in topic category 3 (K-12 learner, learning, and learning environment), 1 (K-12 teaching, teacher, and teacher education), 6 (culture, social, and gender issues in STEM), and 4 (post-secondary STEM learner and learning) were also increasing. Although Fig.  11 shows the number of publications in topic category 1 was slightly more than the number of publications in topic category 3 (see Fig.  11 ), the number of publications in topic category 3 was increasing more rapidly in recent years than its counterpart in topic category 1. This may suggest a more rapidly growing interest in K-12 STEM learner, learning, and learning environment. The numbers of publications in topic categories 2 and 7 were not increasing, but the number of publications in IJ-STEM in topic category 2 was notable (Li, Froyd, & Wang, 2019 ). It will be interesting to follow trends in the seven topic categories in the future.

Publication distributions in terms of research topics over the years

Published articles by research methods

Figure  13 shows the number of publications per year by research methods in empirical studies. Publications with non-empirical studies are shown in a separate category. Although the number of publications in each of the four categories increased during the study period, there were many more publications presenting empirical studies than those without. For those with empirical studies, the number of publications using quantitative methods increased most rapidly in recent years, followed by qualitative and then mixed methods. Although there were quite many publications with non-empirical studies (e.g., theoretical or conceptual papers, literature reviews) during the study period, the increase of the number of publications in this category was noticeably less than empirical studies.

Publication distributions in terms of research methods over the years. (Note: 1=qualitative, 2=quantitative, 3=mixed, 4=Non-empirical)

Concluding remarks

The systematic analysis of publications that were considered to be in STEM education in 36 selected journals shows tremendous growth in scholarship in this field from 2000 to 2018, especially over the past 10 years. Our analysis indicates that STEM education research has been increasingly recognized as an important topic area and studies were being published across many different journals. Scholars still hold diverse perspectives about how research is designated as STEM education; however, authors have been increasingly distinguishing their articles with STEM, STEAM, or related words in the titles, abstracts, and lists of keywords during the past 10 years. Moreover, our systematic analysis shows a dramatic increase in the number of publications in STEM education journals in recent years, which indicates that these journals have been collectively developing their own professional identity. In addition, the International Journal of STEM Education has become the first STEM education journal to be accepted in SSCI in 2019 (Li, 2019a ). The achievement may mark an important milestone as STEM education journals develop their own identity for publishing and sharing STEM education research.

Consistent with our previous reviews (Li, Froyd, & Wang, 2019 ; Li, Wang, & Xiao, 2019 ), the vast majority of publications in STEM education research were contributed by authors from the USA, where STEM and STEAM education originated, followed by Australia, Canada, and Taiwan. At the same time, authors in some countries/regions in Asia were becoming very active in the field over the past several years. This trend is consistent with findings from the IJ-STEM review (Li, Froyd, & Wang, 2019 ). We certainly hope that STEM education scholarship continues its development across all five continents to support educational initiatives and programs in STEM worldwide.

Our analysis has shown that collaboration, as indicated by publications with multiple authors, has been very common among STEM education scholars, as that is often how STEM education distinguishes itself from the traditional individual disciplinary based education. Currently, most collaborations occurred among authors from the same country/region, although collaborations across cross-countries/regions were slowly increasing.

With the rapid changes in STEM education internationally (Li, 2019b ), it is often difficult for researchers to get an overall sense about possible hot topics in STEM education especially when STEM education publications appeared in a vast array of journals across different fields. Our systematic analysis of publications has shown that studies in the topic category of goals, policy, curriculum, evaluation, and assessment have been the most prevalent, by far. Our analysis also suggests that the research community had a broad interest in both teaching and learning in K-12 STEM education. These top three topic categories are the same as in the IJ-STEM review (Li, Froyd, & Wang, 2019 ). Work in STEM education will continue to evolve and it will be interesting to review the trends in another 5 years.

Encouraged by our recent IJ-STEM review, we began this review with an ambitious goal to provide an overview of the status and trends of STEM education research. In a way, this systematic review allowed us to achieve our initial goal with a larger scope of journal selection over a much longer period of publication time. At the same time, there are still limitations, such as the decision to limit the number of journals from which we would identify publications for analysis. We understand that there are many publications on STEM education research that were not included in our review. Also, we only identified publications in journals. Although this is one of the most important outlets for scholars to share their research work, future reviews could examine publications on STEM education research in other venues such as books, conference proceedings, and grant proposals.

Availability of data and materials

The data and materials used and analyzed for the report are publicly available at the various journal websites.

Journals containing the word "computers" or "ICT" appeared automatically when searching with the word "technology". Thus, the word of "computers" or "ICT" was taken as equivalent to "technology" if appeared in a journal's name.

Abbreviations

Information and Communications Technology

International Journal of STEM Education

Kindergarten–Grade 12

Science, Mathematics, Engineering, and Technology

Science, Technology, Engineering, Arts, and Mathematics

Science, Technology, Engineering, and Mathematics

Borrego, M., Foster, M. J., & Froyd, J. E. (2015). What is the state of the art of systematic review in engineering education? Journal of Engineering Education, 104 (2), 212–242. https://doi.org/10.1002/jee.20069 .

Article   Google Scholar  

Bray, A., & Tangney, B. (2017). Technology usage in mathematics education research – a systematic review of recent trends. Computers & Education, 114 , 255–273.

Brown, J. (2012). The current status of STEM education research. Journal of STEM Education: Innovations & Research, 13 (5), 7–11.

Google Scholar  

Christenson, J. (2011). Ramaley coined STEM term now used nationwide . Winona Daily News Retrieved from http://www.winonadailynews.com/news/local/article_457afe3e-0db3-11e1-abe0-001cc4c03286.html Accessed on 16 Jan 2018.

Chute, E. (2009). STEM education is branching out . Pittsburgh Post-Gazette Feb 9, 2009. https://www.post-gazette.com/news/education/2009/02/10/STEM-education-is-branching-out/stories/200902100165 Accessed on 2 Jan 2020.

DeCoito, I. (2016). STEM education in Canada: A knowledge synthesis. Canadian Journal of Science, Mathematics and Technology Education, 16 (2), 114–128.

Dika, S. L., & D'Amico, M. M. (2016). Early experiences and integration in the persistence of first-generation college students in STEM and non-STEM majors. Journal of Research in Science Teaching, 53 (3), 368–383.

English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education, 3 , 3. https://doi.org/10.1186/s4059%204-016-0036-1 .

Erduran, S., Ozdem, Y., & Park, J.-Y. (2015). Research trends on argumentation in science education: A journal content analysis from 1998-2014. International Journal of STEM Education, 2 , 5. https://doi.org/10.1186/s40594-015-0020-1 .

Gonzalez, H. B. & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. CRS report for congress, R42642, https://fas.org/sgp/crs/misc/R42642.pdf Accessed on 2 Jan 2020.

Henderson, C., Beach, A., & Finkelstein, N. (2011). Facilitating change in undergraduate STEM instructional practices: An analytic review of the literature. Journal of Research in Science Teaching, 48 (8), 952–984.

Honey, M., Pearson, G., & Schweingruber, A. (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research . Washington: National Academies Press.

Howard, G. S., Cole, D. A., & Maxwell, S. E. (1987). Research productivity in psychology based on publication in the journals of the American Psychological Association. American Psychologist, 42 (11), 975–986.

Johnson, C. C., Peters-Burton, E. E., & Moore, T. J. (2015). STEM roadmap: A framework for integration . London: Taylor & Francis.

Book   Google Scholar  

Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3 , 11. https://doi.org/10.1186/s40594-016-0046-z .

Kilpatrick, J. (1992). A history of research in mathematics education. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 3–38). New York: Macmillan.

Kim, A. Y., Sinatra, G. M., & Seyranian, V. (2018). Developing a STEM identity among young women: A social identity perspective. Review of Educational Research, 88 (4), 589–625.

Li, Y. (2014). International journal of STEM education – a platform to promote STEM education and research worldwide. International Journal of STEM Education, 1 , 1. https://doi.org/10.1186/2196-7822-1-1 .

Li, Y. (2018a). Journal for STEM education research – promoting the development of interdisciplinary research in STEM education. Journal for STEM Education Research, 1 (1–2), 1–6. https://doi.org/10.1007/s41979-018-0009-z .

Li, Y. (2018b). Four years of development as a gathering place for international researchers and readers in STEM education. International Journal of STEM Education, 5 , 54. https://doi.org/10.1186/s40594-018-0153-0 .

Li, Y. (2019a). Five years of development in pursuing excellence in quality and global impact to become the first journal in STEM education covered in SSCI. International Journal of STEM Education, 6 , 42. https://doi.org/10.1186/s40594-019-0198-8 .

Li, Y. (2019b). STEM education research and development as a rapidly evolving and international field. 数学教育学报(Journal of Mathematics Education), 28 (3), 42–44.

Li, Y., Froyd, J. E., & Wang, K. (2019). Learning about research and readership development in STEM education: A systematic analysis of the journal’s publications from 2014 to 2018. International Journal of STEM Education, 6 , 19. https://doi.org/10.1186/s40594-019-0176-1 .

Li, Y., & Schoenfeld, A. H. (2019). Problematizing teaching and learning mathematics as ‘given’ in STEM education. International Journal of STEM Education, 6 , 44. https://doi.org/10.1186/s40594-019-0197-9 .

Li, Y., Wang, K., & Xiao, Y. (2019). Exploring the status and development trends of STEM education research: A review of research articles in selected journals published between 2000 and 2018. 数学教育学报(Journal of Mathematics Education), 28 (3), 45–52.

Lin, T.-J., Lin, T.-C., Potvin, P., & Tsai, C.-C. (2019). Research trends in science education from 2013 to 2017: A systematic content analysis of publications in selected journals. International Journal of Science Education, 41 (3), 367–387.

Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: A systematic literature review. International Journal of STEM Education, 6 , 2. https://doi.org/10.1186/s40594-018-0151-2 .

Minichiello, A., Hood, J. R., & Harkness, D. S. (2018). Bring user experience design to bear on STEM education: A narrative literature review. Journal for STEM Education Research, 1 (1–2), 7–33.

Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction – what is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47 (4), 474–496.

Mizell, S., & Brown, S. (2016). The current status of STEM education research 2013-2015. Journal of STEM Education: Innovations & Research, 17 (4), 52–56.

National Research Council. (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering . Washington DC: National Academies Press.

National Science Foundation (1998). Information technology: Its impact on undergraduate education in science, mathematics, engineering, and technology. (NSF 98–82), April 18–20, 1996. http://www.nsf.gov/cgi-bin/getpub?nsf9882 Accessed 16 Jan 2018.

Raju, P. K., & Sankar, C. S. (2003). Editorial. Journal of STEM Education: Innovations & Research, 4 (3&4), 2.

Ring-Whalen, E., Dare, E., Roehrig, G., Titu, P., & Crotty, E. (2018). From conception to curricula: The role of science, technology, engineering, and mathematics in integrated STEM units. International Journal of Education in Mathematics, Science and Technology, 6 (4), 343–362.

Schreffler, J., Vasquez III, E., Chini, J., & James, W. (2019). Universal design for learning in postsecondary STEM education for students with disabilities: A systematic literature review. International Journal of STEM Education, 6 , 8. https://doi.org/10.1186/s40594-019-0161-8 .

Schwab, D. B., Cole, L. W., Desai, K. M., Hemann, J., Hummels, K. R., & Maltese, A. V. (2018). A summer STEM outreach program run by graduate students: Successes, challenges, and recommendations for implementation. Journal of Research in STEM Education, 4 (2), 117–129.

Sochacka, N. W., Guyotte, K. W., & Walther, J. (2016). Learning together: A collaborative autoethnographic exploration of STEAM (STEM+ the Arts) education. Journal of Engineering Education, 105 (1), 15–42.

Sokolowski, A., Li, Y., & Willson, V. (2015). The effects of using exploratory computerized environments in grades 1 to 8 mathematics: A meta-analysis of research. International Journal of STEM Education, 2 , 8. https://doi.org/10.1186/s40594-015-0022-z .

Thibaut, L., Ceuppens, S., De Loof, H., De Meester, J., Goovaerts, L., Struyf, A., Pauw, J. B., Dehaene, W., Deprez, J., De Cock, M., Hellinckx, L., Knipprath, H., Langie, G., Struyven, K., Van de Velde, D., Van Petegem, P., & Depaepe, F. (2018). Integrated STEM education: A systematic review of instructional practices in secondary education. European Journal of STEM Education, 3 (1), 2.

Tsai, C. C., & Wen, L. M. C. (2005). Research and trends in science education from 1998 to 2002: A content analysis of publication in selected journals. International Journal of Science Education, 27 (1), 3–14.

United States Congress House Committee on Science. (1998). The state of science, math, engineering, and technology (SMET) education in America, parts I-IV, including the results of the Third International Mathematics and Science Study (TIMSS): hearings before the Committee on Science, U.S. House of Representatives, One Hundred Fifth Congress, first session, July 23, September 24, October 8 and 29, 1997. Washington: U.S. G.P.O.

Vasquez, J., Sneider, C., & Comer, M. (2013). STEM lesson essentials, grades 3–8: Integrating science, technology, engineering, and mathematics . Portsmouth, NH: Heinemann.

Wu, S. P. W., & Rau, M. A. (2019). How students learn content in science, technology, engineering, and mathematics (STEM) through drawing activities. Educational Psychology Review . https://doi.org/10.1007/s10648-019-09467-3 .

Xu, M., Williams, P. J., Gu, J., & Zhang, H. (2019). Hotspots and trends of technology education in the International Journal of Technology and Design Education: 2000-2018. International Journal of Technology and Design Education . https://doi.org/10.1007/s10798-019-09508-6 .

Download references

Not applicable

Author information

Authors and affiliations.

Texas A&M University, College Station, TX, 77843-4232, USA

Yeping Li & Yu Xiao

Nicholls State University, Thibodaux, LA, 70310, USA

Ohio State University, Columbus, OH, 43210, USA

Jeffrey E. Froyd

You can also search for this author in PubMed   Google Scholar

Contributions

YL conceptualized the study and drafted the manuscript. KW and YX contributed with data collection, coding, and analyses. JEF reviewed drafts and contributed to manuscript revisions. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yeping Li .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Li, Y., Wang, K., Xiao, Y. et al. Research and trends in STEM education: a systematic review of journal publications. IJ STEM Ed 7 , 11 (2020). https://doi.org/10.1186/s40594-020-00207-6

Download citation

Received : 10 February 2020

Accepted : 12 February 2020

Published : 10 March 2020

DOI : https://doi.org/10.1186/s40594-020-00207-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Journal publication
• Literature review
• STEM education research

We apologize for the inconvenience...

To ensure we keep this website safe, please can you confirm you are a human by ticking the box below.

If you are unable to complete the above request please contact us using the below link, providing a screenshot of your experience.

https://ioppublishing.org/contacts/

An official website of the United States government

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS. A lock ( Lock Locked padlock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

News quick access

• For News Media
• Media Advisories
• Multimedia Gallery
• News Archive
• Search News
• Special Reports
• Speeches and Lectures

Research in Science, Technology, Engineering and Math (STEM) Education

Event at NSF offers the latest from researchers on STEM learning, and the best approaches for teaching STEM subjects

STEM education researchers are drawing on cognitive sciences to better understand student learning.

• Credit and Larger Version

February 10, 2009

This material is available primarily for archival purposes. Telephone numbers or other contact information may be out of date; please see current contact information at media contacts .

STEM education researchers are increasingly drawing on the cognitive sciences for a more detailed understanding of how students learn, and researchers are investigating topics as diverse as the neural basis for learning mathematics, and how virtual environments support scientific inquiry.

Advancements in education research are prompting NSF to ask: Where is the cutting edge in education research? How can we best support it? What are its implications down the road for teaching and learning?

Among NSF's programs supporting basic research into how students learn STEM, and what approaches are most effective in teaching them, is a program called, Research and Evaluation on Education in Science and Engineering (REESE).

Eleven researchers funded through REESE are participating in an event at NSF headquarters where they will be presenting information about their ongoing work. The event, which is open to the public, takes place on Thurs., Feb. 19, from 3:30-5:00 p.m. in the first-floor atrium at NSF headquarters, 4201 Wilson Boulevard, Arlington, Va.

Remarks on ongoing directions and priorities will be given at 4:00 p.m. The event will include light refreshments.

Among the researchers scheduled to attend are:

• Janet Hyde, University of Wisconsin-Madison-- Recently featured in the New York Times and other media for her work on gender and math learning, she will discuss three different projects related to math learning, including one on mothers' interactions with children doing mathematics.
• Jeremy Roschelle, SRI International-- Recently interviewed for NSF's multimedia special report, "Math: What's the Problem?" , will discuss his projects, which have addressed issues of how to teach and scale up the teaching of important mathematics concepts to middle school students using innovative technologies.
• Sarah Brem from Arizona State University-- She will discuss her study synthesizing what we know from cognitive scientists, science educators, biologists and teachers about the challenges of learning about and teaching evolution.
• Kathryn Borman, University of South Florida-- Her research draws on the wealth of data from the state of Florida about students in public postsecondary institutions. Through her research projects, Borman examines student pathways all the way from high school through college and into their later career placements in STEM or other fields.
• Bruce McCandliss, Vanderbilt University-- He will discuss his new research project that is using brain imaging measures, including brain structure (MRI, DTI), bloodflow changes (fMRI), and electrical responses (ERP) to analyze early math and number skills in first graders and kindergartners.

Attendees will have an opportunity to hear from all of the participants about their work. Media interviews may be arranged by contacting Maria Zacharias in NSF's Office of Legislative and Public Affairs (OLPA) at [email protected] or 703-292-8454.

Individuals with special needs, who would like to request accomodations, may contact Nicole Harris for assistance at [email protected] or 703-292-7155 by Monday, February 16, 2009.

A complete list of participants follows:

• Kathryn Borman, The Alliance for Applied Research in Education and Anthropology, University of South Florida, Understanding Factors that Sustain STEM Career Pathways , and On-Track for STEM Careers: Access to Rigorous and Relevant STEM Courses in Florida's High Schools
• Sarah Brem, Mary Lou Fulton College of Education, Arizona State University, Collaborative Research: Facing the Challenges of Learning and Teaching About Evolution: A Synthesis
• Janet Hyde, Department of Psychology, University of Wisconsin-Madison, Mothers' Interactions with Children Doing Mathematics ; New Trends in Gender and Mathematics Performance: Meta-Analytic Synthesis ; and Parents, Utility Value, and Motivating Adolescents in Mathematics and Science
• Richard Ingersoll, Graduate School of Education, University of Pennsylvania, The Effects of Accountability and Teacher Preparation on Mathematics and Science Teacher Retention
• Robert Jacobs, Brain & Cognitive Sciences, University of Rochester, A Machine Learning Approach to Human Visual Learning
• Lynn S. Liben, Department of Psychology, Pennsylvania State University, Collaborative Research: Constructing Mental Images of Geologic Structures from Field Observations and Collaborative Research/Applied Research: Map and Spatial Skills of Children and Teachers, and How They Are Affected by the "Where are We?" Instructional Materials
• Richard Lehrer, Department of Teaching and Learning, Vanderbilt University, Constructing Data, Modeling Worlds: Collaborative Investigation of Statistical Reasoning
• Bruce McCandliss, Psychological Sciences, Vanderbilt University, Brain Correlates of Early Math and Number Skills: Tracing Changes Related to Age and Instruction in a Natural Experiment
• Roxana A. Moreno, Educational Psychology Program, PECASE: Bridging the Gap Between Theory and Practice in Teacher Education: Guided Interactive Virtual Environments (GIVEs) for Case-Based Learning
• Jeremy Roschelle, Center for Technology in Learning, SRI International, Working with Teachers and Leveraging Technology to Scale Opportunities to Learn More Complex and Conceptually Difficult Middle School Mathematics
• Nancy Songer, School of Education, University of Michigan, DeepThink: Thinking Deeply about Biodiversity and Ecology ; and BioKIDS: Kids' Inquiry of Diverse Species

Media Contacts Maria C. Zacharias, NSF, (703) 292-8454, email: [email protected]

The U.S. National Science Foundation propels the nation forward by advancing fundamental research in all fields of science and engineering. NSF supports research and people by providing facilities, instruments and funding to support their ingenuity and sustain the U.S. as a global leader in research and innovation. With a fiscal year 2023 budget of $9.5 billion, NSF funds reach all 50 states through grants to nearly 2,000 colleges, universities and institutions. Each year, NSF receives more than 40,000 competitive proposals and makes about 11,000 new awards. Those awards include support for cooperative research with industry, Arctic and Antarctic research and operations, and U.S. participation in international scientific efforts.

Connect with us online NSF website: nsf.gov NSF News: nsf.gov/news For News Media: nsf.gov/news/newsroom Statistics: nsf.gov/statistics/ Awards database: nsf.gov/awardsearch/

Follow us on social Twitter: twitter.com/NSF Facebook: facebook.com/US.NSF Instagram: instagram.com/nsfgov

• Open access
• Published: 01 February 2023

Investigating the presence of mathematics and the levels of cognitively demanding mathematical tasks in integrated STEM units

• Elizabeth N. Forde   ORCID: orcid.org/0000-0002-3166-9546 1 ,
• Latanya Robinson 2 ,
• Joshua A. Ellis 2 &
• Emily A. Dare 2  

Disciplinary and Interdisciplinary Science Education Research volume  5 , Article number:  3 ( 2023 ) Cite this article

2867 Accesses

7 Citations

2 Altmetric

Metrics details

Effective K-12 integrated STEM education should reflect an intentional effort to adequately represent and facilitate each of its component disciplines in a meaningful way. However, most research in this space has been conducted within the context of science classrooms, ignoring mathematics. Also missing from the literature is research that examines the level of cognitive demand required from mathematical tasks present within integrated STEM lessons. In order to seek insight pertaining to this gap in the literature, we sought to better understand how science teachers use mathematics within K-12 integrated STEM instruction. We used an explanatory sequential mixed methods research design to explore the enactment of mathematics in integrated STEM lessons that focus on physical, earth, and life science content. We first examined 2030 sets of video-recorded classroom observation scores generated from the 10-item STEM Observation Protocol (STEM-OP) designed for observing integrated STEM education in K-12 classrooms. We compared the STEM-OP scores of classroom observations that included mathematics with those that did not. This quantitative analysis was followed by a closer, more in-depth qualitative examination of how mathematics was employed, focusing on the degree of cognitive demand. To do this, we coded and analyzed transcripts from video-recorded classroom observations in which mathematical content was present. Our study yielded two main findings about mathematics in integrated STEM lessons: (1) the presence of mathematical content resulted in higher STEM-OP scores on nearly all items, and (2) mathematical tasks within these lessons were categorized as requiring mainly low levels of cognitive demand from students. This study highlights the need for the increased inclusion of mathematical tasks in integrated STEM teaching. Implications for including higher-order mathematical thinking within integrated STEM teaching are discussed.

Introduction

With an increased desire to draw individuals into the science, technology, engineering, and mathematics (STEM) fields to meet current workforce demands and compete in the global economy (Wang et al., 2011 ), many countries have heightened their focus on STEM education within recent years. In the United States, there is a projected increase in STEM occupations of 10.5% as compared to non-STEM occupations of 7.5% for the period 2020 to 2030 (U.S. Bureau of Labor Statistics, 2021 ). Wang et al. ( 2011 ) noted that the problems being faced in our changing global society are multidisciplinary and require the integration of STEM-related knowledge and skills to create feasible solutions. Although an explicit, clear definition for integrated STEM education has not been agreed upon (Angier, 2010 ; Dare et al., 2019 ; Bybee, 2013 ; Moore et al., 2020 ), many scholars share an increased interest in its importance and inclusion in education, specifically within K-12 spaces.

A range of STEM competencies is developed through integrated STEM education according to the varying requirements posed by a particular situation or a problem (McLoughlin et al., 2020 ). Bybee ( 2013 ) suggested that these competencies include a combination of conceptual understanding as well as procedural skills and abilities for individuals to address STEM-related social and global issues. He further explained that these concepts and skills embody the integration of STEM disciplines. Hence, a quality integrated STEM education experience ought to reflect an approach that seeks to effectively integrate the four disciplines of science, technology, engineering, and mathematics (Johnson et al., 2020 ). A joint statement from The National Council of Supervisors of Mathematics (NCSM) and the National Council of Teachers of Mathematics (NCTM) (NCSM & NCTM, 2018 ) purports that any integrated STEM activity that aims to address the effective incorporation of mathematics should do so with integrity and target the grade level’s mathematics content and appropriate mathematical practices. Currently, though, the majority of research related to integrated STEM education has taken place within the context of science classrooms (e.g., English, 2016 ), which offer a space for reasoning abstractly and quantitatively, as described in A Framework for K-12 Science Education (National Research Council, 2012 ) and the Next Generation Science Standards (NGSS Lead States, 2013 ). It is clear that additional research is needed to determine the ways in which learning across disciplines can support integration so that one discipline does not dominate the others (English, 2016 ).

Although Fitzallen ( 2015 ) and Maass et al. ( 2019 ) examined the role of mathematics in integrated STEM education, other scholars have noted an under-representation of mathematics activities and practices used in STEM education (English, 2016 ; Marginson et al., 2013 ; Shaughnessy, 2013 ). English ( 2016 ) and Stohlmann ( 2018 ) noted that despite the inclusion of mathematical content and concepts within integrated STEM curricula, the significance and level of mathematical thinking required from students remain unclear. This is problematic as Maass et al. ( 2019 ) acknowledged that although mathematics undoubtedly supports the other three STEM disciplines, its understated and underrepresented role in integrated STEM education cannot go unnoticed. For instance, Ring et al. ( 2017 ) and Ring-Whalen et al. ( 2018 ) found that science teachers’ conceptualizations of integrated STEM often relegated mathematics (along with technology) as a tool to be used in support of science and engineering learning. Consequently, elevating the role of mathematics within the broader context of integrated STEM teaching and learning is needed. In order to make that happen, we first must understand how exactly mathematics is being utilized, especially with respect to the level of mathematical thinking and cognitive demand.

This dearth of literature related to mathematics within integrated STEM teaching has not gone unnoticed. English ( 2016 ) noted that of the four STEM disciplines, student outcomes in mathematics were the lowest when compared to the other three disciplines. Hence, English ( 2016 ) suggested that this anomaly was worth further scrutiny. Additionally, Stohlmann ( 2018 ) called for a focus on how the content of mathematics included within integrated STEM ties to the other STEM content to make the learning of mathematics more explicit. In response to these calls for research on mathematics in integrated STEM education, the current study sought to address the following research questions: (1) How do integrated STEM lessons that include mathematics perform on an integrated STEM observational protocol compared to lessons without mathematics? and (2) What levels of mathematical cognitive demand are represented in physical, earth, and life science integrated STEM units?

Literature review

Mathematics and stemintegration.

In a call for greater emphasis on STEM disciplinary integration, English ( 2016 ) reiterated that mathematics, along with engineering are notably underrepresented in studies of STEM education and subsequently called for “lifting the profile of mathematics in STEM integration” (p. 4). Although research has made some initial progress along these lines and attempts to understand mathematics inclusion within integrated STEM educational contexts are present in the literature, it is primarily based on few empirical studies (e.g., Becker & Park, 2011 ; Hurley, 2001 ) that investigated how different integrated approaches can support mathematics learning. The different approaches can include coordination across disciplines or complementary overlapping across disciplines (Bybee, 2013 ), and the distinct characteristics of the specific approach make mathematics achievement challenging (National Academy of Engineering and National Research Council [NAE and NRC], 2014 ). Becker and Park ( 2011 ) meta-analysis focused on the effects of integrative approaches among STEM subjects while considering eight different combinations of STEM disciplines (e.g., science and mathematics; engineering and technology; science, technology, and mathematics). Mathematics achievement showed the smallest effect size when paired with another discipline (Becker & Park, 2011 ). Similarly, Hurley’s ( 2001 ) meta-analysis of the effects of five different integrated teaching approaches for mathematics and science (i.e., sequenced, parallel, partial, enhanced, and total) found similar results concerning mathematics achievement. However, Becker & Park’s ( 2011 ) work also revealed differences in effect sizes of mathematics achievement when comparing across the types of integration. For example, in the sequence integration approach, the effect size of mathematics achievement was large when compared to that of the parallel and total integration approaches. These findings suggest that when mathematics is included in integrated STEM that attention needs to be paid to how mathematics is paired with other disciplines and the integrated teaching approach(es) to support mathematics achievement.

Whereas these two studies focused on discipline combination and integrated approaches, there is research that looks more specifically at how mathematical content and practices can be incorporated into discipline integration. For instance, Baldinger et al.’s ( 2020 ) literature review of 32 published studies from 2013 to 2018 focused on mathematical topics, proficiencies, and practices to determine how mathematics is integrated with other disciplines. Baldinger et al. ( 2020 ) then noted that currently within discipline integration practices, mathematics serves as a supporting role for science, technology, or engineering learning goals, while the associated conceptual mathematics learning goals are essentially overlooked. Furthermore, Stohlmann’s ( 2018 ) analysis of 21 studies from 2008 to 2018 examined the outcome of mathematics learning in discipline integration by considering content integration. Stohlmann ( 2018 ) suggested the fact that mathematics is not emphasized in integrated STEM may partially be a result of the “perception that mathematics achievement is difficult to promote through STEM integration” (p. 317). In terms of content integration, Moore, Stohlmann, et al., ( 2014 ) stated that integrated STEM lessons with an emphasis on content integration with learning objectives from mathematics and another discipline should focus on the central idea that connects the disciplines. Additionally, for effective development of mathematics concepts and skills, Stohlmann ( 2018 ) reiterated that it is essential to attend to the natural connections between mathematics and other STEM disciplines in integrated lessons.

When addressing content integration as proposed above, the NAE and NRC ( 2014 ) noted the challenges of leveraging similarities between overlapping content presented in the different disciplines’ standards to develop discipline-specific knowledge. One challenge is that the shared content is presented in different ways for the subjects in their respective fields. For example, according to the Standards for Technological and Engineering Literacy ( 2020 ), geometry is necessary within science, engineering, and technology. The mathematics topic of geometry involves determining congruence and similarity in mathematics using physical models, transparencies, or geometry software. Particularly, this mathematics topic ties engineering and technology to the design solutions and supports the concept of developing models in science (NAE and NRC, 2014 ). The challenge lies in how to effectively convey the underpinning mathematical ideas of the content when the disciplines are combined. Notably, in mathematics instruction, the focus is on the development of concepts whereas when it is integrated with science, engineering, and technology, mathematics is used to support the development of the concepts within these respective disciplines. Hence, this requires a close examination of the nature of integration with a focus on the connected concepts within disciplines.

Integrated STEM and higher order thinking

Integrated STEM education ties together practices of scientific inquiry and mathematical analysis, which aligns with the interdisciplinary format of STEM standards in science and mathematics (Bybee, 2013 ; Sanders, 2012 ). Today’s K-12 educational systems have made efforts to reform STEM discipline standards making provision for students to think critically and experience meaningful integration of the STEM disciplines within the context of authentic, real-world challenges (Council of Chief State School Officers & National Governors Association Center for Best Practices, 2010 ; NGSS Lead States, 2013 ). The content standards in mathematics (e.g., Common Core) and science (e.g., NGSS) now emphasize developing students’ abilities to think deeply and understand relationships about their respective disciplinary concepts and practices (Achieve, 2010 ; NAE and NRC, 2014 ). Students should engage in disciplinary tasks that require interpretation and construction of meaning to arrive at answers or solutions that are not obvious at the onset of assigned tasks (Tekkumru-Kisa et al., 2020 ). These tasks can be in the form of an instructional unit of interdisciplinary work or a classroom activity that is assigned to students by the teacher and directly requires students to intellectually engage in science and/or mathematics thinking (Tekkumru-Kisa et al., 2015 ). For instance, a task that requires students to memorize or reproduce procedures is considered to be a low-level task, whereas a task that requires students to construct arguments or use evidence-based reasoning to support ideas is a high-level task. Students should spend a significant amount of time working on tasks that are considered high-level because such tasks improve students’ abilities to learn content at a deeper level and to think more critically (Tekkumru-Kisa et al., 2015 ).

Categorizing activities that require students to complete high-level or low-level tasks is not new. This method has been used in education since the 1980s. For example, Doyle’s ( 1983 ) work on the mental processes students engage in to complete academic tasks is often credited with the start of classifying tasks as low-level or high-level; this type of categorization has been used in both mathematics and science education communities. Further, Smith and Stein’s ( 1998 ) task-analysis guide for the evaluation of Characteristics of Mathematical Instructional Tasks has been used by NCTM and the Stanford Center for Professional Development in mathematics professional development workshops that focus on the quality of mathematical tasks. This guide can be found in mathematics methods course textbooks. Notably, mathematics education journals report its use in supporting the development of mathematical reasoning and problem-solving skills (Boston & Smith, 2011 ; Dempsey & O’Shea, 2020 ). The lowest level demand (“memorization”) involves reproducing previously learned rules or formulas, for example, requiring students to recall the formula for two- and three-dimensional shapes. The highest-level demand (“doing mathematics”) requires non-algorithmic thinking and an understanding of mathematical relationships between concepts, for example, presenting students with opportunities to apply more than one solution strategy and producing explanations and justifications for these. The intention of this guide, though, was not on observing instruction, but on classifying mathematical tasks as “good” (Smith & Stein, 1998 , p. 344). Building on Smith and Stein’s ( 1998 ) work, Matsumura et al. ( 2006 ) developed a series of protocols collectively referred to as the Instructional Quality Assessment (IQA), which were designed to assess observed classroom instruction and the quality of work in mathematics (and comprehension) that teachers assign to students. One of these protocols is the Academic Rigor - Implementation of the Task rubric (Rubric 2), which rates the quality and cognitive demand of tasks that students are engaged in during instruction using five levels (Levels 0–4). The rubric mirrors the design of the original Smith and Stein ( 1998 ) task-analysis guide in Levels 1–4 but includes a new level, Level 0, in which the task does not require mathematical knowledge on the part of students, only the teacher.

There are parallels among levels in these instruments. For example, the memorization level is common in the works of Smith and Stein ( 1998 ) and Matsumura et al. ( 2006 ). It is also present in Tekkumru-Kisa et al.’s ( 2015 ) Task Analysis Guide in Science, a similar guide used in the science education community. In the Tekkumru-Kisa et al.’s ( 2015 ) guide, the lowest level is memorization, and the highest level is doing science . These guides and rubrics are useful to consider because the aims of integrated STEM education require students to engage in higher-order thinking as they consider real-world, open-ended problems (Kelley & Knowles, 2016 ). The openness of an authentic, real-world problem allows students to use complex, higher-order thinking in which they analyze data and determine patterns within information to make informed decisions while simultaneously drawing upon knowledge from the disciplines of mathematics and/or science knowledge.

As students are presented with opportunities to draw on knowledge from multiple STEM disciplines, the types of activities they are expected to engage in will look different across classrooms that incorporate integrated STEM. Because K-12 content standards in mathematics and science require students to reason and engage in critical thinking both mathematically and scientifically (Council of Chief State School Officers & National Governors Association Center for Best Practices, 2010 ; NGSS Lead States, 2013 ), one might expect that most activities would be at a cognitively high-level. Classroom activities within integrated STEM lessons that include mathematics or science should inherently allow students to: (a) construct meaning of the content, (b) make sense of the underlying disciplinary idea, and (c) engage in complex thinking (Moore, Stohlmann, et al., 2014 ). The alignment between the levels of cognitive demand in the science and mathematics rubrics noted above makes it possible to analyze classroom activities across different settings. This is critical when the objective is to understand the degree to which mathematics or science cognitive demand tasks are present within integrated STEM classroom activities. Moreover, this is fundamentally important considering that a key feature of integrated STEM education is crossing disciplinary boundaries by presenting tasks in a real-world context requiring students to think critically and effectively use knowledge from several disciplines (NAE and NRC, 2014 ).

Conceptual Framework

Currently, scholars use no standard definition for the term integrated STEM education , which has been reported in numerous editorial comments and reviews of the literature (e.g., English, 2016 ; Li et al., 2020 ; Martín-Páez et al., 2019 ; Moore et al., 2020 ). For example, Martín-Páez et al. ( 2019 ) in their review of the literature shared STEM education as “a teaching approach that integrates content and skills specific to science, technology, engineering, and mathematics” (p. 815). Li et al. ( 2020 ) also suggested an understanding of STEM education that reaches beyond the simple integration of the disciplines’ content but rather one that is seen as “a broad and inclusive perspective to include education in the individual disciplines of STEM, i.e., science education, technology education, engineering education, and mathematics education” (p. 2). This interdisciplinary perspective was also reflected in Vasquez et al.’s ( 2013 ) definition of integrated STEM education as an approach that spans the four disciplines of science, technology, engineering, and mathematics and integrates them to provide relevant and rigorous learning experiences for a diverse range of learners. Additionally, Johnson et al. ( 2020 ) offer another definition that suggests using disciplinary knowledge and practices from engineering and technology to teach and learn specific science and/or mathematics knowledge. Likewise, Moore, Stohlmann, et al.’s ( 2014 ) definition proposes the use of engineering design to develop an understanding of technologies that require the application of mathematics and/or science content. Notably, a commonality across these different definitions of integrated STEM education is the combination of disciplines that is far reaching and promotes the learning of content and skills from different disciplines.

For the purpose of this study, we broadly define and conceptualize integrated STEM education as an approach that focuses on the interconnectedness between the content and skills of the STEM disciplines; science, technology, engineering, and mathematics. In this study, we focus on the inclusion of mathematics to support student learning and how it is positioned with respect to cognitive demand within integrated STEM activities. This aligns best with the definition provided by Kelley and Knowles ( 2016 ) who suggested that STEM education is, “The approach to teaching the STEM content of two or more STEM domains, bound by STEM practices within an authentic context for the purpose of connecting these subjects to enhance student learning” (p. 3). The purpose of intentionally combining content from these disciplines is to ultimately develop concepts and skills from the disciplines and improve student learning outcomes in a meaningful, interconnected way as opposed to a compartmentalized, siloed way that has historically been practiced in K-12 education.

Methods and findings

The aim of this study was to better understand the use of mathematics within integrated STEM contexts. To reach this aim, we first explored how integrated STEM lessons that include mathematics perform on an integrated STEM observational protocol compared to lessons without mathematics. We then investigated the levels of mathematical cognitive demand present in physical, earth, and life science integrated STEM units. The following sections first present the research design and context for the study. This is followed by the quantitative and qualitative phases along with the respective findings for each phase.

Research design

This study used an explanatory sequential mixed methods research design in which the quantitative data were collected and analyzed prior to conducting qualitative analysis (Teddlie & Tashakkori, 2009 ). This design allowed us to first investigate how integrated STEM lessons with and without mathematics performed on an integrated STEM observational protocol. For the second phase of the research, we qualitatively analyzed video-recorded classroom observations to identify the level of mathematical cognitive demand represented in a sample of physical, earth, and life science integrated STEM units. The qualitative analysis provided more detail about the presence and cognitive demand of mathematics within different science domains. The quantitative and qualitative results were later synthesized to understand our findings at a more detailed level.

The raw data used in this study (in the form of video-recorded integrated STEM observations) were collected as part of a previously funded 5-year project. During the first 3 years of the project, K-12 science teachers participated in professional development related to integrated STEM education, co-created integrated STEM curriculum units, and implemented these units into their classrooms. In the fourth and fifth years of the project, teachers participated in a similar professional development but field-tested several of the previously created curriculum units instead of writing new ones. The professional development utilized a design-based vision of integrated STEM based on two frameworks that centralized learning activities within an engineering design challenge (Moore, Glancy, et al., 2014 , Moore, Stohlmann, et al., 2014 ). In the professional development, an emphasis was placed on the inclusion of data analysis as one method of engaging in evidence-based reasoning. Participating teachers were video recorded during the implementation of their integrated STEM units, with each video corresponding to one class period (~ 45 minutes). These video-recorded classroom observations represent a variety of classroom settings, including different grade levels, teachers, student demographics, science content, and engineering design challenges. Specifically, the data used in this study include 2030 video-recorded observations from 106 unique teachers’ classrooms from five school districts that include urban, inner-ring suburban, and outer-ring suburban K-12 settings in the Midwestern United States. Most of the observations focus on grades 4–8, although early elementary (K-3) and high school are represented to a lesser extent. The science content covered in the 48 unique curriculum units from the first 3 years of the project spans several topics in the physical (e.g., force and motion), life (e.g., ecosystems), and earth sciences (e.g., plate tectonics); a total of 13 of those curriculum units were field-tested in the fourth and fifth years. Our data set includes 999 physical science, 434 earth science, and 597 life science observations. These video observations also represent 885 elementary (K-5), 1071 middle school (6–8), and 74 high school (9–12) classrooms.

Quantitative phase

Data collection.

In the quantitative phase, we compared integrated STEM video observations that included mathematics to those that did not. To do this, we used the STEM Observation Protocol (STEM-OP) (Dare et al., 2021 ) to score the 2030 classroom observation videos described above. This observational protocol (Dare et al., 2021 ) measures the degree to which integrated STEM takes place in K-12 science and engineering classrooms; the instrument does not attend to pedagogical quality as other instruments that attend to this are already available. All items on the instrument have demonstrated acceptable Krippendorff’s alpha levels (α > .6) for interrater reliability with the exception of Item 5 (α = .58), which approached our selected threshold. Further, we have also established the structure and reliability of the instrument through principal component analysis (PCA) (Roehrig et al.,  2023 ). The PCA work revealed two core dimensions of integrated STEM education when using our instrument: 1) real-world problem solving and 2) the nature of integrated STEM. The STEM-OP includes 10 items with four descriptive levels for each item (scored 0–3): 1) relating content to students’ lives, 2) contextualizing student learning, 3) developing multiple solutions, 4) cognitive engagement in STEM, 5) integrating STEM content, 6) student agency, 7) student collaboration, 8) evidence-based reasoning, 9) technology practices in STEM, and 10) STEM career awareness. After completing rigorous training with the STEM-OP and establishing interrater reliability, our coding team scored all of the video recordings made available from the previously described project. During this process, the coding team noted whether a given observation included science, technology, engineering, and/or mathematics content. We then used these indicators to subdivide the data into two categories: observations with mathematics ( n  = 637) and observations without mathematics ( n  = 1393).

Data analysis

To determine differences in STEM-OP scores between observations that included or did not include mathematics, we used the Mann-Whitney-Wilcoxon test (Mann & Whitney, 1947 ; Wilcoxon, 1945 ). This nonparametric test is used to compare the outcome between two independent groups from the same or identical sample (Hahs-Vaughn & Lomax, 2020 ). This test does not require that the difference between the samples is normally distributed or that the variances of the two populations are equal (Hahs-Vaughn & Lomax, 2020 ). An additional advantage of the Mann-Whitney-Wilcoxon test is that the two samples can have an unequal number of observations, which was true for our data. This nonparametric test helped answer the first research question: How do integrated STEM lessons that include mathematics perform on an integrated STEM observational protocol compared to lessons without mathematics?

Quantitative findings

Findings from the Mann-Whitney-Wilcoxon tests reveal that there are statistically significant differences in mean rank order for most of the STEM-OP’s 10 items when comparing lessons that included mathematics ( n  = 637) to those that did not ( n  = 1393) (Table  1 ). These results suggest that the presence of mathematics in an observed integrated STEM lesson leads to higher scores on Items 3–9. There is also a negative effect for Items 1 and 10 and no statistically significant effect for Item 2.

The positive effect of the presence of mathematics for Items 3–9 suggests multiple ways in which mathematics may improve integrated STEM instruction. For instance, Item 3 measures to what extent students are provided with opportunities to develop multiple design solutions to an engineering challenge. These design challenges oftentimes require students to use, for example, geometrical concepts in designing their multiple solutions, the incorporation of these mathematical concepts in such engineering aspects of the lessons appeared to have a positive effect on integrated STEM instruction.

Item 4 on the STEM-OP evaluates the level of cognitive engagement in STEM disciplines within the lessons. Mathematics inclusion in these integrated lessons suggests that students were engaged in higher levels of cognitive thinking, for example, in addition to doing calculations related to budgets and cost of production, they used the results of these calculations to make decisions about their design challenges. The positive effect of the presence of mathematics in integrated STEM lessons was also observed in our findings for Item 5. This item measures how teachers integrate content from multiple STEM disciplines, inclusive of mathematics. It should be noted that our comparison used mathematics to sort our scores; however, even within non-mathematics observations, multiple STEM disciplines could still have been integrated (e.g., an observation with science and engineering). The importance in this finding is that including mathematics appears to make a significant difference in the integration of content. Notably, Items 4 and 5, which both examine how STEM content is presented in the observed lessons, displayed the highest mean differences with 0.39 and 0.48, respectively.

Similarly, we observe differences between the two groups for Item 6, which assesses the degree to which students have agency over their learning. It would appear that when mathematics is included that there is more student agency. Somewhat unexpected, we see differences for Item 7, which focuses on student collaboration. Including mathematics appears to result in more complex collaborative activities compared to integrated STEM activities that do not include mathematics. Item 8, which measures evidence-based reasoning, also scored higher when mathematics is present, which may reflect that students often use mathematical evidence in their scientific claims or design decisions. The presence of mathematics within integrated STEM lessons is also supportive in creating opportunities for students to engage in the appropriate use of technology in calculating, collecting, and/or analyzing data as they work on creating possible solutions for the design challenge; these technology-based opportunities are captured by Item 9. Thus, the results from these remaining items suggest that the inclusion of mathematics is correlated to higher scores in the development of multiple solutions (Item 3), cognitive engagement in STEM (Item 4), integrating STEM content (Item 5), student agency (Item 6), student collaboration (Item 7), evidence-based reasoning (Item 8), and technology practices in STEM (Item 9). For Item 1 (relating content to students’ lives) and Item 10 (STEM career awareness), not included in this list, it is curious to note the negative effect of mathematics on both items. This may relate to the fact that these lessons were implemented by science teachers in which their knowledge of relating mathematical content to students’ lives and careers may have been limited.

In conclusion, these quantitative results provide evidence that the inclusion of mathematical content within integrated STEM lessons is associated with overall higher scores on Items 3–9 of the STEM-OP. Considering that the STEM-OP was intentionally designed to measure the degree to which integrated STEM occurs, this is evidence that the inclusion of mathematics within integrated STEM lessons is notable.

Qualitative phase

Data collection and materials.

Since the results of our initial quantitative phase revealed that observed lessons that included mathematics scored higher on the STEM-OP for most items, we sought to conduct a further investigation of the mathematical tasks occurring in these lessons. This was achieved by exploring the levels of mathematical cognitive demand required from the mathematical tasks within selected physical, earth, and life science integrated STEM units. Thus, we address our second research question: What levels of mathematical cognitive demand are represented in physical, earth, and life science integrated STEM units?

The following sections describe the qualitative methods used to investigate the levels of cognitive demand for the mathematical tasks in selected curriculum units. We used a multiple case study design that focused on developing an in-depth understanding of a case or bounded system focused on understanding how events occur and which ones may influence particular outcomes (Savin-Baden & Howell Major, 2012 ). In order to define the cases for our study, we took several steps. We first considered curriculum units wherein 50% or more of the daily observed lessons included mathematics; this left us with 20 curriculum units, which cut across all three science disciplines (11 physical science, seven earth science, and two life science) implemented by multiple teachers due to the team-like nature of the original project. We then considered which grade level to examine for the study and which curriculum unit within each science domain to be selected. To make a decision on the grade level, we closely reviewed the Common Core State Mathematics standards which were contained in each curricular document that the research team had access to. The use of this secondary data source assisted us in narrowing in on the elementary level as we observed that these units covered a wider variety of mathematics domains including measurement and data, number and operations, and geometry. As a result, we selected units implemented in the elementary grades, of which there were nine. To decide which specific curriculum unit would best serve as the case for each science discipline, we again referred to the secondary data, this time simultaneously reviewing the stated mathematics standards and topics contained in each curricular document.

Based on this process, we selected a curriculum unit each for physical science, earth science, and life science. There were 53 video observations in total, which included 19 for the physical science unit, 17 for the earth science unit, and 17 for the life science unit. Both the physical science and earth science units were implemented by four teachers, while the life science unit was implemented by five teachers. All curriculum units were implemented at either the fourth or fifth-grade level and were centered on three different engineering design challenges. In addition to the mathematical content/topics, the written curriculum units also contained science content/topics, clear explanations of the intended engineering design challenge that students were expected to be engaged in, as well as technology and engineering connections. Table  2 presents the science discipline, a brief description of the engineering design challenge, and science and mathematics topics for each curriculum unit.

Overall, a total of 53 classroom-recorded observations that included mathematics were considered and analyzed for the qualitative component of our study. In the first step of our analysis, we reviewed all 53 video observations, noting specific segments in each video for which mathematical tasks were either directly identified by the teacher and/or performed by the students. Through this process, we identified 153 unique segments as multiple segments were possible in the individual video observation. These segments were transcribed in preparation for coding of the mathematical tasks present.

Second, we used the Academic Rigor - Implementation of the Task rubric (Rubric 2) (Matsumura et al., 2006 ) to code the video segments and identify the level of mathematics cognitive demand required of students in the observed integrated STEM activities. This particular rubric was selected over other rubrics we explored because of its focus on specifically categorizing the cognitive demand levels of student engagement for the mathematical tasks examined. The rubric, which includes five levels, has been found to be valid and reliable with overall cognitive demand level agreement at 81.8% (Matsumura et al., 2006 ). The Cronbach’s alpha calculated for the consistency of the rating results yielded an alpha of 0.92 (Matsumura et al., 2006 ). These reliability measures have been reported as good overall. The rubric’s interrater agreement is reported at a moderate value of 76.3% across pairs of raters (Matsumura et al., 2006 ). The five levels of the rubric begin with lower cognitive demand and increase in engagement at each level. As previously noted, Levels 1–4 in particular are derived from Smith and Stein’s ( 1998 ) Characteristics of Mathematical Tasks at Four Levels of Cognitive Demand, such that these levels can be broken into lower-level (Levels 1 and 2) and higher-level (Levels 3 and 4) demand tasks. The rubric levels are presented visually in Fig.  1 with descriptions of each level in the section that follows.

The Characteristics of the Implementation of Mathematical Tasks at Five Levels of Cognitive Demand. Figure 1 Adapted from The Characteristics of the Implementation of Mathematical Tasks at Five Levels of Cognitive Demand (Adapted from Academic Rigor - Implementation of the Task rubric - Rubric 2 (Matsumura et al., 2006 )

The first level, Level 0, indicates that although a mathematical task might be explained to students by the teacher, this did not require cognitive engagement by the students. This may be represented by instances in which a teacher provides explanations, directions, instructions, and/or referred to mathematical objectives. The second level, Level 1, is marked by students engaging in mathematical tasks that focus on memorizing or reproducing facts, rules, formulae, or definitions without making connections to or meaning of the concepts at hand. Level 2 activities require that students engage in using a procedure that was either specifically called for or its use was evident based on prior instruction, experience, or placement of the task. In this, students follow a prescriptive method with little room to make connections to concepts or meaning underlying procedures used. For Level 2 mathematical tasks, students merely used procedure(s) that are specifically called for, requiring no effort by the students to use their initiative or make decisions.

Levels 3 and 4 are considered higher cognitive level tasks. Level 3 tasks are marked by students engaging in complex thinking or in creating meaning for mathematical concepts, procedures, and/or relationships. These tasks require higher-order or complex thinking, but without obvious evidence of students’ reasoning and understanding. At this level, students engage in performing mathematical tasks or procedures with connections within mathematical concepts, however evidence of these connections is not explicit within the assigned tasks. At the highest level of cognitive engagement, Level 4, the mathematical tasks would engage students in exploring and understanding the nature of mathematical concepts, procedures, and/or relationships (i.e., they used complex and non-algorithmic thinking). At this uppermost level, students are expected to use procedures with connections among mathematical concepts as they work on the assigned mathematical tasks.

The first and second authors used this rubric to independently code each of the 153 identified segments and established interrater reliability using Cohen’s weighted kappa ( κ  = 0.80). This demonstrates substantial to approaching almost perfect agreement (Cohen, 1960 ). During this phase of coding, it was necessary to further subdivide some of the identified segments as additional mathematics concepts or skills were embedded within them. Coders resolved disagreements through discussion until they reached a consensus for each identified segment. Once the codes were agreed upon, we were able to count the frequency of codes (rubric Levels) for each of our three cases. This allowed us to understand the frequency distribution of the cognitive demand in the mathematical tasks.

The final step in our analysis was to look for patterns in the segments that were coded at each level. This allowed us to understand not just the levels of the tasks, but what specifically students were doing while engaging in those tasks during an integrated STEM curriculum unit. This was first done within each case and then compared across the three cases in a cross-case comparison.

Qualitative findings

In this section, we first present the three cases (physical, earth, and life science) individually. Within each case, examples of mathematical tasks and how they were coded according to the different levels of cognitive demand required from students are explained. This is then followed by a cross-case comparison, addressing patterns and similarities across the cases. The science units served as the three cases for the study - Case 1: physical science, Case 2: earth science, and Case 3: life science.

Case 1: Physical science

The physical science case consisted of four teachers who implemented the same curriculum unit. This curriculum unit was based on a design challenge in which students were asked to construct a container that does not require a power source to keep vaccines cool in warm climates. Therefore, students completed activities to determine which materials (e.g., metal, cotton fabric) were conductors or insulators, which would allow for heat energy to flow through quickly or slowly, respectively. Throughout this unit there were a total of 32 segments that included the presence of mathematics; this was the lowest frequency count of the three cases. An overview of the frequency of codes within this physical science unit is presented in Table  3 .

When examining the presence of mathematics within each of the cognitive demand levels for this case, we found that of the 32 instances of mathematics, seven of these were coded at Level 0. These represent instances in which the teachers either outlined the objectives of the lessons as related to mathematics or they gave instructions or explanations related to mathematics. As a result, students were not directly engaged in any mathematical activity. For example, in one instance the teacher only explained to students that in this unit they were doing some data analysis, but data analysis took place in a later lesson.

Collectively, 16 of the segments in this case were coded at lower levels of cognitive demand (i.e., Level 1 and Level 2). These two levels accounted for 64% of the instances of the non-Level 0 mathematics in this physical science unit. One instance of a task that was coded at Level 1 was when students were asked to state how they will determine the mean melting time for three materials: metal, wood, and plastic. These instances were coded at this lower level of cognitive demand as students were tasked with reproducing a formula/fact without performing any mathematical procedures or calculations. Another instance of a task which was coded at this memorization level within this unit was when teachers displayed a graph for the cases of a medical condition (Pertussis) to the students. At this point, teachers called on students to recall vocabulary terms associated with constructing graphs. For example, “What do you call the horizontal axis of the graph? What letter is attached to it?” A student responded, “x-axis.”

The number of mathematical tasks coded peaked at Level 2 (Table 3 ) such that students engaged in using a procedure, but the nature of the task did not allow them to make connections to the concepts or meaning underlying the procedure being used. These tasks ranged from the basic recall of mathematical facts to decision-making based on previously collected data. In this case, students were required to read digital thermometers, which is a step-by-step procedure that required limited cognitive demand for successful completion. In this instance, students were engaged in more than just memorizing or reproducing facts, rules, formulae, and definitions.

As students progressed along the unit, the level of cognitive demand for the mathematical tasks increased across all teachers. For instance, teachers provided students with opportunities to develop mathematical ideas related to graphs. Specifically, students analyzed and interpreted collected data to make the decisions needed to understand the science concepts. Particularly, one mathematical task required students to determine which of the materials (e.g., felt, bubble wrap, plastic wrap) they tested would be a good insulator based on the previously collected temperature-change data for these materials. This mathematical task engaged the students in making decisions about the best insulators needed for the specific engineering design challenge of creating a vaccine container and was coded at Level 3. Another instance that was coded at Level 3 was when students described trends and patterns in line graphs, they created that indicated temperature change over time when testing different vaccine containers. They subsequently interpreted these graphs and used the information to determine if their vaccine container met the criteria of the design challenge. Along with exploring the science concepts of heat conductors, students were required to use mathematical knowledge and skills simultaneously to assist in making an informed decision about the most suitable material to use. Hence, for both of these instances, students were engaged in complex thinking or in creating meaning for mathematical concepts, procedures, and/or relationships.

Within this physical science case, there were no instances in which any mathematical tasks assigned by the teachers were coded at Level 4, the highest level of cognitive demand. In other words, none of the tasks required students to be engaged in exploring and understanding the nature of mathematical concepts, procedures, and/or relationships.

Case 2: Earth science

The second case was earth science, in which there were also four teachers. The design challenge for this curriculum unit was to create a mining tool that could be used to extract specific renewable and nonrenewable resources from different exoplanets mining sites. For this unit, there were a total of 78 instances of mathematics recorded and coded, which was the highest occurrence of the three cases. These instances included activities such as calculating costs of materials, profits, and/or area of surfaces. Table  4 provides the distribution for the frequency of the cognitive demand codes for the implementation of mathematical tasks for the earth science case. Based on this distribution, it was noted that most of these tasks were found to be at Level 2, with students engaged in utilizing previously taught procedures. The codes noted for Levels 1 and 3 were equal in number. Similar to the physical science case, there were no mathematical tasks coded at Level 4.

Of the 78 segments of mathematics in the earth science unit, 37 of them were coded at Level 0, which represents just less than half the total segments. With respect to Levels 1 and 2, a total of 32 instances were coded at these levels, requiring lower cognitive demand thinking from students. The tasks within this unit were predominantly procedural in nature but were not directly connected to other mathematical concepts and hence could not be coded above Level 2. More than 78% of the occurrences of mathematics across Levels 1 through 4, in which students were engaged were at the lower levels (1 and 2) of cognitive demand. At Level 1 one task prevalent in the unit was related to calculating area. In particular, students needed the area formula to determine the area of two-dimensional figures. While all four teachers in this case presented mathematical tasks involving the concept of area, three specifically requested students to recall the area formulas. For example, one teacher stated, “We need to find the area,” and then elicited from students how this could be accomplished. In one instance, a student responded by stating “multiply.” Because this specific instance simply involved students reproducing a previously learned rule/fact rather than learning it, it was coded at Level 1. Students were also engaged in tasks related to using procedures previously taught. One example was when students were asked to convert improper fractions to mixed numbers by the teachers in this case. To complete this conversion, students used algorithmic steps and subsequently equated the whole numbers from the mixed numbers to the number of materials extracted from the mining site to the number of shipping container units filled.

Similarly, the instances within this earth science case that required students to calculate the total cost of mining the resources as well as extracting the resource from the mining sites were coded at Level 2. These calculations were categorized as such because coders considered that the addition and/or multiplication algorithms used to calculate total cost and finalize the budget would have been mathematics competencies/skills covered prior to these fourth and fifth-grade levels. Hence, they were considered below grade level as there was little ambiguity in these tasks, and the implementation of these tasks focused on students producing correct answers rather than developing mathematical understanding. However, in follow-up lessons, students calculated the profit in creating their proposed mining tool and cross referencing this with the area of environmental impact caused by using the tool. This cross-referencing activity enabled students to develop an understanding of maximizing profits, and they were also able to make connections between the purpose of the budget and its importance to the engineering design challenge. These calculations and connections required students to engage in complex thinking, and thus supported a deeper understanding of concepts and connected ideas instead of simply performing procedures. This requires some degree of cognitive effort and was accordingly coded at Level 3 as guided by the rubric. None of the mathematical tasks implemented within the earth science case required students’ engagement in exploring and understanding the nature of mathematical concepts or procedures nor considerable cognitive effort from students; no instances were coded at Level 4 within this unit.

Case 3: Life science

Five teachers implemented the life science curriculum unit. In this case, the curriculum unit called for students to design and construct a model greenhouse capable of maintaining an optimal temperature closest to 24 0 C (75.2 0  F) and maintaining a temperature between 18 °C and 35 °C (64.4 °F and 95 °F). As a result, students calculated the area of shapes, measured and recorded temperatures, and/or analyzed data on graphs. In total, there were 43 segments in which mathematics was observed. The distribution of the cognitive demand codes for this case is presented in Table  5 . Within this case, the highest number of instances coded were at Level 2, signifying that students were mostly engaged in using procedures that they previously encountered whether it was in prior grades or within the said unit. Level 4 was not present in any of the instances of mathematics.

We noticed that 19 of the 43 segments with mathematics were coded at Level 0, slightly less than 50% of all total codes. On examining Levels 1 through 4, we observed that for Levels 1 and 2, there were 19 coded segments combined, which represents over 79% of the instances of mathematics. At Level 1 students were tasked with recalling the formula for finding the area of a triangle as they were to consider different possible shapes for greenhouse windows. As the lessons continued, there were instances where in addition to students recalling the area formula, teachers followed up such tasks by requiring students to then use formulas for calculating the areas of both triangles and squares for the two types of panels of their greenhouse designs. Since these mathematical tasks drew on students’ abilities to perform algorithmic calculations with limited cognitive demands, the tasks were coded at Level 2. The inclusion of this measurement concept of area with respect to also determining the window size for the greenhouse was similar across all five teachers’ implementations of mathematical tasks.

To guide students along with the engineering design task for this curriculum unit, teachers-initiated class discussions about testing different materials that may be appropriate for covering the windows of students’ greenhouse models so that the internal temperature can be kept within the required optimal range of 64.4 0  F and 95 0  F. In one such classroom discourse, the teacher shared, “So most of the temperature of the material dropped around 70 0 somewhere around there they kind of settled around the room temperature...based on that information, what have you learned about the material that would be best for your greenhouse?” A student responded, “I think the felt or tinfoil would work because the felt only went up to 86 [degrees], and the tinfoil only went up to 72 [degrees].” Such instances were coded at Level 3 because this type of higher-order questioning required some degree of complex thinking from students as they made connections with the results of the temperature data they previously collected and one of the criteria (optimal temperature range) for the engineering design challenge. Students were also entrusted with factoring in the cost of constructing the greenhouse and analyzing line graphs in their decision-making. As a result of teachers providing opportunities for students to acquire a deeper understanding and connection of concepts, all these instances were coded at Level 3. Notably, yet again there were no instances in which mathematical tasks required students to engage in considerable cognitive thinking and hence the uppermost Level 4 code was not applicable in this case.

Cross case comparison

From the original 53 classroom observation videos identified as including mathematics content in some way, we found a total of 153 segments of mathematical tasks that spanned the first four levels of the cognitive demand rubric; there were no instances of Level 4 in any of our cases. However, the distribution of the codes in the levels revealed some similarities and variations across the cases.

Throughout the three cases, there were 63 instances coded at Level 0. At this initial level, students were not engaged in mathematical activity; instead, teachers across the units either provided directions, instructions, or lesson objectives related to concepts or procedures in mathematics. Of these 63 instances, the earth science case had the most occurrences of mathematics segments (37) coded at this lowest level, while physical science and life science were significantly less at seven and 19, respectively. This high occurrence of segments coded at Level 0 in the earth science unit was evident as to how instruction heavy these lessons were across all earth science teachers. In many instances throughout this unit, teachers explained to students what mathematical concepts were involved, for example, in outlining the design challenge teachers stated that it will be necessary to analyze the data or complete a material cost sheet.

At Level 1, students either recalled previously recorded temperature values as in the physical science case, or they were asked to reproduce previously learned area formulas as in the earth and life science cases. A comparison of these two cases indicated that the number of instances at Level 1 only slightly differed. The physical science unit had a total of five instances of mathematics observed at Level 1. Interestingly, across all three cases, this was the least represented cognitive engagement Level.

At the other lower-level cognitive demand phase, Level 2, the implementation of mathematical tasks for all three cases showed an increase in occurrences when compared to Level 1. This indicated that teachers assigned more mathematical tasks that required higher cognitive demand thinking from students. Specifically, across the three cases, there were more instances where teachers extended students’ knowledge beyond recalling the area formulas and required students to calculate areas of triangles and squares. Just as noted for Level 1, the earth science case recorded the most instances across the three cases for Level 2. Students’ activities at this level of cognitive demand included conversion between mixed numbers and improper fractions as well as calculating total costs. Whereas, for the physical science case, students read thermometers and for the life science case, they calculated areas of shapes. There were an equal number of implementations of mathematical tasks coded at Level 2 for the physical and life science units.

The implementation of mathematical tasks was equal in number at Level 3 for both physical science and earth science while life science recorded the least of the three cases. The mathematical tasks assigned to students in the life science unit that were coded at Level 3 drew upon students’ abilities to perform mathematical tasks involving procedures that were critical to decision-making for the engineering design challenge. Specifically, students were required to make decisions in relation to optimal temperatures, surface area, cost factor for the construction of the greenhouse, and analysis of data. Hence, students’ decisions encompassed a combination of mathematical and science concepts along with engineering skills in their attempts to adhere to the criteria and constraints of building their greenhouse models. Students also made decisions in the physical science unit; however, these decisions were primarily based on the selection of the best insulating materials contingent on the change between initial and final temperatures. The decision-making, mathematics-related tasks that students were engaged in for the earth science unit were related to maximizing profits as well as comparing budgets and environmental impact.

In general, among the three cases, earth science had the greatest number of mathematical tasks despite there being more teachers implementing the life science unit. The total number of instances for the physical science unit was ten less than that for the life science unit and just less than half that for earth science. With respect to the overall distribution of codes throughout the three cases, over 70% of the codes for each science unit were at the lowest levels of cognitive demand. There is a remarkable absence of implementation of mathematical tasks that sought to promote the highest level of cognitive thinking from students. The lack of mathematical tasks at Level 4 in particular, meant students were not presented with opportunities to aptly understand and explore the nature of mathematical concepts, processes, or relationships. Such learning opportunities would be synonymous with using non-algorithmic thinking and procedures as well as exploring and extending students’ thinking in relation to mathematical concepts and ideas within these science units.

This study examined the presence of mathematics in integrated STEM instruction as well as the levels of cognitive demand required by mathematical tasks which were assigned to students within integrated lessons for physical, earth, and life science units. The examination of these two areas was driven by the notable under-representation of mathematical content within integrated STEM education (English, 2016 ; Marginson et al., 2013 ) and, more specifically, the level of mathematical thinking required from students in integrated STEM curricula (Baldinger et al., 2020 ; English, 2016 ; Stohlmann, 2018 ). Moreover, we were drawn to the importance paid to a discipline-integrated approach in education (Li et al., 2020 ; Martín-Páez et al., 2019 ). It is clear from the literature that mathematical tasks ought to be intentionally included in integrated STEM lessons (Fitzallen, 2015 ; Maass et al., 2019 ; Shaughnessy, 2013 ). Moreover, attention must be given as to how the implementation of these mathematics tasks is combined with other disciplinary content to support mathematics concept development; this occurs when mathematics tasks are at higher levels of cognitive demand. These considerations in this study in relation to mathematics inclusion were fueled by the disparity that currently exists in comparison to the extent to which science is noticeably represented and emphasized in integrated STEM. As Ring et al. ( 2017 ) and Ring-Whalen et al. ( 2018 ) noted, teachers often position mathematics as “less than” in integrated STEM curricula, seeing it as a support for teaching science and engineering. As a result of this need, in this study, we explored both features.

Our initial quantitative analysis showed that the presence of mathematics in integrated STEM lessons resulted in statistically significant differences in mean rank order between integrated STEM lessons that included mathematics and without mathematics. From our findings, Items 4 and 5 on the STEM-OP were highlighted for two main reasons, their statistically significant differences in mean rank order as well as the direct presence and/or integration of multiple disciplines. Our finding that video-recorded observations that include mathematics scored higher on these items than those without mathematics reinforces the idea that including mathematical content within integrated STEM lessons correlates to overall increased cognitive engagement as well as the depth of content integration in a meaningful way. This can ultimately provide effective student learning opportunities. The results of the Mann-Whitney-Wilcoxon tests also showed that when teachers intentionally included mathematical content in integrated STEM lessons, there were significant statistical differences for other items among the two groups. In particular, we observed higher overall scores on Items 3–9, which indicates the vital role that mathematics has within integrated STEM education as measured by the STEM-OP (Dare et al., 2021 ).

In our second qualitative component, we observed that teachers in each science domain presented the mathematics and science content within an engineering design context that allowed students to develop an understanding of mathematical content (Moore, Stohlmann, et al., 2014 ) categorized at the different levels of cognitive demand. Even though mathematics was included in the integrated STEM lessons for this study, the mathematical tasks that students engaged in did not necessarily allow them to reach the highest level of cognitive demand, Level 4. We were particularly drawn to the high number of instances coded at Level 0 across the cases - a total of 63 out of 153 instances. The rubric as proposed by Matsumura et al. ( 2006 ) outlines this initial level as the absence of direct student engagement in mathematical activities. There were many instances when teachers either described the lesson/unit objectives or gave explanations that were mathematical in nature. This signifies that although there was intent on the part of teachers to integrate mathematical activity within the integrated STEM units, the extent to which these activities engaged students was not always evident (i.e., they were not always necessarily reflected in actual implementation). With respect to the next two levels, our findings revealed that even though the earth science case contained the greatest number of mathematical tasks compared to the other two cases, the majority of these tasks were at the lower levels of cognitive demand: Levels 1 and 2. As noted in the cross-case comparison, of the three cases, the physical science unit recorded the fewest instances coded at Level 1, such that students engaged in mathematics beyond memorizing or recalling mathematical facts, formulas, or rules. Furthermore, both life science and physical science units reported the same number of instances coded at Level 2, thus indicating a similarity among teachers in these two units in requiring students to engage in using procedures when performing mathematical tasks. The implementation of these integrated STEM lessons demonstrated that the science teachers within this study possess mathematics subject-matter knowledge based on the mathematical tasks that they assigned to their students within the units. The issue, however, stands as to how science teachers can be taught or encouraged to successfully incorporate their mathematics knowledge with their science knowledge within integrated STEM lessons to effectively create tasks that facilitate high cognitive demand from their students.

For the instances in the integrated STEM lessons that were coded at a high level of cognitive demand, which were all at Level 3, we observed that the scientific content covered in the physical, earth, and life science units facilitated the use of mathematical reasoning skills by connecting the mathematics content to the engineering problem. For example, some tasks required that students engage in optimizing profits tied to creating a budget; representing and interpreting statistical data is necessary to make decisions about designing prototypes or selecting the appropriate materials for prototypes. These assigned tasks necessitated the meaningful integration of mathematical concepts within science and/or engineering contexts to address the engineering design challenge. This suggests that the teachers in this study could integrate content across the STEM disciplines and that mathematics plays an integral role in this process, especially concerning the engineering design challenge. This notion was previously noted and supported by the quantitative findings of our study; the inclusion of mathematics in integrated STEM lessons positively affected how these lessons scored on the STEM-OP.

Despite this favorable effect of the presence of mathematics concepts within integrated STEM instruction, interestingly, our findings showed that of the 2030 STEM lessons observed, only 637 (31%) of them contained mathematical content. This low representation of mathematics within the integrated STEM lessons of this study as compared to the other disciplines of science, technology, and engineering, was no surprise as acknowledged by other researchers (e.g., English, 2016 ). Our closer examination of the mathematical tasks assigned within these integrated STEM lessons indicated that what is currently being included did not require the highest level of cognitive demand thinking, i.e. Level 4, from students. This mirrors the notion that when it comes to integrated STEM education, science teachers may inherently understand the importance of including mathematics, but do not always prioritize it in their curriculum design and implementation (Ring-Whalen et al., 2018 ; Roehrig et al., 2021 ). Our findings further suggest that when teachers do incorporate mathematics into their integrated STEM teaching, they may not consider the degree to which the mathematical tasks are cognitively demanding as their focus is more on creating opportunities for including mathematics to begin with (Ring-Whalen et al.,  2018 ). It would appear that the science teachers’ focus in this study was predominantly developing science and/or engineering concepts and practices, hence they incorporated mathematical skills/tasks which they felt were part of their students’ prior knowledge or would have been covered in previous grades. For example, there were instances when teachers drew on mathematical concepts such as finding the area of two-dimensional shapes or calculating means. These topics were not taught in the integrated STEM lessons that were observed for this study: instead, teachers merely asked students to recall or perform those tasks, thus assuming that these concepts were previously learned by students.

Our exploratory work here suggests that new mathematical concepts ought to be introduced within STEM instruction if the goal of integrated STEM education is to ultimately develop content knowledge and skills across all STEM disciplines, not just science and engineering. Consequently, our work confirms what other researchers have called for in terms of a stronger emphasis on mathematics in integrated STEM education (e.g., English, 2016 ; Marginson et al., 2013 ; Shaughnessy, 2013 ). This will require energy and effort in terms of curriculum design and implementation. For instance, including Level 4 tasks would likely require significant revisions to the curriculum, not to mention equipping the teachers (the curriculum designers) with in-depth knowledge of developing such higher-level cognitive demanding mathematical tasks.

Limitations

There are two main limitations to this study. First, the researchers did not conduct direct classroom observations of the teachers implementing the integrated STEM lessons. The primary data source was pre-recorded videos; therefore, it was challenging at times to capture all instances when students were directly engaged in mathematical tasks. This meant that we coded and analyzed the data based on the audio generated from teacher-student discussions and other classroom discourse and student activities captured by the camera located in the classroom. Second, most of the video observations from the original project in which the videos were collected focused on fourth through eighth-grade classrooms. We, however, analyzed a specific subset of these elementary-grade videos using the Academic Rigor - Implementation of the Task rubric (Rubric 2) (Matsumura et al., 2006 ). Analyzing the remaining fourth and fifth-grade video observations would allow for more comprehensive data collection and analysis processes. As a result, generalizability to other grade levels needs to be taken into consideration as the data collection and analysis were done for integrated STEM lessons taught by science teachers in grades at the elementary level. Applying the Implementation of the Task rubric at the kindergarten to second-grade level would allow for a comparison of the levels of cognitive demand for mathematical tasks between the elementary grades and the earlier grades. Additionally, future work should expand this study to middle and high school grade level curriculum units to understand how or if the level of mathematics cognitive demand changes given Baldinger et al.’s ( 2020 ) work indicating mathematics conceptual needs are not being met at the secondary level within discipline integrated settings.

Implications

In addition to science teachers’ approaches to implementing integrated STEM education, the findings from this study also have implications for teachers in general who engage or who are considering engaging students in integrated STEM activities. There are also implications for professional development initiatives which are geared towards promoting integrated STEM teaching among teachers. Addressing the level of cognitive demand for assigned mathematical tasks in integrated STEM lessons needs more attention. In this study, science teachers demonstrated that they assigned mathematical tasks at the higher cognitive demand, Level 3, however, unfortunately these were not implemented as frequently as lower-levels (Levels 1 and 2) mathematical tasks.

Providing additional support to science teachers while they design mathematical tasks alongside science and engineering content may assist them in creating more cognitively demanding mathematical tasks for their integrated STEM curricular units. This support afforded to science teachers should pay attention to how the constituent disciplines’ concepts are interconnected. One beneficial implication of such support can assist these teachers in engaging students in engineering design challenges requiring mathematics and science knowledge within integrated STEM lessons. This is critical as generally mathematics and science standards are geared towards allowing students to develop a deeper understanding of the respective content concepts. Therefore, we recommend professional development that focuses on guiding teachers to intentionally consider the inclusion of higher-order mathematical tasks within integrated STEM teaching. One means to accomplish this is familiarizing teachers with rubrics such as the one employed in this study. This could help to ensure the designed tasks effectively meet the four levels of cognitive demand. The ideal professional development ought to be co-taught by a science and mathematics education expert to ensure both discipline content needs are being addressed. Additionally, at the school site level, science teachers should collaborate with their mathematics colleagues to ensure that grade-level appropriate mathematical content is adequately addressed/presented in STEM lessons and activities. This will require support from the school administration with respect to designating simultaneous planning times for teachers.

Our study implies that it is also imperative that teacher educators ignite the importance to teachers, both pre-service and in-service, of targeting mathematical tasks within integrated STEM lessons that require higher levels of cognitive thinking from students. Awareness of this can be instructive and beneficial for teachers in the planning and implementation of quality integrated STEM lessons. There is no doubt that teachers can strive to include opportunities for high-level thinking through cognitively demanding tasks by way of questioning, providing opportunities for students to make connections, and supporting their answers with explanations (Boston, 2012 ).

This study resulted in two significant findings for including mathematics in integrated STEM units. Using the STEM-OP that measures the degree to which integrated STEM is present, we found that adding mathematics content to integrated lessons increases the degree of STEM integration as measured by our protocol. The second finding is that teachers in this study presented cognitively demanding mathematical tasks to students in integrated STEM lessons. However, these tasks mainly fell into the lower-level demand categories of Level 1 and Level 2, especially in the physical and life science units. These findings suggest that additional work in the area of inclusion of higher cognitively demanding mathematical tasks needs to be more specifically examined. Additionally, support and guidance for teachers with respect to effectively attending to extending students’ mathematical learning within integrated STEM lessons.

An overarching goal of STEM integration is to ultimately provide experiences that build skills and concepts as equitably as possible within and across all its disciplines, therefore, addressing how mathematics tasks are being included is necessary (NAE and NRC, 2014 ). Our findings reiterate the call for more research that is needed to establish a better understanding of both the presence and quality of mathematics tasks in integrated STEM education.

Availability of data and materials

Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Achieve. (2010). Taking the lead in science education: Forging next-generation science standards international science benchmarking report , (p. 66) https://www.achieve.org/files/InternationalScienceBenchmarkingReport.pdf .

Angier, N. (2010, October 4). STEM education has little to do with flowers . The New York Times. https://www.nytimes.com/2010/10/05/science/05angier.html .

Baldinger, E. E., Staats, S., Covington Clarkson, L. M., Gullickson, E. C., Norman, F., & Akoto, B. (2020). A review of conceptions of secondary mathematics in integrated STEM education: Returning voice to the silent M. In J. Anderson, & Y. Li (Eds.), Integrated approaches to STEM education , (pp. 67–90). Springer International Publishing. https://doi.org/10.1007/978-3-030-52229-2_5 .

Chapter   Google Scholar  

Becker, K., & Park, K. (2011). Effects of integrative approaches among science, technology, engineering, and mathematics (STEM) subjects on students’ learning: A preliminary meta-analysis. Journal of STEM Education , 12 (5/6), 23–37.

Google Scholar  

Boston, M. (2012). Assessing instructional quality in mathematics. The Elementary School Journal , 113 (1), 76–104. https://doi.org/10.1086/666387 .

Article   Google Scholar  

Boston, M. D., & Smith, M. S. (2011). A ‘task-centric approach’ to professional development: Enhancing and sustaining mathematics teachers’ ability to implement cognitively challenging mathematical tasks. ZDM , 43 (6–7), 965–977. https://doi.org/10.1007/s11858-011-0353-2 .

Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities . National Science Teachers Association.

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement , 20 (1), 37–46. https://doi.org/10.1177/001316446002000104 .

Council of Chief State School Officers, & National Governors Association Center for Best Practices. (2010). Common core state standards for mathematics . Council of Chief State School Officers.

Dare, E. A., Hiwatig, B., Keratithamkul, K., Ellis, J. A., Roehrig, G. H., Ring-Whalen, E. A., Rouleau, M. D., Faruqi, F., Rice, C., Titu, P., Li, F., Wieselmann, J. R., & Crotty, E. A. (2021). Improving integrated STEM education: The design and development of a K-12 STEM observation protocol (STEM-OP) (RTP). In Proceedings of the 2021 ASEE Annual Conference & Exposition . Virtual Conference. https://peer.asee.org/37307 .

Dare, E. A., Ring-Whalen, E. A., & Roehrig, G. H. (2019). Creating a continuum of STEM models: Exploring how K-12 science teachers conceptualize STEM education. International Journal of Science Education, 41 (12), 1701–1720. https://doi.org/10.1080/09500693.2019.1638531 .

Dempsey, M., & O’Shea, A. (2020). The role of task classification and design in curriculum making for preservice teachers of mathematics. Curriculum Journal , 31 (3), 436–453 Education Source.

Doyle, W. (1983). Academic work. Review of Educational Research , 53 (2), 159–199. https://doi.org/10.3102/00346543053002159 .

English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education , 3 (1), 1–8. https://doi.org/10.1186/s40594-016-0036-1 .

Fitzallen, N. (2015). STEM Education: What does mathematics have to offer? In M. Marshman, V. Geiger, & A. Bennison (Eds.), Mathematics Education in the Margins (Proceedings of the 38th Annual Conference of the Mathematics Education Research Group of Australasia) (pp. 237–244).

Hahs-Vaughn, D. L., & Lomax, R. G. (2020). An introduction to statistical concepts , (4th ed., ). Routledge, Taylor & Francis Group. https://doi.org/10.4324/9781315624358 .

Book   Google Scholar  

Hurley, M. M. (2001). Reviewing integrated science and mathematics: The search for evidence and definitions from new perspectives. School Science and Mathematics , 101 (5), 259–268. https://doi.org/10.1111/j.1949-8594.2001.tb18028.x .

International Technology and Engineering Educators Association. (2020). Standards for technological and engineering literacy: The role of technology and engineering in STEM education.

Johnson, C. C., Mohr-Schroeder, M. J., Moore, T. J., & English, L. D. (2020). STEM integration a synthesis of conceptual frameworks and definitions. In Handbook of research on STEM education , (1st ed., ). Routledge. https://doi.org/10.4324/9780429021381 .

Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education , 3 (11). https://doi.org/10.1186/s40594-016-0046-z .

Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education , 7 (1), 1–16. https://doi.org/10.1186/s40594-020-00207-6 .

Maass, K., Geiger, V., Ariza, M. R., & Goos, M. (2019). The role of mathematics in interdisciplinary STEM education. ZDM , 51 (6), 869–884. https://doi.org/10.1007/s11858-019-01100-5 .

Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics , 18 , 50–60.

Marginson, S., Tytler, R., Freeman, B., & Roberts, K. (2013). STEM: Country comparisons: International comparisons of science, technology, engineering, and mathematics (STEM) education . Australian Council of Learned Academics Final report.

Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vilchez-Gonzalez, J. M. (2019). What are we talking about when we talk about STEM education? A review of literature. Science Education , 103 (4), 799–822. https://doi.org/10.1002/sce.21522 .

Matsumura, L., Slater, S., Junker, B., & Peterson, M. (2006). Measuring reading comprehension and mathematics instruction in urban middle schools: A pilot study of the instructional quality assessment (CSE technical report 681) .

McLoughlin, E., Butler, D., Kaya, S., & Costello, E. (2020). STEM education in schools: What can we learn from the research? (1.0) . Dublin City University. https://doi.org/10.5281/ZENODO.3673728 .

Moore, T. J., Glancy, A. W., Tank, K. M., Kersten, J. A., Smith, K. A., & Stohlmann, M. S. (2014). A framework for quality K-12 engineering education: Research and development. Journal of Pre-College Engineering Education Research (J-PEER) , 4 (1). https://doi.org/10.7771/2157-9288.1069 .

Moore, T. J., Johnston, A. C., & Glancy, A. W. (2020). A synthesis of conceptual frameworks and definitions. In C. C. Johnson, M. J. Mohr-Schroeder, T. J. Moore, & E. L. D (Eds.), Handbook of research on STEM education , (pp. 3–16). Routledge. https://doi.org/10.4324/9780429021381 .

Moore, T. J., Stohlmann, M. S., Wang, H.-H., Tank, K. M., Glancy, A. W., & Roehrig, G. H. (2014). Implementation and Integration of Engineering in K-12 STEM Education. In Engineering in pre-college settings , (pp. 35–59). Purdue University Press.

National Academy of Engineering and National Research Council. (2014). Implications of the research for designing integrated STEM experiences. In STEM integration in K-12 education: Status, prospects, and an agenda for research . National Academies Press. https://doi.org/10.17226/18612 .

National Council of Supervisors of Mathematics (NCSM) and National Council of Teachers of Mathematics (NCTM). (2018). Building STEM education on a sound mathematical foundation. https://www.nctm.org/Standards-and-Positions/Position-Statements/Building-STEM-Education-on-a-Sound-Mathematical-Foundation/ .

National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas . The National Academies Press. https://doi.org/10.17226/13165 .

NGSS Lead States. (2013). Next generation science standards: For states, by states: Vol. 1 (the standards) and Vol. 2 (appendices) . National Academy Press.

Ring, E. A., Dare, E. A., Crotty, E. A., & Roehrig, G. H. (2017). The evolution of teacher conceptions of STEM education throughout an intensive professional development experience. Journal of Science Teacher Education, 28 (5), 444–467. https://doi.org/10.1080/1046560X.2017.1356671 .

Ring-Whalen, E., Dare, E., Roehrig, G., Titu, P., & Crotty, E. (2018). From conception to curricula: The role of science, technology, engineering, and mathematics in integrated STEM units. International Journal of Education in Mathematics, Science and Technology, 6 (4), 343–362. https://doi.org/10.18404/ijemst.440338 .

Roehrig, G. H., Dare, E. A., Ring-Whalen, E. A., & Wieselmann, J. R. (2021). Understanding coherence and integration in integrated STEM curriculum. International Journal of STEM Education, 8 (2). https://doi.org/10.1186/s40594-020-00259-8 .

Roehrig, G. H., Rouleau, M. D., Dare, E. A., & Ring-Whalen, E. A. (2023). Uncovering core dimensions of K-12 integrated STEM. Research in Integrated STEM Education, 1 , 1–25. https://doi.org/10.1163/27726673-00101004 .

Sanders, M. E. (2012). Integrative STEM education as “best practice.” . Griffith Institute for Educational Research, Queensland, Australia.

Savin-Baden, M., & Howell Major, C. (2012). Qualitative research: The essential guide to theory and practice . Routledge.

Shaughnessy, M. (2013). By way of introduction: Mathematics in a STEM context. Mathematics Teaching in the Middle school , 18 (6), 324. https://doi.org/10.5951/mathteacmiddscho.18.6.0324 .

Smith, M. S., & Stein, M. K. (1998). Reflections on practice: Selecting and creating mathematical tasks: From research to practice. Mathematics Teaching in the Middle School , 3 (5), 344–350.

Stohlmann, M. (2018). A vision for future work to focus on the “M” in integrated STEM. School Science and Mathematics , 118 (7), 310–319. https://doi.org/10.1111/ssm.12301 .

Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. In Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences . SAGE.

Tekkumru-Kisa, M., Stein, M. K., & Doyle, W. (2020). Theory and research on tasks revisited: Task as a context for students’ thinking in the era of ambitious reforms in mathematics and science. Educational Researcher , 49 (8), 606–617. https://doi.org/10.3102/0013189X20932480 .

Tekkumru-Kisa, M., Stein, M. K., & Schunn, C. (2015). A framework for analyzing cognitive demand and content-practices integration: Task analysis guide in science: Task analysis guide in science. Journal of Research in Science Teaching , 52 (5), 659–685. https://doi.org/10.1002/tea.21208 .

U.S. Bureau of Labor Statistics. (2021, September 8). Employment in STEM occupations . www.bls.gov/emp/tables/stem-employment.htm .

Vasquez, J. A., Cary, S., & Comer, M. (2013). Stem lesson essentials grades 3- 8 integrating science technology engineering and mathematics .

Wang, H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research , 1 (2), 1–13. https://doi.org/10.5703/1288284314636 .

Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin , 1 , 80–83.

Download references

Acknowledgements

This research was made possible by the National Science Foundation grants 1854801, 1812794, and 1813342. The findings, conclusions, and opinions herein represent the views of the authors and do not necessarily represent the view of personnel affiliated with the National Science Foundation.

Author information

Authors and affiliations.

State University of New York (SUNY), New Paltz, USA

Elizabeth N. Forde

Department of Teaching and Learning, Florida International University, Miami, USA

Latanya Robinson, Joshua A. Ellis & Emily A. Dare

You can also search for this author in PubMed   Google Scholar

Contributions

ENF and LR conceptualized and designed the study based on previous work. ENF, LR, and JAE participated in the data analysis. ENF and LR worked on the initial draft of the manuscript. JAE and EAD led revisions and the final format of the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Elizabeth N. Forde .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Forde, E.N., Robinson, L., Ellis, J.A. et al. Investigating the presence of mathematics and the levels of cognitively demanding mathematical tasks in integrated STEM units. Discip Interdscip Sci Educ Res 5 , 3 (2023). https://doi.org/10.1186/s43031-022-00070-1

Download citation

Received : 04 July 2022

Accepted : 28 December 2022

Published : 01 February 2023

DOI : https://doi.org/10.1186/s43031-022-00070-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Integrated STEM
• Mathematical tasks
• Cognitive demand

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 08 September 2024

Longitudinal analysis of teacher self-efficacy evolution during a STEAM professional development program: a qualitative case study

• Haozhe Jiang   ORCID: orcid.org/0000-0002-7870-0993 1 ,
• Ritesh Chugh   ORCID: orcid.org/0000-0003-0061-7206 2 ,
• Xuesong Zhai   ORCID: orcid.org/0000-0002-4179-7859 1 , 3   nAff7 ,
• Ke Wang 4 &
• Xiaoqin Wang 5 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  1162 ( 2024 ) Cite this article

Metrics details

Despite the widespread advocacy for the integration of arts and humanities (A&H) into science, technology, engineering, and mathematics (STEM) education on an international scale, teachers face numerous obstacles in practically integrating A&H into STEM teaching (IAT). To tackle the challenges, a comprehensive five-stage framework for teacher professional development programs focussed on IAT has been developed. Through the use of a qualitative case study approach, this study outlines the shifts in a participant teacher’s self-efficacy following their exposure to each stage of the framework. The data obtained from interviews and reflective analyses were analyzed using a seven-stage inductive method. The findings have substantiated the significant impact of a teacher professional development program based on the framework on teacher self-efficacy, evident in both individual performance and student outcomes observed over eighteen months. The evolution of teacher self-efficacy in IAT should be regarded as an open and multi-level system, characterized by interactions with teacher knowledge, skills and other entrenched beliefs. Building on our research findings, an enhanced model of teacher professional learning is proposed. The revised model illustrates that professional learning for STEAM teachers should be conceived as a continuous and sustainable process, characterized by the dynamic interaction among teaching performance, teacher knowledge, and teacher beliefs. The updated model further confirms the inseparable link between teacher learning and student learning within STEAM education. This study contributes to the existing body of literature on teacher self-efficacy, teacher professional learning models and the design of IAT teacher professional development programs.

Similar content being viewed by others

Primary and secondary school teachers’ perceptions of their social science training needs

Investigating how subject teachers transition to integrated STEM education: A hybrid qualitative study on primary and middle school teachers

Exploring the factors affecting elementary mathematics teachers’ innovative behavior: an integration of social cognitive theory

Introduction.

In the past decade, there has been a surge in the advancement and widespread adoption of Science, Technology, Engineering, and Mathematics (STEM) education on a global scale (Jiang et al. 2021 ; Jiang et al. 2022 ; Jiang et al. 2023 ; Jiang et al. 2024a , b ; Zhan et al. 2023 ; Zhan and Niu 2023 ; Zhong et al. 2022 ; Zhong et al. 2024 ). Concurrently, there has been a growing chorus of advocates urging the integration of Arts and Humanities (A&H) into STEM education (e.g., Alkhabra et al. 2023 ; Land 2020 ; Park and Cho 2022 ; Uştu et al. 2021 ; Vaziri and Bradburn 2021 ). STEM education is frequently characterized by its emphasis on logic and analysis; however, it may be perceived as deficient in emotional and intuitive elements (Ozkan and Umdu Topsakal 2021 ). Through the integration of Arts and Humanities (A&H), the resulting STEAM approach has the potential to become more holistic, incorporating both rationality and emotional intelligence (Ozkan and Umdu Topsakal 2021 ). Many studies have confirmed that A&H can help students increase interest and develop their understanding of the contents in STEM fields, and thus, A&H can attract potential underrepresented STEM learners such as female students and minorities (Land 2020 ; Park and Cho 2022 ; Perignat and Katz-Buonincontro 2019 ). Despite the increasing interest in STEAM, the approaches to integrating A&H, which represent fundamentally different disciplines, into STEM are theoretically and practically ambiguous (Jacques et al. 2020 ; Uştu et al. 2021 ). Moreover, studies have indicated that the implementation of STEAM poses significant challenges, with STEM educators encountering difficulties in integrating A&H into their teaching practices (e.g., Boice et al. 2021 ; Duong et al. 2024 ; Herro et al. 2019 ; Jacques et al. 2020 ; Park and Cho 2022 ; Perignat and Katz-Buonincontro 2019 ). Hence, there is a pressing need to provide STEAM teachers with effective professional training.

Motivated by this gap, this study proposes a novel five-stage framework tailored for teacher professional development programs specifically designed to facilitate the integration of A&H into STEM teaching (IAT). Following the establishment of this framework, a series of teacher professional development programs were implemented. To explain the framework, a qualitative case study is employed, focusing on examining a specific teacher professional development program’s impact on a pre-service teacher’s self-efficacy. The case narratives, with a particular focus on the pre-service teacher’s changes in teacher self-efficacy, are organized chronologically, delineating stages before and after each stage of the teacher professional development program. More specifically, meaningful vignettes of the pre-service teacher’s learning and teaching experiences during the teacher professional development program are offered to help understand the five-stage framework. This study contributes to understanding teacher self-efficacy, teacher professional learning model and the design of IAT teacher professional development programs.

Theoretical background

The conceptualization of steam education.

STEM education can be interpreted through various lenses (e.g., Jiang et al. 2021 ; English 2016 ). As Li et al. (2020) claimed, on the one hand, STEM education can be defined as individual STEM disciplinary-based education (i.e., science education, technology education, engineering education and mathematics education). On the other hand, STEM education can also be defined as interdisciplinary or cross-disciplinary education where individual STEM disciplines are integrated (Jiang et al. 2021 ; English 2016 ). In this study, we view it as individual disciplinary-based education separately in science, technology, engineering and mathematics (English 2016 ).

STEAM education emerged as a new pedagogy during the Americans for the Arts-National Policy Roundtable discussion in 2007 (Perignat and Katz-Buonincontro 2019 ). This pedagogy was born out of the necessity to enhance students’ engagement, foster creativity, stimulate innovation, improve problem-solving abilities, and cultivate employability skills such as teamwork, communication and adaptability (Perignat and Katz-Buonincontro 2019 ). In particular, within the framework of STEAM education, the ‘A’ should be viewed as a broad concept that represents arts and humanities (A&H) (Herro and Quigley 2016 ; de la Garza 2021 , Park and Cho 2022 ). This conceptualization emphasizes the need to include humanities subjects alongside arts (Herro and Quigley 2016 ; de la Garza 2021 ; Park and Cho 2022 ). Sanz-Camarero et al. ( 2023 ) listed some important fields of A&H, including physical arts, fine arts, manual arts, sociology, politics, philosophy, history, psychology and so on.

In general, STEM education does not necessarily entail the inclusion of all STEM disciplines collectively (Ozkan and Umdu Topsakal 2021 ), and this principle also applies to STEAM education (Gates 2017 ; Perignat and Katz-Buonincontro 2019 ; Quigley et al. 2017 ; Smith and Paré 2016 ). As an illustration, Smith and Paré ( 2016 ) described a STEAM activity in which pottery (representing A&H) and mathematics were integrated, while other STEAM elements such as science, technology and engineering were not included. In our study, STEAM education is conceptualized as an interdisciplinary approach that involves the integration of one or more components of A&H into one or more STEM school subjects within educational activities (Ozkan and Umdu Topsakal 2021 ; Vaziri and Bradburn 2021 ). Notably, interdisciplinary collaboration entails integrating one or more elements from arts and humanities (A&H) with one or more STEM school subjects, cohesively united by a shared theme while maintaining their distinct identities (Perignat and Katz-Buonincontro 2019 ).

In our teacher professional development programs, we help mathematics, technology, and science pre-service teachers integrate one component of A&H into their disciplinary-based teaching practices. For instance, we help mathematics teachers integrate history (a component of A&H) into mathematics teaching. In other words, in our study, integrating A&H into STEM teaching (IAT) can be defined as integrating one component of A&H into the teaching of one of the STEM school subjects. The components of A&H and the STEM school subject are brought together under a common theme, but each of them remains discrete. Engineering is not taught as an individual subject in the K-12 curriculum in mainland China. Therefore, A&H is not integrated into engineering teaching in our teacher professional development programs.

Self-efficacy and teacher self-efficacy

Self-efficacy was initially introduced by Bandura ( 1977 ) as a key concept within his social cognitive theory. Bandura ( 1997 ) defined self-efficacy as “people’s beliefs about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives” (p. 71). Based on Bandura’s ( 1977 ) theory, Tschannen-Moran et al. ( 1998 ) defined the concept of teacher self-efficacy Footnote 1 as “a teacher’s belief in her or his ability to organize and execute the courses of action required to successfully accomplish a specific teaching task in a particular context” (p. 233). Blonder et al. ( 2014 ) pointed out that this definition implicitly included teachers’ judgment of their ability to bring about desired outcomes in terms of students’ engagement and learning. Moreover, OECD ( 2018 ) defined teacher self-efficacy as “the beliefs that teachers have of their ability to enact certain teaching behavior that influences students’ educational outcomes, such as achievement, interest, and motivation” (p. 51). This definition explicitly included two dimensions: teachers’ judgment of the ability related to their teaching performance (i.e., enacting certain teaching behavior) and their influence on student outcomes.

It is argued that teacher self-efficacy should not be regarded as a general or overarching construct (Zee et al. 2017 ; Zee and Koomen 2016 ). Particularly, in the performance-driven context of China, teachers always connect their beliefs in their professional capabilities with the educational outcomes of their students (Liu et al. 2018 ). Therefore, we operationally conceptualize teacher self-efficacy as having two dimensions: self-efficacy in individual performance and student outcomes (see Table 1 ).

Most importantly, given its consistent association with actual teaching performance and student outcomes (Bray-Clark and Bates 2003 ; Kelley et al. 2020 ), teacher self-efficacy is widely regarded as a pivotal indicator of teacher success (Kelley et al. 2020 ). Moreover, the enhancement of teaching self-efficacy reflects the effectiveness of teacher professional development programs (Bray-Clark and Bates 2003 ; Kelley et al. 2020 ; Wong et al. 2022 ; Zhou et al. 2023 ). For instance, Zhou et al. ( 2023 ) claimed that in STEM teacher education, effective teacher professional development programs should bolster teachers’ self-efficacy “in teaching the content in the STEM discipline” (p. 2).

It has been documented that teachers frequently experience diminished confidence and comfort when teaching subject areas beyond their expertise (Kelley et al. 2020 ; Stohlmann et al. 2012 ). This diminished confidence extends to their self-efficacy in implementing interdisciplinary teaching approaches, such as integrated STEM teaching and IAT (Kelley et al. 2020 ). For instance, Geng et al. ( 2019 ) found that STEM teachers in Hong Kong exhibited low levels of self-efficacy, with only 5.53% of teachers rating their overall self-efficacy in implementing STEM education as higher than a score of 4 out of 5. Additionally, Hunter-Doniger and Sydow ( 2016 ) found that teachers may experience apprehension and lack confidence when incorporating A&H elements into the classroom context, particularly within the framework of IAT. Considering the critical importance of teacher self-efficacy in STEM and STEAM education (Kelley et al. 2020 ; Zakariya, 2020 ; Zhou et al. 2023 ), it is necessary to explore effective measures, frameworks and teacher professional development programs to help teachers improve their self-efficacy regarding interdisciplinary teaching (e.g., IAT).

Teacher professional learning models

The relationship between teachers’ professional learning and students’ outcomes (such as achievements, skills and attitudes) has been a subject of extensive discussion and research for many years (Clarke and Hollingsworth 2002 ). For instance, Clarke and Hollingsworth ( 2002 ) proposed and validated the Interconnected Model of Professional Growth, which illustrates that teacher professional development is influenced by the interaction among four interconnected domains: the personal domain (teacher knowledge, beliefs and attitudes), the domain of practice (professional experimentation), the domain of consequence (salient outcomes), and the external domain (sources of information, stimulus or support). Sancar et al. ( 2021 ) emphasized that teachers’ professional learning or development never occurs independently. In practice, this process is inherently intertwined with many variables, including student outcomes, in various ways (Sancar et al. 2021 ). However, many current teacher professional development programs exclude real in-class teaching and fail to establish a comprehensive link between teachers’ professional learning and student outcomes (Cai et al. 2020 ; Sancar et al. 2021 ). Sancar et al. ( 2021 ) claimed that exploring the complex relationships between teachers’ professional learning and student outcomes should be grounded in monitoring and evaluating real in-class teaching, rather than relying on teachers’ self-assessment. It is essential to understand these relationships from a holistic perspective within the context of real classroom teaching (Sancar et al. 2021 ). However, as Sancar et al. ( 2021 ) pointed out, such efforts in teacher education are often considered inadequate. Furthermore, in the field of STEAM education, such efforts are further exacerbated.

Cai et al. ( 2020 ) proposed a teacher professional learning model where student outcomes are emphasized. This model was developed based on Cai ( 2017 ), Philipp ( 2007 ) and Thompson ( 1992 ). It has also been used and justified in a series of teacher professional development programs (e.g., Calabrese et al. 2024 ; Hwang et al. 2024 ; Marco and Palatnik 2024 ; Örnek and Soylu 2021 ). The model posits that teachers typically increase their knowledge and modify their beliefs through professional teacher learning, subsequently improving their classroom instruction, enhancing teaching performance, and ultimately fostering improved student learning outcomes (Cai et al. 2020 ). Notably, this model can be updated in several aspects. Firstly, prior studies have exhibited the interplay between teacher knowledge and beliefs (e.g., Basckin et al. 2021 ; Taimalu and Luik 2019 ). This indicates that the increase in teacher knowledge and the change in teacher belief may not be parallel. The two processes can be intertwined. Secondly, the Interconnected Model of Professional Growth highlights that the personal domain and the domain of practice are interconnected (Clarke and Hollingsworth 2002 ). Liu et al. ( 2022 ) also confirmed that improvements in classroom instruction may, in turn, influence teacher beliefs. This necessitates a reconsideration of the relationships between classroom instruction, teacher knowledge and teacher beliefs in Cai et al.’s ( 2020 ) model. Thirdly, the Interconnected Model of Professional Growth also exhibits the connections between the domain of consequence and the personal domain (Clarke and Hollingsworth 2002 ). Hence, the improvement of learning outcomes may signify the end of teacher learning. For instance, students’ learning feedback may be a vital source of teacher self-efficacy (Bandura 1977 ). Therefore, the improvement of student outcomes may, in turn, affect teacher beliefs. The aforementioned arguments highlight the need for an updated model that integrates Cai et al.’s ( 2020 ) teacher professional learning model with Clarke and Hollingsworth’s ( 2002 ) Interconnected Model of Professional Growth. This integration may provide a holistic view of the teacher’s professional learning process, especially within the complex contexts of STEAM teacher education.

The framework for teacher professional development programs of integrating arts and humanities into STEM teaching

In this section, we present a framework for IAT teacher professional development programs, aiming to address the practical challenges associated with STEAM teaching implementation. Our framework incorporates the five features of effective teacher professional development programs outlined by Archibald et al. ( 2011 ), Cai et al. ( 2020 ), Darling-Hammond et al. ( 2017 ), Desimone and Garet ( 2015 ) and Roth et al. ( 2017 ). These features include: (a) alignment with shared goals (e.g., school, district, and national policies and practice), (b) emphasis on core content and modeling of teaching strategies for the content, (c) collaboration among teachers within a community, (d) adequate opportunities for active learning of new teaching strategies, and (e) embedded follow-up and continuous feedback. It is worth noting that two concepts, namely community of practice and lesson study, have been incorporated into our framework. Below, we delineate how these features are reflected in our framework.

(a) The Chinese government has issued a series of policies to facilitate STEAM education in K-12 schools (Jiang et al. 2021 ; Li and Chiang 2019 ; Lyu et al. 2024 ; Ro et al. 2022 ). The new curriculum standards released in 2022 mandate that all K-12 teachers implement interdisciplinary teaching, including STEAM education. Our framework for teacher professional development programs, which aims to help teachers integrate A&H into STEM teaching, closely aligns with these national policies and practices supporting STEAM education in K-12 schools.

(b) The core content of the framework is IAT. Specifically, as A&H is a broad concept, we divide it into several subcomponents, such as history, culture, and visual and performing arts (e.g., drama). We are implementing a series of teacher professional development programs to help mathematics, technology and science pre-service teachers integrate these subcomponents of A&H into their teaching Footnote 2 . Notably, pre-service teachers often lack teaching experience, making it challenging to master and implement new teaching strategies. Therefore, our framework provides five step-by-step stages designed to help them effectively model the teaching strategies of IAT.

(c) Our framework advocates for collaboration among teachers within a community of practice. Specifically, a community of practice is “a group of people who share an interest in a domain of human endeavor and engage in a process of collective learning that creates bonds between them” (Wenger et al. 2002 , p. 1). A teacher community of practice can be considered a group of teachers “sharing and critically observing their practices in growth-promoting ways” (Näykki et al. 2021 , p. 497). Long et al. ( 2021 ) claimed that in a teacher community of practice, members collaboratively share their teaching experiences and work together to address teaching problems. Our community of practice includes three types of members. (1) Mentors: These are professors and experts with rich experience in helping pre-service teachers practice IAT. (2) Pre-service teachers: Few have teaching experience before the teacher professional development programs. (3) In-service teachers: All in-service teachers are senior teachers with rich teaching experience. All the members work closely together to share and improve their IAT practice. Moreover, our community includes not only mentors and in-service teachers but also pre-service teachers. We encourage pre-service teachers to collaborate with experienced in-service teachers in various ways, such as developing IAT lesson plans, writing IAT case reports and so on. In-service teachers can provide cognitive and emotional support and share their practical knowledge and experience, which may significantly benefit the professional growth of pre-service teachers (Alwafi et al. 2020 ).

(d) Our framework offers pre-service teachers various opportunities to engage in lesson study, allowing them to actively design and implement IAT lessons. Based on the key points of effective lesson study outlined by Akiba et al. ( 2019 ), Ding et al. ( 2024 ), and Takahashi and McDougal ( 2016 ), our lesson study incorporates the following seven features. (1) Study of IAT materials: Pre-service teachers are required to study relevant IAT materials under the guidance of mentors. (2) Collaboration on lesson proposals: Pre-service teachers should collaborate with in-service teachers to develop comprehensive lesson proposals. (3) Observation and data collection: During the lesson, pre-service teachers are required to carefully observe and collect data on student learning and development. (4) Reflection and analysis: Pre-service teachers use the collected data to reflect on the lesson and their teaching effects. (5) Lesson revision and reteaching: If needed, pre-service teachers revise and reteach the lesson based on their reflections and data analysis. (6) Mentor and experienced in-service teacher involvement: Mentors and experienced in-service teachers, as knowledgeable others, are involved throughout the lesson study process. (7) Collaboration on reporting: Pre-service teachers collaborate with in-service teachers to draft reports and disseminate the results of the lesson study. Specifically, recognizing that pre-service teachers often lack teaching experience, we do not require them to complete all the steps of lesson study independently at once. Instead, we guide them through the lesson study process in a step-by-step manner, allowing them to gradually build their IAT skills and confidence. For instance, in Stage 1, pre-service teachers primarily focus on studying IAT materials. In Stage 2, they develop lesson proposals, observe and collect data, and draft reports. However, the implementation of IAT lessons is carried out by in-service teachers. This approach prevents pre-service teachers from experiencing failures due to their lack of teaching experience. In Stage 3, pre-service teachers implement, revise, and reteach IAT lessons, experiencing the lesson study process within a simulated environment. In Stage 4, pre-service teachers engage in lesson study in an actual classroom environment. However, their focus is limited to one micro-course during each lesson study session. It is not until the fifth stage that they experience a complete lesson study in an actual classroom environment.

(e) Our teacher professional development programs incorporate assessments specifically designed to evaluate pre-service teachers’ IAT practices. We use formative assessments to measure their understanding and application of IAT strategies. Pre-service teachers receive ongoing and timely feedback from peers, mentors, in-service teachers, and students, which helps them continuously refine their IAT practices throughout the program. Recognizing that pre-service teachers often have limited contact with real students and may not fully understand students’ learning needs, processes and outcomes, our framework requires them to actively collect and analyze student feedback. By doing so, they can make informed improvements to their instructional practice based on student feedback.

After undergoing three rounds of theoretical and practical testing and revision over the past five years, we have successfully finalized the optimization of the framework design (Zhou 2021 ). Throughout each cycle, we collected feedback from both participants and researchers on at least three occasions. Subsequently, we analyzed this feedback and iteratively refined the framework. For example, we enlisted the participation of in-service teachers to enhance the implementation of STEAM teaching, extended practice time through micro-teaching sessions, and introduced a stage of micro-course development within the framework to provide more opportunities for pre-service teachers to engage with real teaching situations. In this process, we continuously improved the coherence between each stage of the framework, ensuring that they mutually complement one another. The five-stage framework is described as follows.

Stage 1 Literature study

Pre-service teachers are provided with a series of reading materials from A&H. On a weekly basis, two pre-service teachers are assigned to present their readings and reflections to the entire group, followed by critical discussions thereafter. Mentors and all pre-service teachers discuss and explore strategies for translating the original A&H materials into viable instructional resources suitable for classroom use. Subsequently, pre-service teachers select topics of personal interest for further study under mentor guidance.

Stage 2 Case learning

Given that pre-service teachers have no teaching experience, collaborative efforts between in-service teachers and pre-service teachers are undertaken to design IAT lesson plans. Subsequently, the in-service teachers implement these plans. Throughout this process, pre-service teachers are afforded opportunities to engage in lesson plan implementation. Figure 1 illustrates the role of pre-service teachers in case learning. In the first step, pre-service teachers read about materials related to A&H, select suitable materials, and report their ideas on IAT lesson design to mentors, in-service teachers, and fellow pre-service teachers.

Note: A&H refers to arts and humanities.

In the second step, they liaise with the in-service teachers responsible for implementing the lesson plan, discussing the integration of A&H into teaching practices. Pre-service teachers then analyze student learning objectives aligned with curriculum standards, collaboratively designing the IAT lesson plan with in-service teachers. Subsequently, pre-service teachers present lesson plans for feedback from mentors and other in-service teachers.

In the third step, pre-service teachers observe the lesson plan’s implementation, gathering and analyzing feedback from students and in-service teachers using an inductive approach (Merriam 1998 ). Feedback includes opinions on the roles and values of A&H, perceptions of the teaching effect, and recommendations for lesson plan implementation and modification. The second and third steps may iterate multiple times to refine the IAT lesson plan. In the fourth step, pre-service teachers consolidate all data, including various versions of teaching instructions, classroom videos, feedback, and discussion notes, composing reflection notes. Finally, pre-service teachers collaborate with in-service teachers to compile the IAT case report and submit it for publication.

Stage 3 Micro-teaching

Figure 2 illustrates the role of pre-service teachers in micro-teaching. Before entering the micro-classrooms Footnote 3 , all the discussions and communications occur within the pre-service teacher group, excluding mentors and in-service teachers. After designing the IAT lesson plan, pre-service teachers take turns implementing 40-min lesson plans in a simulated micro-classroom setting. Within this simulated environment, one pre-service teacher acts as the teacher, while others, including mentors, in-service teachers, and other fellow pre-service teachers, assume the role of students Footnote 4 . Following the simulated teaching, the implementer reviews the video of their session and self-assesses their performance. Subsequently, the implementer receives feedback from other pre-service teachers, mentors, and in-service teachers. Based on this feedback, the implementer revisits steps 2 and 3, revising the lesson plan and conducting the simulated teaching again. This iterative process typically repeats at least three times until the mentors, in-service teachers, and other pre-service teachers are satisfied with the implementation of the revised lesson plan. Finally, pre-service teachers complete reflection notes and submit a summary of their reflections on the micro-teaching experience. Each pre-service teacher is required to choose at least three topics and undergo at least nine simulated teaching sessions.

Stage 4 Micro-course development

While pre-service teachers may not have the opportunity to execute the whole lesson plans in real classrooms, they can design and create five-minute micro-courses Footnote 5 before class, subsequently presenting these videos to actual students. The process of developing micro-courses closely mirrors that of developing IAT cases in the case learning stage (see Fig. 1 ). However, in Step 3, pre-service teachers assume dual roles, not only as observers of IAT lesson implementation but also as implementers of a five-minute IAT micro-course.

Stage 5 Classroom teaching

Pre-service teachers undertake the implementation of IAT lesson plans independently, a process resembling micro-teaching (see Fig. 2 ). However, pre-service teachers engage with real school students in partner schools Footnote 6 instead of simulated classrooms. Furthermore, they collect feedback not only from the mentors, in-service teachers, and fellow pre-service teachers but also from real students.

To provide our readers with a better understanding of the framework, we provide meaningful vignettes of a pre-service teacher’s learning and teaching experiences in one of the teacher professional development programs based on the framework. In addition, we choose teacher self-efficacy as an indicator to assess the framework’s effectiveness, detailing the pre-service teacher’s changes in teacher self-efficacy.

Research design

Research method.

Teacher self-efficacy can be measured both quantitatively and qualitatively (Bandura 1986 , 1997 ; Lee and Bobko 1994 ; Soprano and Yang 2013 ; Unfried et al. 2022 ). However, researchers and theorists in the area of teacher self-efficacy have called for more qualitative and longitudinal studies (Klassen et al. 2011 ). As some critiques stated, most studies were based on correlational and cross-sectional data obtained from self-report surveys, and qualitative studies of teacher efficacy were overwhelmingly neglected (Henson 2002 ; Klassen et al. 2011 ; Tschannen-Moran et al. 1998 ; Xenofontos and Andrews 2020 ). There is an urgent need for more longitudinal studies to shed light on the development of teacher efficacy (Klassen et al. 2011 ; Xenofontos and Andrews 2020 ).

This study utilized a longitudinal qualitative case study methodology to delve deeply into the context (Jiang et al. 2021 ; Corden and Millar 2007 ; Dicks et al. 2023 ; Henderson et al. 2012 ; Matusovich et al. 2010 ; Shirani and Henwood 2011 ), presenting details grounded in real-life situations and analyzing the inner relationships rather than generalize findings about the change of a large group of pre-service teachers’ self-efficacy.

Participant

This study forms a component of a broader multi-case research initiative examining teachers’ professional learning in the STEAM teacher professional development programs in China (Jiang et al. 2021 ; Wang et al. 2018 ; Wang et al. 2024 ). Within this context, one participant, Shuitao (pseudonym), is selected and reported in this current study. Shuitao was a first-year graduate student at a first-tier Normal university in Shanghai, China. Normal universities specialize in teacher education. Her graduate major was mathematics curriculum and instruction. Teaching practice courses are offered to students in this major exclusively during their third year of study. The selection of Shuitao was driven by three primary factors. Firstly, Shuitao attended the entire teacher professional development program and actively engaged in nearly all associated activities. Table 2 illustrates the timeline of the five stages in which Shuitao was involved. Secondly, her undergraduate major was applied mathematics, which was not related to mathematics teaching Footnote 7 . She possessed no prior teaching experience and had not undergone any systematic study of IAT before her involvement in the teacher professional development program. Thirdly, her other master’s courses during her first two years of study focused on mathematics education theory and did not include IAT Footnote 8 . Additionally, she scarcely participated in any other teaching practice outside of the teacher professional development program. As a pre-service teacher, Shuitao harbored a keen interest in IAT. Furthermore, she discovered that she possessed fewer teaching skills compared to her peers who had majored in education during their undergraduate studies. Hence, she had a strong desire to enhance her teaching skills. Consequently, Shuitao decided to participate in our teacher professional development program.

Shuitao was grouped with three other first-year graduate students during the teacher professional development program. She actively collaborated with them at every stage of the program. For instance, they advised each other on their IAT lesson designs, observed each other’s IAT practice and offered constructive suggestions for improvement.

Research question

Shuitao was a mathematics pre-service teacher who participated in one of our teacher professional development programs, focusing on integrating history into mathematics teaching (IHT) Footnote 9 . Notably, this teacher professional development program was designed based on our five-stage framework for teacher professional development programs of IAT. To examine the impact of this teacher professional development program on Shuitao’s self-efficacy related to IHT, this case study addresses the following research question:

What changes in Shuitao’s self-efficacy in individual performance regarding integrating history into mathematics teaching (SE-IHT-IP) may occur through participation in the teacher professional development program?

What changes in Shuitao’s self-efficacy in student outcomes regarding integrating history into mathematics teaching (SE-IHT-SO) may occur through participation in the teacher professional development program?

Data collection and analysis

Before Shuitao joined the teacher professional development program, a one-hour preliminary interview was conducted to guide her in self-narrating her psychological and cognitive state of IHT.

During the teacher professional development program, follow-up unstructured interviews were conducted once a month with Shuitao. All discussions in the development of IHT cases were recorded, Shuitao’s teaching and micro-teaching were videotaped, and the reflection notes, journals, and summary reports written by Shuitao were collected.

After completing the teacher professional development program, Shuitao participated in a semi-structured three-hour interview. The objectives of this interview were twofold: to reassess her self-efficacy and to explore the relationship between her self-efficacy changes and each stage of the teacher professional development program.

Interview data, discussions, reflection notes, journals, summary reports and videos, and analysis records were archived and transcribed before, during, and after the teacher professional development program.

In this study, we primarily utilized data from seven interviews: one conducted before the teacher professional development program, five conducted after each stage of the program, and one conducted upon completion of the program. Additionally, we reviewed Shuitao’s five reflective notes, which were written after each stage, as well as her final summary report that encompassed the entire teacher professional development program.

Merriam’s ( 1998 ) approach to coding data and inductive approach to retrieving possible concepts and themes were employed using a seven-stage method. Considering theoretical underpinnings in qualitative research is common when interpreting data (Strauss and Corbin 1990 ). First, a list based on our conceptual framework of teacher self-efficacy (see Table 1 ) was developed. The list included two codes (i.e., SE-IHT-IP and SE-IHT-SO). Second, all data were sorted chronologically, read and reread to be better understood. Third, texts were coded into multi-colored highlighting and comment balloons. Fourth, the data for groups of meanings, themes, and behaviors were examined. How these groups were connected within the conceptual framework of teacher self-efficacy was confirmed. Fifth, after comparing, confirming, and modifying, the selective codes were extracted and mapped onto the two categories according to the conceptual framework of teacher self-efficacy. Accordingly, changes in SE-IHT-IP and SE-IHT-SO at the five stages of the teacher professional development program were identified, respectively, and then the preliminary findings came (Strauss and Corbin 1990 ). In reality, in Shuitao’s narratives, SE-IHT-IP and SE-IHT-SO were frequently intertwined. Through our coding process, we differentiated between SE-IHT-IP and SE-IHT-SO, enabling us to obtain a more distinct understanding of how these two aspects of teacher self-efficacy evolved over time. This helped us address the two research questions effectively.

Reliability and validity

Two researchers independently analyzed the data to establish inter-rater reliability. The inter-rater reliability was established as kappa = 0.959. Stake ( 1995 ) suggested that the most critical assertions in a study require the greatest effort toward confirmation. In this study, three methods served this purpose and helped ensure the validity of the findings. The first way to substantiate the statement about the changes in self-efficacy was by revisiting each transcript to confirm whether the participant explicitly acknowledged the changes (Yin 2003 ). Such a check was repeated in the analysis of this study. The second way to confirm patterns in the data was by examining whether Shuitao’s statements were replicated in separate interviews (Morris and Usher 2011 ). The third approach involved presenting the preliminary conclusions to Shuitao and affording her the opportunity to provide feedback on the data and conclusions. This step aimed to ascertain whether we accurately grasped the true intentions of her statements and whether our subjective interpretations inadvertently influenced our analysis of her statements. Additionally, data from diverse sources underwent analysis by at least two researchers, with all researchers reaching consensus on each finding.

As each stage of our teacher professional development programs spanned a minimum of three months, numerous documented statements regarding the enhancement of Shuitao’s self-efficacy regarding IHT were recorded. Notably, what we present here offers only a concise overview of findings derived from our qualitative analysis. The changes in Shuitao’s SE-IHT-IP and SE-IHT-SO are organized chronologically, delineating the period before and during the teacher professional development program.

Before the teacher professional development program: “I have no confidence in IHT”

Before the teacher professional development program, Shuitao frequently expressed her lack of confidence in IHT. On the one hand, Shuitao expressed considerable apprehension about her individual performance in IHT. “How can I design and implement IHT lesson plans? I do not know anything [about it]…” With a sense of doubt, confusion and anxiety, Shuitao voiced her lack of confidence in her ability to design and implement an IHT case that would meet the requirements of the curriculum standards. Regarding the reasons for her lack of confidence, Shuitao attributed it to her insufficient theoretical knowledge and practical experience in IHT:

I do not know the basic approaches to IHT that I could follow… it is very difficult for me to find suitable historical materials… I am very confused about how to organize [historical] materials logically around the teaching goals and contents… [Furthermore,] I am [a] novice, [and] I have no IHT experience.

On the other hand, Shuitao articulated very low confidence in the efficacy of her IHT on student outcomes:

I think my IHT will have a limited impact on student outcomes… I do not know any specific effects [of history] other than making students interested in mathematics… In fact, I always think it is difficult for [my] students to understand the history… If students cannot understand [the history], will they feel bored?

This statement suggests that Shuitao did not fully grasp the significance of IHT. In fact, she knew little about the educational significance of history for students, and she harbored no belief that her IHT approach could positively impact students. In sum, her SE-IHT-SO was very low.

After stage 1: “I can do well in the first step of IHT”

After Stage 1, Shuitao indicated a slight improvement in her confidence in IHT. She attributed this improvement to her acquisition of theoretical knowledge in IHT, the approaches for selecting history-related materials, and an understanding of the educational value of history.

One of Shuitao’s primary concerns about implementing IHT before the teacher professional development program was the challenge of sourcing suitable history-related materials. However, after Stage 1, Shuitao explicitly affirmed her capability in this aspect. She shared her experience of organizing history-related materials related to logarithms as an example.

Recognizing the significance of suitable history-related materials in effective IHT implementation, Shuitao acknowledged that conducting literature studies significantly contributed to enhancing her confidence in undertaking this initial step. Furthermore, she expressed increased confidence in designing IHT lesson plans by utilizing history-related materials aligned with teaching objectives derived from the curriculum standards. In other words, her SE-IHT-IP was enhanced. She said:

After experiencing multiple discussions, I gradually know more about what kinds of materials are essential and should be emphasized, what kinds of materials should be adapted, and what kinds of materials should be omitted in the classroom instructions… I have a little confidence to implement IHT that could meet the requirements [of the curriculum standards] since now I can complete the critical first step [of IHT] well…

However, despite the improvement in her confidence in IHT following Stage 1, Shuitao also expressed some concerns. She articulated uncertainty regarding her performance in the subsequent stages of the teacher professional development program. Consequently, her confidence in IHT experienced only a modest increase.

After stage 2: “I participate in the development of IHT cases, and my confidence is increased a little bit more”

Following Stage 2, Shuitao reported further increased confidence in IHT. She attributed this growth to two main factors. Firstly, she successfully developed several instructional designs for IHT through collaboration with in-service teachers. These collaborative experiences enabled her to gain a deeper understanding of IHT approaches and enhance her pedagogical content knowledge in this area, consequently bolstering her confidence in her ability to perform effectively. Secondly, Shuitao observed the tangible impact of IHT cases on students in real classroom settings, which reinforced her belief in the efficacy of IHT. These experiences instilled in her a greater sense of confidence in her capacity to positively influence her students through her implementation of IHT. Shuitao remarked that she gradually understood how to integrate suitable history-related materials into her instructional designs (e.g., employ a genetic approach Footnote 10 ), considering it as the second important step of IHT. She shared her experience of developing IHT instructional design on the concept of logarithms. After creating several iterations of IHT instructional designs, Shuitao emphasized that her confidence in SE-IHT-IP has strengthened. She expressed belief in her ability to apply these approaches to IHT, as well as the pedagogical content knowledge of IHT, acquired through practical experience, in her future teaching endeavors. The following is an excerpt from the interview:

I learned some effective knowledge, skills, techniques and approaches [to IHT]… By employing these approaches, I thought I could [and] I had the confidence to integrate the history into instructional designs very well… For instance, [inspired] by the genetic approach, we designed a series of questions and tasks based on the history of logarithms. The introduction of the new concept of logarithms became very natural, and it perfectly met the requirements of our curriculum standards, [which] asked students to understand the necessity of learning the concept of logarithms…

Shuitao actively observed the classroom teaching conducted by her cooperating in-service teacher. She helped her cooperating in-service teacher in collecting and analyzing students’ feedback. Subsequently, discussions ensued on how to improve the instructional designs based on this feedback. The refined IHT instructional designs were subsequently re-implemented by the in-service teacher. After three rounds of developing IHT cases, Shuitao became increasingly convinced of the significance and efficacy of integrating history into teaching practices, as evidenced by the following excerpt:

The impacts of IHT on students are visible… For instance, more than 93% of the students mentioned in the open-ended questionnaires that they became more interested in mathematics because of the [historical] story of Napier… For another example, according to the results of our surveys, more than 75% of the students stated that they knew log a ( M  +  N ) = log a M  × log a N was wrong because of history… I have a little bit more confidence in the effects of my IHT on students.

This excerpt highlights that Shuitao’s SE-IHT-SO was enhanced. She attributed this enhancement to her realization of the compelling nature of history and her belief in her ability to effectively leverage its power to positively influence her students’ cognitive and emotional development. This also underscores the importance of reinforcing pre-service teachers’ awareness of the significance of history. Nonetheless, Shuiato elucidated that she still retained concerns regarding the effectiveness of her IHT implementation. Her following statement shed light on why her self-efficacy only experienced a marginal increase in this stage:

Knowing how to do it successfully and doing it successfully in practice are two totally different things… I can develop IHT instructional designs well, but I have no idea whether I can implement them well and whether I can introduce the history professionally in practice… My cooperation in-service teacher has a long history of teaching mathematics and gains rich experience in educational practices… If I cannot acquire some required teaching skills and capabilities, I still cannot influence my students powerfully.

After stage 3: “Practice makes perfect, and my SE-IHT-IP is steadily enhanced after a hit”

After successfully developing IHT instructional designs, the next critical step was the implementation of these designs. Drawing from her observations of her cooperating in-service teachers’ IHT implementations and discussions with other pre-service teachers, Shuitao developed her own IHT lesson plans. In Stage 3, she conducted simulated teaching sessions and evaluated her teaching performance ten times Footnote 11 . Shuitao claimed that her SE-IHT-IP steadily improved over the course of these sessions. According to Shuitao, two main processes in Stage 3 facilitated this steady enhancement of SE-IHT-IP.

On the one hand, through the repeated implementation of simulated teaching sessions, Shuitao’s teaching proficiency and fluency markedly improved. Shuitao first described the importance of teaching proficiency and fluency:

Since the detailed history is not included in our curriculum standards and textbooks, if I use my historical materials in class, I have to teach more contents than traditional teachers. Therefore, I have to teach proficiently so that teaching pace becomes a little faster than usual… I have to teach fluently so as to use each minute efficiently in my class. Otherwise, I cannot complete the teaching tasks required [by curriculum standards].

As Shuitao said, at the beginning of Stage 3, her self-efficacy even decreased because she lacked teaching proficiency and fluency and was unable to complete the required teaching tasks:

In the first few times of simulated teaching, I always needed to think for a second about what I should say next when I finish one sentence. I also felt very nervous when I stood in the front of the classrooms. This made my narration of the historical story between Briggs and Napier not fluent at all. I paused many times to look for some hints on my notes… All these made me unable to complete the required teaching tasks… My [teaching] confidence took a hit.

Shuitao quoted the proverb, “practice makes perfect”, and she emphasized that it was repeated practice that improved her teaching proficiency and fluency:

I thought I had no other choice but to practice IHT repeatedly… [At the end of Stage 3,] I could naturally remember most words that I should say when teaching the topics that I selected… My teaching proficiency and fluency was improved through my repeated review of my instructional designs and implementation of IHT in the micro-classrooms… With the improvement [of my teaching proficiency and fluency], I could complete the teaching tasks, and my confidence was increased as well.

In addition, Shuitao also mentioned that through this kind of self-exploration in simulated teaching practice, her teaching skills and capabilities (e.g., blackboard writing, abilities of language organization abilities, etc.) improved. This process was of great help to her enhancement of SE-IHT-IP.

On the other hand, Shuitao’s simulated teaching underwent assessment by herself, with mentors, in-service teachers and fellow pre-service teachers. This comprehensive evaluation process played a pivotal role in enhancing her individual performance and self-efficacy. Reflecting on this aspect, Shuitao articulated the following sentiments in one of her reflection reports:

By watching the videos, conducting self-assessment, and collecting feedback from others, I can understand what I should improve or emphasize in my teaching. [Then,] I think my IHT can better meet the requirements [of curriculum standards]… I think my teaching performance is getting better and better.

After stage 4: “My micro-courses influenced students positively, and my SE-IHT-SO is steadily enhanced”

In Stage 4, Shuitao commenced by creating 5-min micro-course videos. Subsequently, she played these videos in her cooperating in-service teachers’ authentic classroom settings and collected student feedback. This micro-course was played at the end of her cooperating in-service teachers’ lesson Footnote 12 . Shuitao wrote in her reflections that this micro-course of logarithms helped students better understand the nature of mathematics:

According to the results of our surveys, many students stated that they knew the development and evolution of the concept of logarithms is a long process and many mathematicians from different countries have contributed to the development of the concept of logarithms… This indicated that my micro-course helped students better understand the nature of mathematics… My micro-course about the history informed students that mathematics is an evolving and human subject and helped them understand the dynamic development of the [mathematics] concept…

Meanwhile, Shuitao’s micro-course positively influenced some students’ beliefs towards mathematics. As evident from the quote below, integrating historical context into mathematics teaching transformed students’ perception of the subject, boosting Shuitao’s confidence too.

Some students’ responses were very exciting… [O]ne [typical] response stated, he always regarded mathematics as abstract, boring, and dreadful subject; but after seeing the photos of mathematicians and great men and learning the development of the concept of logarithms through the micro-course, he found mathematics could be interesting. He wanted to learn more the interesting history… Students’ such changes made me confident.

Furthermore, during post-class interviews, several students expressed their recognition of the significance of the logarithms concept to Shuitao, attributing this realization to the insights provided by prominent figures in the micro-courses. They also conveyed their intention to exert greater effort in mastering the subject matter. This feedback made Shuitao believe that her IHT had the potential to positively influence students’ attitudes towards learning mathematics.

In summary, Stage 4 marked Shuitao’s first opportunity to directly impact students through her IHT in authentic classroom settings. Despite implementing only brief 5-min micro-courses integrating history during each session, the effectiveness of her short IHT implementation was validated by student feedback. Shuitao unequivocally expressed that students actively engaged with her micro-courses and that these sessions positively influenced them, including attitudes and motivation toward mathematics learning, understanding of mathematics concepts, and beliefs regarding mathematics. These collective factors contributed to a steady enhancement of her confidence in SE-IHT-SO.

After stage 5: “My overall self-efficacy is greatly enhanced”

Following Stage 5, Shuitao reported a significant increase in her overall confidence in IHT, attributing it to gaining mastery through successful implementations of IHT in real classroom settings. On the one hand, Shuitao successfully designed and executed her IHT lesson plans, consistently achieving the teaching objectives mandated by curriculum standards. This significantly enhanced her SE-IHT-IP. On the other hand, as Shuitao’s IHT implementation directly influenced her students, her confidence in SE-IHT-SO experienced considerable improvement.

According to Bandura ( 1997 ), mastery experience is the most powerful source of self-efficacy. Shuitao’s statements confirmed this. As she claimed, her enhanced SE-IHT-IP in Stage 5 mainly came from the experience of successful implementations of IHT in real classrooms:

[Before the teacher professional development program,] I had no idea about implementing IHT… Now, I successfully implemented IHT in senior high school [classrooms] many times… I can complete the teaching tasks and even better completed the teaching objectives required [by the curriculum standards]… The successful experience greatly enhances my confidence to perform well in my future implementation of IHT… Yeah, I think the successful teaching practice experience is the strongest booster of my confidence.

At the end of stage 5, Shuitao’s mentors and in-service teachers gave her a high evaluation. For instance, after Shuitao’s IHT implementation of the concept of logarithms, all mentors and in-service teachers consistently provided feedback that her IHT teaching illustrated the necessity of learning the concept of logarithms and met the requirements of the curriculum standards very well. This kind of verbal persuasion (Bandura 1997 ) enhanced her SE-IHT-IP.

Similarly, Shuitao’s successful experience of influencing students positively through IHT, as one kind of mastery experience, powerfully enhanced her SE-IHT-SO. She described her changes in SE-IHT-SO as follows:

I could not imagine my IHT could be so influential [before]… But now, my IHT implementation directly influenced students in so many aspects… When I witnessed students’ real changes in various cognitive and affective aspects, my confidence was greatly improved.

Shuitao described the influence of her IHT implementation of the concept of logarithms on her students. The depiction is grounded in the outcomes of surveys conducted by Shuitao following her implementation. Shuitao asserted that these results filled her with excitement and confidence regarding her future implementation of IHT.

In summary, following Stage 5 of the teacher professional development program, Shuitao experienced a notable enhancement in her overall self-efficacy, primarily attributed to her successful practical experience in authentic classroom settings during this stage.

A primary objective of our teacher professional development programs is to equip pre-service teachers with the skills and confidence needed to effectively implement IAT. Our findings show that one teacher professional development program, significantly augmented a participant’s TSE-IHT across two dimensions: individual performance and student outcomes. Considering the pressing need to provide STEAM teachers with effective professional training (e.g., Boice et al. 2021 ; Duong et al. 2024 ; Herro et al. 2019 ; Jacques et al. 2020 ; Park and Cho 2022 ; Perignat and Katz-Buonincontro 2019 ), the proposed five-stage framework holds significant promise in both theoretical and practical realms. Furthermore, this study offers a viable solution to address the prevalent issue of low levels of teacher self-efficacy in interdisciplinary teaching, including IAT, which is critical in STEAM education (Zhou et al. 2023 ). This study holds the potential to make unique contributions to the existing body of literature on teacher self-efficacy, teacher professional learning models and the design of teacher professional development programs of IAT.

Firstly, this study enhances our understanding of the development of teacher self-efficacy. Our findings further confirm the complexity of the development of teacher self-efficacy. On the one hand, the observed enhancement of the participant’s teacher self-efficacy did not occur swiftly but unfolded gradually through a protracted, incremental process. Moreover, it is noteworthy that the participant’s self-efficacy exhibited fluctuations, underscoring that the augmentation of teacher self-efficacy is neither straightforward nor linear. On the other hand, the study elucidated that the augmentation of teacher self-efficacy constitutes an intricate, multi-level system that interacts with teacher knowledge, skills, and other beliefs. This finding resonates with prior research on teacher self-efficacy (Morris et al. 2017 ; Xenofontos and Andrews 2020 ). For example, our study revealed that Shuitao’s enhancement of SE-IHT-SO may always be interwoven with her continuous comprehension of the significance of the A&H in classroom settings. Similarly, the participant progressively acknowledged the educational value of A&H in classroom contexts in tandem with the stepwise enhancement of SE-IHT-SO. Factors such as the participant’s pedagogical content knowledge of IHT, instructional design, and teaching skills were also identified as pivotal components of SE-IHT-IP. This finding corroborates Morris and Usher ( 2011 ) assertion that sustained improvements in self-efficacy stem from developing teachers’ skills and knowledge. With the bolstering of SE-IHT-IP, the participant’s related teaching skills and content knowledge also exhibited improvement.

Methodologically, many researchers advocate for qualitative investigations into self-efficacy (e.g., Philippou and Pantziara 2015; Klassen et al. 2011 ; Wyatt 2015 ; Xenofontos and Andrews 2020 ). While acknowledging limitations in sample scope and the generalizability of the findings, this study offers a longitudinal perspective on the stage-by-stage development of teacher self-efficacy and its interactions with different factors (i.e., teacher knowledge, skills, and beliefs), often ignored by quantitative studies. Considering that studies of self-efficacy have been predominantly quantitative, typically drawing on survey techniques and pre-determined scales (Xenofontos and Andrews, 2020 ; Zhou et al. 2023 ), this study highlights the need for greater attention to qualitative studies so that more cultural, situational and contextual factors in the development of self-efficacy can be captured.

Our study provides valuable practical implications for enhancing pre-service teachers’ self-efficacy. We conceptualize teacher self-efficacy in two primary dimensions: individual performance and student outcomes. On the one hand, pre-service teachers can enhance their teaching qualities, boosting their self-efficacy in individual performance. The adage “practice makes perfect” underscores the necessity of ample teaching practice opportunities for pre-service teachers who lack prior teaching experience. Engaging in consistent and reflective practice helps them develop confidence in their teaching qualities. On the other hand, pre-service teachers should focus on positive feedback from their students, reinforcing their self-efficacy in individual performance. Positive student feedback serves as an affirmation of their teaching effectiveness and encourages continuous improvement. Furthermore, our findings highlight the significance of mentors’ and peers’ positive feedback as critical sources of teacher self-efficacy. Mentors and peers play a pivotal role in the professional growth of pre-service teachers by actively encouraging them and recognizing their teaching achievements. Constructive feedback from experienced mentors and supportive peers fosters a collaborative learning environment and bolsters the self-confidence of pre-service teachers. Additionally, our research indicates that pre-service teachers’ self-efficacy may fluctuate. Therefore, mentors should be prepared to help pre-service teachers manage teaching challenges and setbacks, and alleviate any teaching-related anxiety. Mentors can help pre-service teachers build resilience and maintain a positive outlook on their teaching journey through emotional support and guidance. Moreover, a strong correlation exists between teacher self-efficacy and teacher knowledge and skills. Enhancing pre-service teachers’ knowledge base and instructional skills is crucial for bolstering their overall self-efficacy.

Secondly, this study also responds to the appeal to understand teachers’ professional learning from a holistic perspective and interrelate teachers’ professional learning process with student outcome variables (Sancar et al. 2021 ), and thus contributes to the understanding of the complexity of STEAM teachers’ professional learning. On the one hand, we have confirmed Cai et al.’s ( 2020 ) teacher professional learning model in a new context, namely STEAM teacher education. Throughout the teacher professional development program, the pre-service teacher, Shuitao, demonstrated an augmentation in her knowledge, encompassing both content knowledge and pedagogical understanding concerning IHT. Moreover, her beliefs regarding IHT transformed as a result of her engagement in teacher learning across the five stages. This facilitated her in executing effective IHT teaching and improving her students’ outcomes. On the other hand, notably, in our studies (including this current study and some follow-up studies), student feedback is a pivotal tool to assist teachers in discerning the impact they are effectuating. This enables pre-service teachers to grasp the actual efficacy of their teaching efforts and subsequently contributes significantly to the augmentation of their self-efficacy. Such steps have seldom been conducted in prior studies (e.g., Cai et al. 2020 ), where student outcomes are often perceived solely as the results of teachers’ instruction rather than sources informing teacher beliefs. Additionally, this study has validated both the interaction between teaching performance and teacher beliefs and between teacher knowledge and teacher beliefs. These aspects were overlooked in Cai et al.’s ( 2020 ) model. More importantly, while Clarke and Hollingsworth’s ( 2002 ) Interconnected Model of Professional Growth illustrates the connections between the domain of consequence and the personal domain, as well as between the personal domain and the domain of practice, it does not adequately clarify the complex relationships among the factors within the personal domain (e.g., the interaction between teacher knowledge and teacher beliefs). Therefore, our study also supplements Clarke and Hollingsworth’s ( 2002 ) model by addressing these intricacies. Based on our findings, an updated model of teacher professional learning has been proposed, as shown in Fig. 3 . This expanded model indicates that teacher learning should be an ongoing and sustainable process, with the enhancement of student learning not marking the conclusion of teacher learning, but rather serving as the catalyst for a new phase of learning. In this sense, we advocate for further research to investigate the tangible impacts of teacher professional development programs on students and how those impacts stimulate subsequent cycles of teacher learning.

Note: Paths in blue were proposed by Cai et al. ( 2020 ), and paths in yellow are proposed and verified in this study.

Thirdly, in light of the updated model of teacher professional learning (see Fig. 3 ), this study provides insights into the design of teacher professional development programs of IAT. According to Huang et al. ( 2022 ), to date, very few studies have set goals to “develop a comprehensive understanding of effective designs” for STEM (or STEAM) teacher professional development programs (p. 15). To fill this gap, this study proposes a novel and effective five-stage framework for teacher professional development programs of IAT. This framework provides a possible and feasible solution to the challenges of STEAM teacher professional development programs’ design and planning, and teachers’ IAT practice (Boice et al. 2021 ; Herro et al. 2019 ; Jacques et al. 2020 ; Park and Cho 2022 ; Perignat and Katz-Buonincontro 2019 ).

Specifically, our five-stage framework incorporates at least six important features. Firstly, teacher professional development programs should focus on specific STEAM content. Given the expansive nature of STEAM, teacher professional development programs cannot feasibly encompass all facets of its contents. Consistent with recommendations by Cai et al. ( 2020 ), Desimone et al. ( 2002 ) and Garet et al. ( 2001 ), an effective teacher professional development program should prioritize content focus. Our five-stage framework is centered on IAT. Throughout an 18-month duration, each pre-service teacher is limited to selecting one subcomponent of A&H, such as history, for integration into their subject teaching (i.e., mathematics teaching, technology teaching or science teaching) within one teacher professional development program. Secondly, in response to the appeals that teacher professional development programs should shift from emphasizing teaching and instruction to emphasizing student learning (Cai et al. 2020 ; Calabrese et al. 2024 ; Hwang et al. 2024 ; Marco and Palatnik 2024 ; Örnek and Soylu 2021 ), our framework requires pre-service teachers to pay close attention to the effects of IAT on student learning outcomes, and use students’ feedback as the basis of improving their instruction. Thirdly, prior studies found that teacher education with a preference for theory led to pre-service teachers’ dissatisfaction with the quality of teacher professional development program and hindered the development of pre-service teachers’ teaching skills and teaching beliefs, which also widened the gap between theory and practice (Hennissen et al. 2017 ; Ord and Nuttall 2016 ). In this regard, our five-stage framework connects theory and teaching practice closely. In particular, pre-service teachers can experience the values of IAT not only through theoretical learning but also through diverse teaching practices. Fourthly, we build a teacher community of practice tailored for pre-service teachers. Additionally, we aim to encourage greater participation of in-service teachers in such teacher professional development programs designed for pre-service educators in STEAM teacher education. By engaging in such programs, in-service teachers can offer valuable teaching opportunities for pre-service educators and contribute their insights and experiences from teaching practice. Importantly, pre-service teachers stand to gain from the in-service teachers’ familiarity with textbooks, subject matter expertise, and better understanding of student dynamics. Fifthly, our five-stage framework lasts for an extended period, spanning 18 months. This duration ensures that pre-service teachers engage in a sustained and comprehensive learning journey. Lastly, our framework facilitates a practical understanding of “integration” by offering detailed, sequential instructions for blending two disciplines in teaching. For example, our teacher professional development programs prioritize systematic learning of pedagogical theories and simulated teaching experiences before pre-service teachers embark on real STEAM teaching endeavors. This approach is designed to mitigate the risk of unsuccessful experiences during initial teaching efforts, thereby safeguarding pre-service teachers’ teacher self-efficacy. Considering the complexity of “integration” in interdisciplinary teaching practices, including IAT (Han et al. 2022 ; Ryu et al. 2019 ), we believe detailed stage-by-stage and step-by-step instructions are crucial components of relevant pre-service teacher professional development programs. Notably, this aspect, emphasizing structural instructional guidance, has not been explicitly addressed in prior research (e.g., Cai et al. 2020 ). Figure 4 illustrates the six important features outlined in this study, encompassing both established elements and the novel addition proposed herein, describing an effective teacher professional development program.

Note: STEAM refers to science, technology, engineering, arts and humanities, and mathematics.

The successful implementation of this framework is also related to the Chinese teacher education system and cultural background. For instance, the Chinese government has promoted many university-school collaboration initiatives, encouraging in-service teachers to provide guidance and practical opportunities for pre-service teachers (Lu et al. 2019 ). Influenced by Confucian values emphasizing altruism, many experienced in-service teachers in China are eager to assist pre-service teachers, helping them better realize their teaching career aspirations. It is reported that experienced in-service teachers in China show significantly higher motivation than their international peers when mentoring pre-service teachers (Lu et al. 2019 ). Therefore, for the successful implementation of this framework in other countries, it is crucial for universities to forge close collaborative relationships with K-12 schools and actively involve K-12 teachers in pre-service teacher education.

Notably, approximately 5% of our participants dropped out midway as they found that the IAT practice was too challenging or felt overwhelmed by the number of required tasks in the program. Consequently, we are exploring options to potentially simplify this framework in future iterations.

Without minimizing the limitations of this study, it is important to recognize that a qualitative longitudinal case study can be a useful means of shedding light on the development of a pre-service STEAM teacher’s self-efficacy. However, this methodology did not allow for a pre-post or a quasi-experimental design, and the effectiveness of our five-stage framework could not be confirmed quantitatively. In the future, conducting more experimental or design-based studies could further validate the effectiveness of our framework and broaden our findings. Furthermore, future studies should incorporate triangulation methods and utilize multiple data sources to enhance the reliability and validity of the findings. Meanwhile, owing to space limitations, we could only report the changes in Shuitao’s SE-IHT-IP and SE-IHT-SO here, and we could not describe the teacher self-efficacy of other participants regarding IAT. While nearly all of the pre-service teachers experienced an improvement in their teacher self-efficacy concerning IAT upon participating in our teacher professional development programs, the processes of their change were not entirely uniform. We will need to report the specific findings of these variations in the future. Further studies are also needed to explore the factors contributing to these variations. Moreover, following this study, we are implementing more teacher professional development programs of IAT. Future studies can explore the impact of this framework on additional aspects of pre-service STEAM teachers’ professional development. This will help gain a more comprehensive understanding of its effectiveness and potential areas for further improvement. Additionally, our five-stage framework was initially developed and implemented within the Chinese teacher education system. Future research should investigate how this framework can be adapted in other educational systems and cultural contexts.

The impetus behind this study stems from the burgeoning discourse advocating for the integration of A&H disciplines into STEM education on a global scale (e.g., Land 2020 ; Park and Cho 2022 ; Uştu et al. 2021 ; Vaziri and Bradburn 2021 ). Concurrently, there exists a pervasive concern regarding the challenges teachers face in implementing STEAM approaches, particularly in the context of IAT practices (e.g., Boice et al. 2021 ; Herro et al. 2019 ; Jacques et al. 2020 ; Park and Cho 2022 ; Perignat and Katz-Buonincontro 2019 ). To tackle this challenge, we first proposed a five-stage framework designed for teacher professional development programs of IAT. Then, utilizing this innovative framework, we implemented a series of teacher professional development programs. Drawing from the recommendations of Bray-Clark and Bates ( 2003 ), Kelley et al. ( 2020 ) and Zhou et al. ( 2023 ), we have selected teacher self-efficacy as a key metric to examine the effectiveness of the five-stage framework. Through a qualitative longitudinal case study, we scrutinized the influence of a specific teacher professional development program on the self-efficacy of a single pre-service teacher over an 18-month period. Our findings revealed a notable enhancement in teacher self-efficacy across both individual performance and student outcomes. The observed enhancement of the participant’s teacher self-efficacy did not occur swiftly but unfolded gradually through a prolonged, incremental process. Building on our findings, an updated model of teacher learning has been proposed. The updated model illustrates that teacher learning should be viewed as a continuous and sustainable process, wherein teaching performance, teacher beliefs, and teacher knowledge dynamically interact with one another. The updated model also confirms that teacher learning is inherently intertwined with student learning in STEAM education. Furthermore, this study also summarizes effective design features of STEAM teacher professional development programs.

Data availability

The datasets generated and/or analyzed during this study are not publicly available due to general data protection regulations, but are available from the corresponding author on reasonable request.

In their review article, Morris et al. ( 2017 ) equated “teaching self-efficacy” and “teacher self-efficacy” as synonymous concepts. This perspective is also adopted in this study.

An effective teacher professional development program should have specific, focused, and clear content instead of broad and scattered ones. Therefore, each pre-service teacher can only choose to integrate one subcomponent of A&H into their teaching in one teacher professional development program. For instance, Shuitao, a mathematics pre-service teacher, participated in one teacher professional development program focused on integrating history into mathematics teaching. However, she did not explore the integration of other subcomponents of A&H into her teaching during her graduate studies.

In the micro-classrooms, multi-angle, and multi-point high-definition video recorders are set up to record the teaching process.

In micro-teaching, mentors, in-service teachers, and other fellow pre-service teachers take on the roles of students.

In China, teachers can video record one section of a lesson and play them in formal classes. This is a practice known as a micro-course. For instance, in one teacher professional development program of integrating history into mathematics teaching, micro-courses encompass various mathematics concepts, methods, ideas, history-related material and related topics. Typically, teachers use these micro-courses to broaden students’ views, foster inquiry-based learning, and cultivate critical thinking skills. Such initiatives play an important role in improving teaching quality.

Many university-school collaboration initiatives in China focus on pre-service teachers’ practicum experiences (Lu et al. 2019 ). Our teacher professional development program is also supported by many K-12 schools in Shanghai. Personal information in videos is strictly protected.

In China, students are not required to pursue a graduate major that matches their undergraduate major. Most participants in our teacher professional development programs did not pursue undergraduate degrees in education-related fields.

Shuitao’s university reserves Wednesday afternoons for students to engage in various programs or clubs, as classes are not scheduled during this time. Similarly, our teacher professional development program activities are planned for Wednesday afternoons to avoid overlapping with participants’ other coursework commitments.

History is one of the most important components of A&H (Park and Cho 2022 ).

To learn more about genetic approach (i.e., genetic principle), see Jankvist ( 2009 ).

For the assessment process, see Fig. 2 .

Shuitao’s cooperating in-service teacher taught the concept of logarithms in Stage 2. In Stage 4, the teaching objective of her cooperating in-service teacher’s review lesson was to help students review the concept of logarithms to prepare students for the final exam.

Akiba M, Murata A, Howard C, Wilkinson B, Fabrega J (2019) Race to the top and lesson study implementation in Florida: District policy and leadership for teacher professional development. In: Huang R, Takahashi A, daPonte JP (eds) Theory and practice of lesson study in mathematics, pp. 731–754. Springer, Cham. https://doi.org/10.1007/978-3-030-04031-4_35

Alkhabra YA, Ibrahem UM, Alkhabra SA (2023) Augmented reality technology in enhancing learning retention and critical thinking according to STEAM program. Humanit Soc Sci Commun 10:174. https://doi.org/10.1057/s41599-023-01650-w

Article   Google Scholar  

Alwafi EM, Downey C, Kinchin G (2020) Promoting pre-service teachers’ engagement in an online professional learning community: Support from practitioners. J Professional Cap Community 5(2):129–146. https://doi.org/10.1108/JPCC-10-2019-0027

Archibald S, Coggshall JG, Croft A, Goe L (2011) High-quality professional development for all teachers: effectively allocating resources. National Comprehensive Center for Teacher Quality, Washington, DC

Google Scholar  

Bandura A (1977) Self-efficacy: Toward a unifying theory of behavioral change. Psychological Rev 84:191–215. https://doi.org/10.1016/0146-6402(78)90002-4

Article   CAS   Google Scholar  

Bandura A (1986) The explanatory and predictive scope of self-efficacy theory. J Soc Clin Psychol 4:359–373. https://doi.org/10.1521/jscp.1986.4.3.359

Bandura A (1997) Self-efficacy: The exercise of control. Freeman, New York

Basckin C, Strnadová I, Cumming TM (2021) Teacher beliefs about evidence-based practice: A systematic review. Int J Educ Res 106:101727. https://doi.org/10.1016/j.ijer.2020.101727

Bray-Clark N, Bates R (2003) Self-efficacy beliefs and teacher effectiveness: Implications for professional development. Prof Educ 26(1):13–22

Blonder R, Benny N, Jones MG (2014) Teaching self-efficacy of science teachers. In: Evans R, Luft J, Czerniak C, Pea C (eds), The role of science teachers’ beliefs in international classrooms: From teacher actions to student learning, Sense Publishers, Rotterdam, Zuid-Holland, pp. 3–16

Boice KL, Jackson JR, Alemdar M, Rao AE, Grossman S, Usselman M (2021) Supporting teachers on their STEAM journey: A collaborative STEAM teacher training program. Educ Sci 11(3):105. https://doi.org/10.3390/educsci11030105

Cai J (2017) Longitudinally investigating the effect of teacher professional development on instructional practice and student learning: A focus on mathematical problem posing. The University of Delaware, Newark, DE

Cai J, Chen T, Li X, Xu R, Zhang S, Hu Y, Zhang L, Song N (2020) Exploring the impact of a problem-posing workshop on elementary school mathematics teachers’ conceptions on problem posing and lesson design. Int J Educ Res 102:101404. https://doi.org/10.1016/j.ijer.2019.02.004

Calabrese JE, Capraro MM, Viruru R (2024) Semantic structure and problem posing: Preservice teachers’ experience. School Sci Math. https://doi.org/10.1111/ssm.12648

Clarke D, Hollingsworth H (2002) Elaborating a model of teacher professional growth. Teach Teach Educ 18(8):947–967. https://doi.org/10.1016/S0742-051X(02)00053-7

Corden A, Millar J (2007) Time and change: A review of the qualitative longitudinal research literature for social policy. Soc Policy Soc 6(4):583–592. https://doi.org/10.1017/S1474746407003910

Darling-Hammond L, Hyler ME, Gardner M (2017) Effective teacher professional development. Learning Policy Institute, Palo Alto, CA

Book   Google Scholar  

de la Garza A (2021) Internationalizing the curriculum for STEAM (STEM+ Arts and Humanities): From intercultural competence to cultural humility. J Stud Int Educ 25(2):123–135. https://doi.org/10.1177/1028315319888468

Article   MathSciNet   Google Scholar  

Desimone LM, Garet MS (2015) Best practices in teachers’ professional development in the United States. Psychol, Soc, Educ 7(3):252–263

Desimone LM, Porter AC, Garet MS, Yoon KS, Birman BF (2002) Effects of professional development on teachers’ instruction: Results from a three-year longitudinal study. Educ Evaluation Policy Anal 24(2):81–112. https://doi.org/10.3102/01623737024002081

Dicks SG, Northam HL, van Haren FM, Boer DP (2023) The bereavement experiences of families of potential organ donors: a qualitative longitudinal case study illuminating opportunities for family care. Int J Qualitative Stud Health Well-being 18(1):1–24. https://doi.org/10.1080/17482631.2022.2149100

Ding M, Huang R, Pressimone Beckowski C, Li X, Li Y (2024) A review of lesson study in mathematics education from 2015 to 2022: implementation and impact. ZDM Math Educ 56:87–99. https://doi.org/10.1007/s11858-023-01538-8

Duong NH, Nam NH, Trung TT (2024) Factors affecting the implementation of STEAM education among primary school teachers in various countries and Vietnamese educators: comparative analysis. Education 3–13. https://doi.org/10.1080/03004279.2024.2318239

English LD (2016) STEM education K-12: Perspectives on integration. Int J STEM Educ 3:3. https://doi.org/10.1186/s40594-016-0036-1

Garet MS, Porter AC, Desimone L, Birman BF, Yoon KS (2001) What makes professional development effective? Results from a national sample of teachers. Am Educ Res J 38(4):915–945. https://doi.org/10.3102/00028312038004915

Gates AE (2017) Benefits of a STEAM collaboration in Newark, New Jersey: Volcano simulation through a glass-making experience. J Geosci Educ 65(1):4–11. https://doi.org/10.5408/16-188.1

Geng J, Jong MSY, Chai CS (2019) Hong Kong teachers’ self-efficacy and concerns about STEM education. Asia-Pac Educ Researcher 28:35–45. https://doi.org/10.1007/s40299-018-0414-1

Han J, Kelley T, Knowles JG (2022) Building a sustainable model of integrated stem education: investigating secondary school STEM classes after an integrated STEM project. Int J Technol Design Educ. https://doi.org/10.1007/s10798-022-09777-8

Henderson S, Holland J, McGrellis S, Sharpe S, Thomson R (2012) Storying qualitative longitudinal research: Sequence, voice and motif. Qualitative Res 12(1):16–34. https://doi.org/10.1177/1468794111426232

Hennissen P, Beckers H, Moerkerke G (2017) Linking practice to theory in teacher education: A growth in cognitive structures. Teach Teach Educ 63:314–325. https://doi.org/10.1016/j.tate.2017.01.008

Henson RK (2002) From adolescent angst to adulthood: Substantive implications and measurement dilemmas in the development of teacher efficacy research. Educ Psychologist 37:137–150. https://doi.org/10.1207/S15326985EP3703_1

Herro D, Quigley C (2016) Innovating with STEAM in middle school classrooms: remixing education. Horizon 24(3):190–204. https://doi.org/10.1108/OTH-03-2016-0008

Herro D, Quigley C, Cian H (2019) The challenges of STEAM instruction: Lessons from the field. Action Teach Educ 41(2):172–190. https://doi.org/10.1080/01626620.2018.1551159

Huang B, Jong MSY, Tu YF, Hwang GJ, Chai CS, Jiang MYC (2022) Trends and exemplary practices of STEM teacher professional development programs in K-12 contexts: A systematic review of empirical studies. Comput Educ 189:104577. https://doi.org/10.1016/j.compedu.2022.104577

Hunter-Doniger T, Sydow L (2016) A journey from STEM to STEAM: A middle school case study. Clearing House 89(4-5):159–166. https://doi.org/10.1080/00098655.2016.1170461

Hwang S, Xu R, Yao Y, Cai J (2024) Learning to teach through problem posing: A teacher’s journey in a networked teacher−researcher partnership. J Math Behav 73:101120. https://doi.org/10.1016/j.jmathb.2023.101120

Jacques LA, Cian H, Herro DC, Quigley C (2020) The impact of questioning techniques on STEAM instruction. Action Teach Educ 42(3):290–308. https://doi.org/10.1080/01626620.2019.1638848

Jankvist UT (2009) A categorization of the “whys” and “hows” of using history in mathematics education. Educ Stud Math 71:235–261. https://doi.org/10.1007/s10649-008-9174-9

Jiang H, Chugh R, Turnbull D, Wang X, Chen S (2023) Modeling the impact of intrinsic coding interest on STEM career interest: evidence from senior high school students in two large Chinese cities. Educ Inf Technol 28:2639–2659. https://doi.org/10.1007/s10639-022-11277-0

Jiang H, Chugh R, Turnbull D, Wang X, Chen S (2024a) Exploring the effects of technology-related informal mathematics learning activities: A structural equation modeling analysis. Int J Sci Math Educ . Advance online publication. https://doi.org/10.1007/s10763-024-10456-4

Jiang H, Islam AYMA, Gu X, Guan J (2024b) How do thinking styles and STEM attitudes have effects on computational thinking? A structural equation modeling analysis. J Res Sci Teach 61:645–673. https://doi.org/10.1002/tea.21899

Jiang H, Turnbull D, Wang X, Chugh R, Dou Y, Chen S (2022) How do mathematics interest and self-efficacy influence coding interest and self-efficacy? A structural equation modeling analysis. Int J Educ Res 115:102058. https://doi.org/10.1016/j.ijer.2022.102058

Jiang H, Wang K, Wang X, Lei X, Huang Z (2021) Understanding a STEM teacher’s emotions and professional identities: A three-year longitudinal case study. Int J STEM Educ 8:51. https://doi.org/10.1186/s40594-021-00309-9

Kelley TR, Knowles JG, Holland JD, Han J (2020) Increasing high school teachers self-efficacy for integrated STEM instruction through a collaborative community of practice. Int J STEM Educ 7:14. https://doi.org/10.1186/s40594-020-00211-w

Klassen RM, Tze VMC, Betts SM, Gordon KA (2011) Teacher efficacy research 1998–2009: Signs of progress or unfulfilled promise? Educ Psychol Rev 23(1):21–43. https://doi.org/10.1007/s10648-010-9141-8

Land M (2020) The importance of integrating the arts into STEM curriculum. In: Stewart AJ, Mueller MP, Tippins DJ (eds), Converting STEM into STEAM programs, pp. 11–19. Springer. https://doi.org/10.1007/978-3-030-25101-7_2

Lee C, Bobko P (1994) Self-efficacy beliefs: Comparison of five measures. J Appl Psychol 79(3):364–369. https://doi.org/10.1037/0021-9010.79.3.364

Li W, Chiang FK (2019) Preservice teachers’ perceptions of STEAM education and attitudes toward STEAM disciplines and careers in China. In: Sengupta P, Shanahan MC, Kim B, (eds), Critical, transdisciplinary and embodied approaches in STEM education. Springer. https://doi.org/10.1007/978-3-030-29489-2_5

Liu M, Zwart R, Bronkhorst L, Wubbels T (2022) Chinese student teachers’ beliefs and the role of teaching experiences in the development of their beliefs. Teach Teach Educ 109:103525. https://doi.org/10.1016/j.tate.2021.103525

Liu S, Xu X, Stronge J (2018) The influences of teachers’ perceptions of using student achievement data in evaluation and their self-efficacy on job satisfaction: evidence from China. Asia Pac Educ Rev 19:493–509. https://doi.org/10.1007/s12564-018-9552-7

Long T, Zhao G, Yang X, Zhao R, Chen Q (2021) Bridging the belief-action gap in a teachers’ professional learning community on teaching of thinking. Professional Dev Educ 47(5):729–744. https://doi.org/10.1080/19415257.2019.1647872

Lu L, Wang F, Ma Y, Clarke A, Collins J (2019) Exploring Chinese teachers’ commitment to being a cooperating teacher in a university-government-school initiative for rural practicum placements. In: Liu WC, Goh CCM (eds), Teachers’ perceptions, experience and learning, pp. 33–54. Routledge, London

Lyu S, Niu S, Yuan J, Zhan Z (2024) Developing professional capital through technology-enabled university-school-enterprise collaboration: an innovative model for C-STEAM preservice teacher education in the Greater Bay area. Asia Pacific J Innov Entrepreneurship. https://doi.org/10.1108/APJIE-01-2024-0014

Marco N, Palatnik A (2024) From “learning to variate” to “variate for learning”: Teachers learning through collaborative, iterative context-based mathematical problem posing. J Math Behav 73:101119. https://doi.org/10.1016/j.jmathb.2023.101119

Merriam SB (1998) Qualitative research and case study applications in education. Jossey-Bass Publishers, Hoboken, New Jersey

Morris DB, Usher EL (2011) Developing teaching self-efficacy in research institutions: A study of award-winning professors. Contemp Educ Psychol 36(3):232–245. https://doi.org/10.1016/j.cedpsych.2010.10.005

Morris DB, Usher EL, Chen JA (2017) Reconceptualizing the sources of teaching self-efficacy: A critical review of emerging literature. Educ Psychol Rev 29(4):795–833. https://doi.org/10.1007/s10648-016-9378-y

Matusovich HM, Streveler RA, Miller RL (2010) Why do students choose engineering? A qualitative, longitudinal investigation of students’ motivational values. J Eng Educ 99(4):289–303. https://doi.org/10.1002/j.2168-9830.2010.tb01064.x

Näykki P, Kontturi H, Seppänen V, Impiö N, Järvelä S (2021) Teachers as learners–a qualitative exploration of pre-service and in-service teachers’ continuous learning community OpenDigi. J Educ Teach 47(4):495–512. https://doi.org/10.1080/02607476.2021.1904777

OECD (2018) Teaching and learning international survey (TALIS) 2018 conceptual framework. OECD, Paris

Örnek T, Soylu Y (2021) A model design to be used in teaching problem posing to develop problem-posing skills. Think Skills Creativity 41:100905. https://doi.org/10.1016/j.tsc.2021.100905

Ord K, Nuttall J (2016) Bodies of knowledge: The concept of embodiment as an alternative to theory/practice debates in the preparation of teachers. Teach Teach Educ 60:355–362. https://doi.org/10.1016/j.tate.2016.05.019

Ozkan G, Umdu Topsakal U (2021) Investigating the effectiveness of STEAM education on students’ conceptual understanding of force and energy topics. Res Sci Technol Educ 39(4):441–460. https://doi.org/10.1080/02635143.2020.1769586

Park W, Cho H (2022) The interaction of history and STEM learning goals in teacher-developed curriculum materials: opportunities and challenges for STEAM education. Asia Pacific Educ Rev. https://doi.org/10.1007/s12564-022-09741-0

Perignat E, Katz-Buonincontro J (2019) STEAM in practice and research: An integrative literature review. Think Skills Creativity 31:31–43. https://doi.org/10.1016/j.tsc.2018.10.002

Philipp RA (2007) Mathematics teachers’ beliefs and affect. In: Lester Jr FK, (ed), Second handbook of research on mathematics teaching and learning, pp. 257–315. Information Age, Charlotte, NC

Quigley CF, Herro D, Jamil FM (2017) Developing a conceptual model of STEAM teaching practices. Sch Sci Math 117(1–2):1–12. https://doi.org/10.1111/ssm.12201

Ro S, Xiao S, Zhou Z (2022) Starting up STEAM in China: A case study of technology entrepreneurship for STEAM education in China. In: Ray P, Shaw R (eds), Technology entrepreneurship and sustainable development, pp. 115–136. Springer. https://doi.org/10.1007/978-981-19-2053-0_6

Roth KJ, Bintz J, Wickler NI, Hvidsten C, Taylor J, Beardsley PM, Wilson CD (2017) Design principles for effective video-based professional development. Int J STEM Educ 4:31. https://doi.org/10.1186/s40594-017-0091-2

Article   PubMed   PubMed Central   Google Scholar  

Ryu M, Mentzer N, Knobloch N (2019) Preservice teachers’ experiences of STEM integration: Challenges and implications for integrated STEM teacher preparation. Int J Technol Des Educ, 29:493–512. https://doi.org/10.1007/s10798-018-9440-9

Sancar R, Atal D, Deryakulu D (2021) A new framework for teachers’ professional development. Teach Teach Educ 101:103305. https://doi.org/10.1016/j.tate.2021.103305

Sanz-Camarero R, Ortiz-Revilla J, Greca IM (2023) The place of the arts within integrated education. Arts Educ Policy Rev. https://doi.org/10.1080/10632913.2023.2260917

Shirani F, Henwood K (2011) Continuity and change in a qualitative longitudinal study of fatherhood: relevance without responsibility. Int J Soc Res Methodol 14(1):17–29. https://doi.org/10.1080/13645571003690876

Smith CE, Paré JN (2016) Exploring Klein bottles through pottery: A STEAM investigation. Math Teach 110(3):208–214. https://doi.org/10.5951/mathteacher.110.3.0208

Soprano K, Yang L (2013) Inquiring into my science teaching through action research: A case study on one pre-service teacher’s inquiry-based science teaching and self-efficacy. Int J Sci Math Educ 11(6):1351–1368. https://doi.org/10.1007/s10763-012-9380-x

Stake RE (1995) The art of case study research. Sage Publication, Thousand Oaks, California

Stohlmann M, Moore T, Roehrig G (2012) Considerations for teaching integrated STEM education. J Pre Coll Eng Educ Res 2(1):28–34. https://doi.org/10.5703/1288284314653

Strauss AL, Corbin JM (1990) Basics of qualitative research. Sage Publications, Thousand Oaks, California

Taimalu M, Luik P (2019) The impact of beliefs and knowledge on the integration of technology among teacher educators: A path analysis. Teach Teach Educ 79:101–110. https://doi.org/10.1016/j.tate.2018.12.012

Takahashi A, McDougal T (2016) Collaborative lesson research: Maximizing the impact of lesson study. ZDM Math Educ 48:513–526. https://doi.org/10.1007/s11858-015-0752-x

Thompson AG (1992) Teachers’ beliefs and conceptions: A synthesis of the research. In: Grouws DA (ed), Handbook of research on mathematics teaching and learning, pp. 127–146. Macmillan, New York

Tschannen-Moran M, Woolfolk Hoy A, Hoy WK (1998) Teacher efficacy: Its meaning and measure. Rev Educ Res 68:202–248. https://doi.org/10.3102/00346543068002202

Unfried A, Rachmatullah A, Alexander A, Wiebe E (2022) An alternative to STEBI-A: validation of the T-STEM science scale. Int J STEM Educ 9:24. https://doi.org/10.1186/s40594-022-00339-x

Uştu H, Saito T, Mentiş Taş A (2021) Integration of art into STEM education at primary schools: an action research study with primary school teachers. Syst Pract Action Res. https://doi.org/10.1007/s11213-021-09570-z

Vaziri H, Bradburn NM (2021) Flourishing effects of integrating the arts and humanities in STEM Education: A review of past studies and an agenda for future research. In: Tay L, Pawelski JO (eds), The Oxford handbook of the positive humanities, pp. 71–82. Oxford University Press, Oxford

Wang K, Wang X, Li Y, Rugh MS (2018) A framework for integrating the history of mathematics into teaching in Shanghai. Educ Stud Math 98:135–155. https://doi.org/10.1007/s10649-018-9811-x

Wang Z, Jiang H, Jin W, Jiang J, Liu J, Guan J, Liu Y, Bin E (2024) Examining the antecedents of novice stem teachers’ job satisfaction: The roles of personality traits, perceived social support, and work engagement. Behav Sci 14(3):214. https://doi.org/10.3390/bs14030214

Wenger E, McDermott R, Snyder WM (2002) Cultivating communities of practice. Harvard Business School Press, Boston, MA

Wong JT, Bui NN, Fields DT, Hughes BS (2022) A learning experience design approach to online professional development for teaching science through the arts: Evaluation of teacher content knowledge, self-efficacy and STEAM perceptions. J Sci Teacher Educ. https://doi.org/10.1080/1046560X.2022.2112552

Wyatt M (2015) Using qualitative research methods to assess the degree of fit between teachers’ reported self-efficacy beliefs and their practical knowledge during teacher education. Aust J Teach Educ 40(1):117–145

Xenofontos C, Andrews P (2020) The discursive construction of mathematics teacher self-efficacy. Educ Stud Math 105(2):261–283. https://doi.org/10.1007/s10649-020-09990-z

Yin R (2003) Case study research: Design and methods. Sage Publications, Thousand Oaks, California

Zakariya YF (2020) Effects of school climate and teacher self-efficacy on job satisfaction of mostly STEM teachers: a structural multigroup invariance approach. Int J STEM Educ 7:10. https://doi.org/10.1186/s40594-020-00209-4

Zee M, de Jong PF, Koomen HM (2017) From externalizing student behavior to student-specific teacher self-efficacy: The role of teacher-perceived conflict and closeness in the student–teacher relationship. Contemp Educ Psychol 51:37–50. https://doi.org/10.1016/j.cedpsych.2017.06.009

Zee M, Koomen HM (2016) Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being: A synthesis of 40 years of research. Rev Educ Res 86(4):981–1015. https://doi.org/10.3102/0034654315626801

Zhan Z, Li Y, Mei H, Lyu S (2023) Key competencies acquired from STEM education: gender-differentiated parental expectations. Humanit Soc Sci Commun 10:464. https://doi.org/10.1057/s41599-023-01946-x

Zhan Z, Niu S (2023) Subject integration and theme evolution of STEM education in K-12 and higher education research. Humanit Soc Sci Commun 10:781. https://doi.org/10.1057/s41599-023-02303-8

Zhong B, Liu X, Li X (2024) Effects of reverse engineering pedagogy on students’ learning performance in STEM education: The bridge-design project as an example. Heliyon 10(2):e24278. https://doi.org/10.1016/j.heliyon.2024.e24278

Zhong B, Liu X, Zhan Z, Ke Q, Wang F (2022) What should a Chinese top-level design in STEM Education look like? Humanit Soc Sci Commun 9:261. https://doi.org/10.1057/s41599-022-01279-1

Zhou B (2021) Cultivation of Ed. M. to bring up “famous subject teachers”: practical exploration and policy suggestions. Teach Educ Res 33(5):19–26. https://doi.org/10.13445/j.cnki.t.e.r.2021.05.003

Zhou X, Shu L, Xu Z, Padrón Y (2023) The effect of professional development on in-service STEM teachers’ self-efficacy: a meta-analysis of experimental studies. Int J STEM Educ 10:37. https://doi.org/10.1186/s40594-023-00422-x

Download references

Acknowledgements

This research is funded by 2021 National Natural Science Foundation of China (Grant No.62177042), 2024 Zhejiang Provincial Natural Science Foundation of China (Grant No. Y24F020039), and 2024 Zhejiang Educational Science Planning Project (Grant No. 2024SCG247).

Author information

Xuesong Zhai

Present address: School of Education, City University of Macau, Macau, China

Authors and Affiliations

College of Education, Zhejiang University, Hangzhou, China

Haozhe Jiang & Xuesong Zhai

School of Engineering and Technology, CML‑NET & CREATE Research Centres, Central Queensland University, North Rockhampton, QLD, Australia

Ritesh Chugh

Hangzhou International Urbanology Research Center & Zhejiang Urban Governance Studies Center, Hangzhou, China

Department of Teacher Education, Nicholls State University, Thibodaux, LA, USA

School of Mathematical Sciences, East China Normal University, Shanghai, China

Xiaoqin Wang

College of Teacher Education, Faculty of Education, East China Normal University, Shanghai, China

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization - Haozhe Jiang; methodology - Haozhe Jiang; software - Xuesong Zhai; formal analysis - Haozhe Jiang & Ke Wang; investigation - Haozhe Jiang; resources - Haozhe Jiang, Xuesong Zhai & Xiaoqin Wang; data curation - Haozhe Jiang & Ke Wang; writing—original draft preparation - Haozhe Jiang & Ritesh Chugh; writing—review and editing - Ritesh Chugh & Ke Wang; visualization - Haozhe Jiang, Ke Wang & Xiaoqin Wang; supervision - Xuesong Zhai & Xiaoqin Wang; project administration - Xuesong Zhai & Xiaoqin Wang; and funding acquisition - Xuesong Zhai & Xiaoqin Wang. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Xuesong Zhai .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This research was approved by the Committee for Human Research of East China Normal University (Number: HR 347-2022). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Informed consent

Written informed consent was obtained from all participants in this study before data collection.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jiang, H., Chugh, R., Zhai, X. et al. Longitudinal analysis of teacher self-efficacy evolution during a STEAM professional development program: a qualitative case study. Humanit Soc Sci Commun 11 , 1162 (2024). https://doi.org/10.1057/s41599-024-03655-5

Download citation

Received : 27 April 2024

Accepted : 12 August 2024

Published : 08 September 2024

DOI : https://doi.org/10.1057/s41599-024-03655-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Journals By Subject

• Proceedings

Information

Qualitative Approaches in Mathematics Education Research: Challenges and Possible Solutions

Sashi Sharma

Department of Mathematics, Science and Technology Education, Faculty of Education,The university of Waikato, Hamilton, New Zealand

Contributor Roles: Sashi Sharma is the sole author. The author read and approved the final manuscript.

Add to Mendeley

Despite being relatively new in mathematics education research, qualitative researchapproaches need special attention as attempts are being made to enhance the credibility and trustworthiness of this approach. It is important that researchers are aware of the limitations associated with these methods so that measures are put in place to try and minimize the effects of these limitations Philosophical roots and key features of this paradigm are outlined. Qualitative methods such as the interview approach in research literature as a data gathering tool are considered next. Challenges faced by qualitative researchers in terms of reliability, validity and generability are considered. Examples are provided to illustrate methodological problems and solutions related to qualitative methods.

Research Methods, Qualitative Research, Data Collection, Quality Criteria, Limitations, Possible Solutions

Sashi Sharma. (2013). Qualitative Approaches in Mathematics Education Research: Challenges and Possible Solutions. Education Journal , 2 (2), 50-57. https://doi.org/10.11648/j.edu.20130202.14

Sashi Sharma. Qualitative Approaches in Mathematics Education Research: Challenges and Possible Solutions. Educ. J. 2013 , 2 (2), 50-57. doi: 10.11648/j.edu.20130202.14

Sashi Sharma. Qualitative Approaches in Mathematics Education Research: Challenges and Possible Solutions. Educ J . 2013;2(2):50-57. doi: 10.11648/j.edu.20130202.14

Cite This Article

• Author Information

Verification Code/

The captcha is required.

Captcha is not valid.

Verification Code

Science Publishing Group (SciencePG) is an Open Access publisher, with more than 300 online, peer-reviewed journals covering a wide range of academic disciplines.

Learn More About SciencePG

• Special Issues
• AcademicEvents
• ScholarProfiles
• For Authors
• For Reviewers
• For Editors
• For Conference Organizers
• For Librarians
• Article Processing Charges
• Special Issues Guidelines
• Editorial Process
• Peer Review at SciencePG
• Open Access
• Ethical Guidelines

Important Link

• Manuscript Submission
• Propose a Special Issue
• Join the Editorial Board
• Become a Reviewer

Gender equality in science, technology, engineering and mathematics: industrial vis-a-vis academic perspective

• Open access
• Published: 02 January 2024
• Volume 3 , article number  3 , ( 2024 )

Cite this article

You have full access to this open access article

• Antigoni Parmaxi   ORCID: orcid.org/0000-0002-0687-0176 1 ,
• Eirini Christou   ORCID: orcid.org/0000-0001-6928-1013 1 ,
• Julia Fernández Valdés 2 ,
• Dalia María Puente Hevia 2 ,
• Maria Perifanou 3 ,
• Anastasios A. Economides 3 ,
• Jelena Mazaj   ORCID: orcid.org/0000-0001-6862-8767 4 &
• Maryna Manchenko   ORCID: orcid.org/0000-0003-3056-7609 4  

3006 Accesses

1 Altmetric

Explore all metrics

The aim of this study is to present the findings of a qualitative study aiming at capturing key stakeholders’ perceptions with regard to: (a) gender equality in academia and the workplace; (b) challenges, needs, and experiences in academia and workplace with regard to gender. This research captures the current situation of gender equality in the fields of Science, Technology, Engineering and Mathematics (STEM) and provides a deep understanding of the needs, challenges and experiences both men and women encounter in academia vis-a-vis the industry. Forty-one interviews were conducted in Cyprus, Greece, Italy, Slovenia, and Spain. Data collected demonstrate a variety of challenges faced by all genders in the workplace and in academia, as well as the need for more concrete actions that will allow for a gender-balanced perspective to be heard in the STEM fields. Implications for practitioners, policymakers and researchers are also provided.

Similar content being viewed by others

Gender, STEM Studies and Educational Choices. Insights from Feminist Perspectives

The Scientific Culture in Electrical Engineering: An Insider’s Reflection

Women in male-dominated technology study programmes – findings of a survey conducted at the kiel university of applied sciences.

Avoid common mistakes on your manuscript.

1 Introduction

Despite the digitalisation benefit, low participation of women in the labour force persists [ 1 , 2 ]. The main obstacles are gender bias and socio-cultural constructs, which at different life stages dissuade girls and women from taking up Science, Technology, Engineering and Mathematics (STEM) studies and careers. It is essential to integrate awareness of gender bias across all relevant sectors including in the initial and continuous training of teachers; address structural barriers such as work conditions and culture, which hinder girls and women from entering a predominantly male-dominated field; and increase the visibility of role models who are insufficiently valued, aiming to inspire women and girls [ 3 ].

The present study uses inclusive definitions of “woman” and “female” that also include trans, genderqueer, non-binary and intersex people. The use of “women” and “female” as umbrella terms is not designed to unify the unique experiences of individuals, but to reflect on a collective experience of difference defined by gender and sexuality. When citing other scholars, the article adopts the terms used in their work [ 4 , 5 , 6 ].

Ceci and Williams [ 7 ] provide an overall view of the main empirical evidence that currently exists on gender bias in science. An indicative example provided in their book demonstrates one study in which the same curriculum was signed by a man, a woman, or with initials. The curriculum signed with a male name always received the highest scores, while the one signed with a female name consistently received the lowest marks. When addressing gender inequality, things seem to worsen when women become mothers. For women of equal merit, mothers are perceived as less competent and committed than women without children, whilst men are not only not penalized by their parenthood but rather on the contrary, it has been proven to be a factor that works in their favor in some occasions. The study shows that employers discriminate against mothers and favour fathers [ 8 ]. When analysing gender biased situations within the academic world, many factors have to be considered, as these influence the different situations as well as the many levels at which the analysis would be operating. As stated by Sánchez de Madariaga, de la Rica, and Dolado [ 9 ] we should take into account different aspects such as: gender differences in diverse subject areas at 15–16 years of age, differences in university education, the transition from bachelor degree/ master’s degree to doctorate, the transition from doctorate to post-doctorate and the public grants for post-graduate and postdoctoral study.

Reducing the gender gap in STEM education areas may lower labour market bottlenecks, increase women's employment and productivity, and reduce occupational segregation. Getting more women into STEM education will have a positive impact on economic growth and employment in the European Union (EU) [ 10 ]. By 2050, total EU employment would increase by 850 000 to 1 200 000. Women will become more productive as a result of higher rates of STEM qualifications, contributing to the smart growth envisioned in the Europe 2020 strategy. Likewise, the increased employment of women in STEM fields is also expected to benefit the EU economy's competitiveness [ 10 ].

1.1 Rationale

The acquisition of data about women’s reality in HE and the industry is key to boosting our understanding of the status quo and to promoting equality politics that are effective for all genders in any field. Additionally, by identifying key themes and patterns that emerge across the different participants and organizations, we aim to provide a more comprehensive view of the issue of gender inequality in STEM fields, which adds significant new insights to the existing literature.

1.2 Objectives

This paper aims at capturing the current situation in gender equality in STEM by taking a snapshot on the way women and men experience and ascribe meaning to it in the field of STEM in HE and the industry in five EU countries: Cyprus, Greece, Italy, Slovenia, and Spain. Through a qualitative methodology, this study brought together both men and women from HE and the industry to voice their views regarding gender equality in STEM, barriers that they encounter as well as how they overcome them. The research questions guiding this study are:

How do experiences of gender inequality in STEM differ between HE and industry?

What are the specific challenges faced by men and women in the fields of STEM in achieving gender equality?

In what ways do national policies can alleviate gender inequalities in the STEM fields in HE and industry?

The paper is structured as follows: in the next section we provide an overview of the state-of-the art of gender equality in the STEM industry and HE and the methodology adopted follows. The article then provides the findings extracted and concludes by connecting the empirical findings to the existing literature.

2 Literature review

2.1 women in the stem industry.

Gender equality in industry is an important issue that has economic and social implications. Women are overrepresented in some industries and underrepresented in others [ 11 ]. Research has highlighted the persistent gender inequality in STEM fields, particularly, where women continue to be underrepresented in leadership positions and technical roles (e.g., Charlesworth & Banaji [ 12 ]; Wang & Degol [ 13 ]; UNESCO [ 14 ]; Women in Digital Scoreboard, 2019 [ 15 , 16 , 17 , 18 ]). Even though the number of women enrolled in STEM fields at universities has increased, there are still not enough job opportunities for women in this field, which adds to the general decline in women's employment [ 19 ]. Studies have shown that gender biases in recruitment, performance evaluations, and networking continue to be major barriers for women in STEM fields in the Industry (e.g., Moss-Racusin, et al. [ 20 ]). There is also a significant number of women that quit from technological job positions [ 21 ]. This happens due to parenthood [ 22 , 23 ], unfriendly work environments and microaggressions [ 24 , 25 ] as well as due to dissatisfaction with management and a desire for greater advancement opportunities [ 26 ]. According to the study of Friedmann [ 27 ], the most significant factors influencing women's career decisions in STEM were salary and the capacity to balance work and family responsibilities. It is recommended that women’s entry into the STEM fields be facilitated by concentrating on these aspects. Additionally, research has highlighted the important role of organizational culture, work-life balance policies, and mentorship programs in shaping gender inequalities in STEM fields in the Private Industry (e.g., Moser and Branscombe [ 28 ]; Christou and Parmaxi [ 29 ]; Christou, et al. [ 30 ]). Recent studies have also shown that intersectional factors such as race, ethnicity, and sexual orientation, interact with gender to further exacerbate the inequalities faced by women in STEM fields in Private Industry (e.g., Alfred, et al. [ 31 ]). Women that work in STEM and technological fields face several barriers that prevent them from starting or progressing in their professional career [ 32 ]. These barriers include gender bias and stereotypes [ 26 , 32 , 33 , 34 ], sexual harassment [ 16 , 33 ] and lack of personal life and work balance [ 21 , 32 , 33 , 34 ], as well as the insufficient opportunities for employment for women in the STEM fields and the socio-cultural obstacles hindering their career advancement [ 19 ].

2.2 Women in STEM in HE

Recent research has highlighted the persistent gender inequality in STEM fields, particularly in HE, where women continue to be underrepresented in science and engineering disciplines (e.g. [ 35 ]). Even though the number of women enrolled in STEM fields at universities is now higher [ 19 ], s.tudies have shown that gender biases are prevalent in academia specifically in recruitment, promotion, and pay. While more women are obtaining postgraduate degrees now than in the past, the proportion of women holding STEM faculty positions has not changed significantly. Negative and widespread gender stereotypes could be a contributing factor in the lack of progress toward gender parity, as they can encourage discrimination in hiring practices and limit women's career advancement opportunities [ 36 ]. A recent review demonstrates the issues that women encounter as members of faculty, such as gendered teaching loads, tenure and promotion, work–family balance, departmental policies and diverse hiring practices [ 37 ]. The same review also notes improvement in achieving equality in the status of women in STEM in HE, yet more work is needed for the situation to be considered significantly improved and stable.

Additionally, research has highlighted the important role of implicit biases, gender stereotypes, and lack of role models in shaping gender inequalities in STEM fields (e.g. [ 38 , 39 , 40 , 41 ]). Recent studies have also shown that intersectional factors such as race, ethnicity, and social class, interact with gender to further exacerbate the inequalities faced by women in STEM fields (e.g. Kricorian, et al. [ 40 ]). Organizational change interventions that focus on recruiting diverse applicants (e.g., training search committees), mentoring, networking, and professional development (e.g., promoting women faculty networks), and improving academic climate (e.g., educating male faculty on gender bias) are potential solutions for these issues [ 36 ]. Overall, these findings suggest that there is a need for targeted interventions that address the systemic barriers faced by women in STEM fields, particularly in HE.

2.3 Intersectionality

Crenshaw [ 42 ] coined the term ‘‘intersectionality’’ to describe how Black women experience dual oppression based on both their race and gender, which has historically resulted in their marginalization in feminist movements and social justice efforts. Intersectionality is an approach that recognizes that individuals are not defined by just one identity category, but rather, are shaped by multiple intersecting identities such as race, ethnicity, gender, sexuality, class, and ability. This means that experiences of oppression and privilege are not additive, but rather, are interconnected and mutually reinforcing. Crenshaw [ 43 ] emphasized that intersectionality is not merely a theory of multiple identities, but that some identities can make individuals more vulnerable to discrimination, exclusion, marginalization, and invisibility. Intersectional feminism is about giving voice to people who experience both concurrent and intersecting forms of oppression. The intention is to learn more about the complex relationships between disparities within a particular setting and the intricacies of inequalities themselves [ 42 , 44 ].

2.4 Intersectionality and STEM

In the context of STEM fields, intersectionality can help to illuminate the complex and often overlapping barriers that women and underrepresented groups face in these fields. For example, research has shown that women experience a unique set of challenges in STEM fields, including racial discrimination, gender bias, and stereotype threat [ 45 , 46 , 47 ]. According to the study of Smith et al. [ 48 ], black women in STEM fields may face discrimination and exclusion due to the intersection of their race and gender. A review of racialized experiences in STEM entrepreneurship has underscored the significance of intersectionality by pointing out the underrepresentation of racially minoritized populations in STEM fields, entrepreneurship, and innovation [ 49 ]. By taking an intersectional approach, researchers can more fully understand how these different forms of oppression intersect and compound to shape experiences of inequality and exclusion.

3 Methodology

3.1 research design.

To capture a thorough understanding of participants’ views of the role of women in the fields of STEM in HE and the industry, a qualitative methodology was adopted. To analyze the data in our study on gender equality in STEM fields, we draw on an intersectional feminist framework that highlights the ways in which gender intersects with other forms of social stratification. The intersectional feminist framework guided the data analysis by providing a lens through which to examine the experiences of individuals in STEM fields. The intersectional feminist framework acknowledges that individuals are not defined by a single identity (e.g., gender) but are shaped by the intersections of various identities (e.g., gender, race, class) (Crenshaw [ 42 ]). In our study, we recognized the participants' multifaceted identities and how these intersected to influence their experiences in STEM. The framework helped us identify power dynamics within the experiences shared by participants. By considering how various identities intersect, we could discern patterns of privilege and disadvantage, shedding light on the structural inequalities present in both higher education and industry settings. Moreover, by acknowledging the connections between different types of discrimination, we were able to make more focused and inclusive policy recommendations that cater to the unique needs of people with a variety of intersectional identities. This approach is informed by numerous studies on gender inequality in STEM fields that have used an intersectional feminist lens, including work on the experiences of black immigrant women in STEM [ 50 ] and the impact of intersectionality on STEM career choices [ 51 ].

3.2 Sampling

Forty one (41) interviews were conducted, as the literature indicates anywhere from 5 to 50 participants as adequate for qualitative studies [ 52 , 53 , 54 ]. The interviews involved all genders in senior and junior positions in the fields of STEM industry as well as students and academics in the fields of STEM in HE. More specifically we involved 13 participants from the industry and 28 participants from HE from five different EU countries: Cyprus, Greece, Italy, Slovenia, and Spain. The aim was to capture a wide range of experiences and to represent both senior and junior participants from the fields of STEM.

A convenience sampling was employed. Participants were recruited through the research team’s professional contacts with key stakeholders in these fields. Participants’ age varied (18–53) and career stage varied. The inclusion criteria were the participants to belong in the fields of STEM, either in HE or in the industry. With regards to the industry, we aimed at interviewing stakeholders with more than 3 years of experience. A breakdown of the participants appears in Table  1 .

The interview protocol employed in this study was carefully developed, drawing directly from the insights gained from the literature review on gender inequality in STEM. The literature review offered a thorough comprehension of the body of research, major themes, and obstacles encountered by individuals in academic and industrial settings. Acknowledging the significance of capturing nuanced viewpoints, the review also emphasized the intersectionality of gender experiences. This plethora of information directly influenced the development of the interview questions and prompts, ensuring that our investigation was purposefully in line with the gaps in the literature. Therefore, an interview protocol was designed focusing on the following thematic areas: (a) experiences and needs at workplace/studies with regard to gender; (b) perceptions of equality/inequality; (c) challenges encountered at workplace/studies with regard to gender; (d) expectations and practices and (e) recommendations/suggestions for overcoming challenges at workplace/studies with regard to gender.

3.4 Data collection

The interviews were conducted in five different EU countries—Cyprus, Greece, Italy, Slovenia and Spain. Interview facilitators informed the participants on the aim of the interview and sought written consent. Each interview lasted approximately 15–25 min. The facilitators followed the interview protocols with the thematic areas mentioned above.

3.5 Data analysis

The interviews’ transcriptions (henceforth the interview dataset) were then imported in the qualitative software Nvivo 12 (Nvivo, 2012) for organizing, analyzing and visualizing qualitative data [ 55 ]. Data was then coded and categorized according to the participants’ sayings. Thematic analysis was undertaken in order to generate key themes and patterns related to the areas mentioned earlier. Thematic analysis was undertaken as we aimed at interpreting “meanings and perceptions created and shared during a conversation” [ 56 ]. The analysis involved an iterative process which encompassed reading and rereading of the interview dataset and generation of initial codes that were then refined further upon further coding and discussion among the research team. The analysis followed six steps (see Fig.  1 ): familiarisation with the data; code generation; classification of codes in categories; reviewing of categories and subcategories; definition of themes and production of report [ 57 ].

Six-step followed for the analysis of the interview dataset

3.6 Ethical considerations

All participants were informed about the aim and context of the research study verbally and in writing. They were also informed on their right to withdraw at any time. Each participant signed a consent form. The principles of confidentiality, anonymity and personal data were also applied.

The dataset revealed similarities as well as differences in the various areas explored. Overall, participants expressed similar views regarding gender equality elaborating on equal opportunities and challenges in HE. On the other hand, participants from the industry had a different view of gender equality at the workplace. In the following sections, we present the analysis of the data classified in the five areas mentioned earlier:

(a) experiences and needs at workplace/studies with regards to gender; (b) perceptions of equality/inequality; (c) challenges encountered at workplace/studies with regard to gender; (d) expectations and practices and (e) recommendations/ suggestions for overcoming challenges at workplace/studies with regard to gender.

4.1 Experiences and needs with regard to gender in HE vis-a-vis the industry

Participants in both HE and the Industry recognized the lack of women in the fields of STEM and often characterized the field as men-driven.

HE participants’ experiences concerned pay gap, receiving sexist comments or hostile behavior due to gender, struggling with work-life balance, being downgraded because of being a woman, suffering from the scissors effect, i.e. while the participation of men increases, that of women decreases as the academic levels move towards the top ranks [ 58 ]. Moreover, participants also voiced the need for women in academia to work harder than men in order to reach recognition and the tendency to allocate unimportant or administrative tasks to women. “ Generally, they have to demonstrate their capacity and skills for the position in a way in which men don’t need to ” (Male, Spain, HE student). Inequalities were particularly voiced in terms of maternity, promotion and reaching the upper-ranks of the academic ladder. With regard to maternity, participants in HE voiced the need to take maternity leave into consideration when dealing with evaluation and meeting specific Key Performance Indicators (KPIs). Regarding promotion and participation of women in the upper ranks of the academic ladder, participants noted improvement in the last decades, yet ‘double standards’ still exist. As noted by one participant, “ it is not officially forbidden for a woman to be in a higher position, but there is a groundbreaking event, when a woman has children, then falls out of the competition for prestigious positions because it is harder to meet the criteria of scientific excellence which are written only for persons who have time just for a career, not necessarily just for men, it’s also a matter of social status, not just sexual. Those who are socially privileged, regardless of gender, reach the highest positions ” (Female, Slovenia, Researcher). Another participant also voiced “ I don’t know any company with women in the higher positions, to be honest ” (Male, Spain, HE student). It is worth noting though, that there were also participants noting less discriminative behavior and no or minimum differentiated behavior due to gender “ If someone is ambitious and works in this direction to have a higher position and is committed to it, that position can occur regardless of gender. ” (Male, Slovenia, HE lecturer).

On the same line, participants from the industry noted discrimination prominently-either at the position they hold now or in previous employment. The following experiences were highlighted by participants from the industry: sexual harassment, sexist jokes, gender segregation (i.e. the idea that jobs in STEM are overwhelmingly done by men), the need for women to prove themselves, dealing with misogynist employers, lack of women in upper ranks positions, exclusion of women from conversations and limiting women to administrative/unimportant tasks because of their gender.

4.2 Perceptions of equality/inequality in HE vis-a-vis the industry

When coming to perceptions of equality/inequality, participants in HE noted inequality as an issue that emerges specifically in terms of workload as they highlighted that women tend to work harder in order to move higher in the academic hierarchy. Moreover, one of the participants voiced the gender equality paradox. The gender-equality paradox, suggests that countries with a higher level of gender equality (e.g., Scandinavian countries) tend to have less gender balance in fields such as STEM, than less equal countries [ 59 ]. Despite the increasing number of women in STEM, women still indicate that they are excluded from opportunities and being discriminated against. As voiced by one participant: “ Gender bias definitely exists […] men are always the first ones to be invited to speak at more prestigious conferences ” (Female, Researcher, Italy). However, there seems to be an optimistic tendency indicating change of this discriminatory behaviour by women by advocating equality in terms of gender in academic conferences and between generations “ I think gender bias is no longer a thing, at least when speaking with my generation […] I think in the recent years slowly the STEM industry increases the numbers of women but there are still more males. I do know some enterprises with women in leading positions ” (Male, Cyprus, HE student). When it comes to celebrations (e.g. International Women's Day, International Day of Women and Girls in Science), participants noted that these types of events are important as women’s achievements gain visibility and recognition, providing role models of prominent women; however, these events need to be linked with action: “ these international days are good for a start, to trigger discussions and remind some things to the people, but after that, we need something aligned deeper with everyday life aspects ” (Female, HE student, Greece).

With regard to perceptions of equality/inequality in the industry, participants noted that STEM is a male dominated field and often cultural norms drive inequality, as the field was male dominated from school years until university. Lack of trust, confidence and role models were also noted by participants as an impeder for women in leadership roles. As voiced by one participant: “ I never heard of any enterprises with women in leading positions. Sometimes women can be associate members or something, but not leaders or Chief Executive Officers (CEOs) unfortunately. I think women underestimate themselves quite a lot as well. […] My male colleagues take more risks than me—we all studied together, but they have already opened their own firms and I haven’t even thought about doing it on my own ” (Female, Junior professional, Italy).

4.3 Challenges encountered at HE and industry with regard to gender

Both participants from academia and industry highlighted discrimination, stereotypes, the glass ceiling and lack of role models as the key challenges in the fields of STEM. Participants from the HE highlighted that gender has greatly affected their career path and employment potential. STEM is often characterized as a “ male-dominated, change resistant ” (Female, HE instructor, Greece) domain with few female role-models which is reported to have a great influence on women’s recognition. Lack of trust and confidence were voiced as inhibitors of women’s successes, as noted by one HE instructor “ the STEM world is seen as difficult, with only male references, so it feels like if you are not Marie Curie you don't make it ” (Female, HE instructor, Spain). Participants in HE also highlighted career instability for women which has a disproportionate effect on them, as well as suffering subtle and overt discrimination and sexism which leaves many women feeling unwelcome, undermining their value and recognition. Work-life balance, glass-ceiling and impostor syndrome were highlighted as key issues for women in academia. When it comes to technical skills, all participants agreed that both men and women have the same capabilities, yet it was highlighted that especially in the field of STEM technical experimentation takes time (needs trial and error) and “ the lack of time that comes with starting a family falls on women. So maybe men have a stronger advantage in technical skills because they have more time to experiment and become better ” (Female, Cyprus, HE instructor). To this end, stable funding is often sought so that women overcome the specific precariousness.

Reconciliation of work and family obligations appeared to be a demanding issue in the industry, especially because “ STEM industry demands long working hours and forces many women to choose between a successful career and a family ” (Female, Greece, HE instructor). Women are also often excluded from decision making bodies and need to work harder than men to demonstrate their value.

4.4 Expectations and practices: women in STEM in HE vis-a-vis the Industry

Unequal expectations were highlighted as a demanding issue that women in HE need to deal with. Women often deal with uncertainty and distrust, and need to work harder to prove themselves and promote their achievements in a prominent way in order to gain attention, validation and recognition. With respect to practices, participants in HE highlighted the importance of bringing more women in the STEM fields, and setting forward equity and equality strategies: “ I have experienced differentiated attitudes/behaviour towards women. For instance, in the past few months I was away from my work due to maternity leave. My absence of 4 months, but also the last 1–2 months before leaving for maternity, did not allow me to perform at the same levels as before getting pregnant. … [However], the KPIs remained the same. This is an experience that a man does not have. Everyone is being assessed with the same criteria, with the same numbers (KPIs) at the end of the year. And it is not the same when being a mother vs being a father ” (Female, Cyprus, HE researcher).

When it comes to the industry, participants noted the expectations that women encounter beyond their work environments, as well as the cultural norm of a woman being excellent in handling family but average in work-related tasks. Gender segregation as a prominent expectation in the STEM industry -that is women are good in keeping notes, cleaning etc. Participants also noted that the lack of trust and confidence is often lagging women behind the upper ranks of the ladder.

4.5 Recommendations and suggestions for overcoming challenges with regard to gender in HE

Participants voiced the need for action in order to tackle the aforementioned challenges. The recommendations provided involve both high level changes such as adopting a systemic approach “ from kindergarten onwards to the promotion of women [in jobs and professional community] ” (Female, Slovenia, HE instructor) and low-level changes in employers’ mentality. Almost all participants noted that both society, European and governmental bodies should focus on educational capabilities to tackle gender bias by overturning the dominant gender-related stereotypes through actively informing parents and students and promoting equality in STEM from a very young age “ they have to give more visibility to women in science since the school years. With this, the basis of the problem would be solved ” (Male, Spain, HE student). Education has a crucial role in this endeavour, as schools need to promote further STEM education (in school and outside of school) “ giving equal opportunities to men and women to participate, so they would gain more knowledge regarding STEM and decide whether it fits them or not ” (Female, Cyprus, HE student). Overcoming gender-related stereotypes is also a challenge that the education system needs to address, primarily by training teachers to put forward a culture of equality. Incentives are also needed in order to keep women active in STEM in HE. These incentives could include encouragement of women students in STEM competitions, and gender quotas in research proposals. Since STEM education does not attract or does not retain the interest of many women, a rewarding plan could be adopted by the EU for benefiting organizations which have a balanced number of employees in STEM jobs. Supporting women to reconcile career and family is also considered. More specifically, participants suggest longer maternity leave for women without any pay cut, as well as economic support to women who return from maternity and financial incentives for incorporating family-friendly practices. Gender action plans along with the monitoring of gender equality practices are also recommended. Such practices take place in Scandinavian countries, for example the Research Council of Norway has actively sought a central role in the advancement of gender equality in Norwegian research in STEM. According to the Norwegian Gender Equality Act, all employers, both private and public, are obliged to promote gender equality and to prevent discrimination through active, targeted, planned work. The Ministry of Education and Research requires that all universities and university colleges adopt a gender action plan to fulfill these legal duties. Participants also noted that raising awareness through public events and promoting or taking compulsory measures for supporting diverse hiring.

4.6 Recommendations and suggestions for overcoming challenges with regard to gender in the industry

Participants from the industry focused on the need to put forward gender equality as an aspect in the educational system and the need to optimise existing policies and establish new ones for supporting women in STEM. Pertaining to existing policies, participants highlighted the need to incorporate work-from-home as an established practice for working mothers as well as supporting family-friendly companies. With regard to new policies, participants noted the need for an innovative ecosystem to grow and new employment opportunities for women as well as further support of women start-ups in STEM. This is also in-line with IGNITE (2014) who noted that women’s business ideas receive less start-up investment although they adopt creative and innovative approaches (Table 2 ).

5 Discussion

Despite the strong efforts and the promotion of social and political measures to establish gender equality, women in STEM both in HE and the industry still experience inequality. The intersectional framework adopted in this study provided a powerful tool for understanding the complex and interconnected factors that shape experiences of inequality and exclusion in STEM fields. Through this lens, the research community can more fully capture the experiences of diverse groups and develop more effective strategies for promoting gender equality in these fields. The participants of the study referred to common barriers that especially women face in STEM fields, in both HE and the industry. The barriers include sexual harassment, bias, stereotypes, discrimination, need to prove ones self and exclusion from decision making. While women in academia face obstacles related to maternity, promotions, and reaching upper ranks, industry participants focused on discrimination, sexual harassment, and gender segregation, with women frequently facing barriers to leadership positions. Notably, the gender equality paradox surfaced, indicating that even in countries with high gender equality, disparities persist in STEM fields. These barriers are very common in the STEM field and are ascertained by previous research [ 26 , 32 , 33 , 34 ]. Our findings are also aligned with the attitudes and the overall statistical picture of the specific countries. In the survey of the European Commision about European citizens’ knowledge and attitudes towards science and technology that was published in September 2021, respondents from Cyprus (47%), Greece (45%) and Slovenia (38%) were least likely to agree with the statement “Science and technology pay sufficient attention to differences between women’s and men’s needs”. Spain (53%) and Italy (51%) had the majority of the participants agree to that statement [ 60 ].

Male respondents from the academia were more likely to take an optimistic stance or believe that gender equality may not be a major issue. This tendency could be attributed to differences in the perspectives or experiences that men and women have had. It is important to note that a variety of factors could contribute to the variation in respondents’ perceptions, such as institutional structures, systemic biases, and societal conditioning. An interesting pattern of support and recognition of the difficulties women may encounter in academia in comparison to men is evident in the views of male respondents in the context of gender dynamics in STEM.

In HE, women recognize a paradox of gender equality and the need for more integration of women in STEM, whereas industry participants emphasize the predominance of men in STEM fields. Despite this difference in perspective, both groups emphasize the significance of events such as International Women's Day in honoring and promoting women's accomplishments. Another research conducted involving participants from the academia and the industry, highlights efficacious methodologies for propelling female empowerment such as offering female role models, recruiting women specifically, mentorship programs, support systems, workshops geared toward women, and equal opportunity for women [ 61 ]. The research also emphasizes the value of developing soft skills such as critical thinking, problem-solving, communication and teamwork in addition to technical expertise [ 61 ]. Amongst the challenges in effective pursuit of gender equality policies include limited plans and monitoring mechanisms, lack of awareness on gender equality systems as well as a lack of an effective monitoring system in evaluating gender equality initiatives and actions [ 18 , 62 ]. In this study, there are similarities but also differences in the views amongst participants from HE and the industry. However, in principle there is an agreement that gender inequality exists in many aspects of the STEM fields. Reconciling family and work is an issue that has been put forward by all participants and it is of high importance to put forward policies that will allow mothers to remain active in STEM. Research findings often set forward the dilemmas that women encounter across different cultures to choose between their job and having a child [ 63 ]. Possible suggestions for reconciling family and working life include longer maternity leaves and the support of family-friendly companies. Previous studies also support the provision of flexible work arrangements for STEM positions, such as extensive parental leave, that will encourage women to remain active in the field [ 22 , 23 , 32 , 33 ].

It seems that the participants would like to see high-level and low-level policies to be implemented for reinforcing the participation of women in STEM. Small changes from early childhood education can have big achievements that could impact remarkably on the beliefs and understanding of women in STEM. This is in line with previous studies that support that effective interventions for increasing girls’ interest in STEM should be implemented at an early stage, because their decisions at high school will determine their future education, career and salary [ 39 ].

Further, there is a need to establish gender equality action plans both in HE and in the industry as well as monitoring mechanisms for supporting equal opportunities are given to women. The first step towards this direction is already in action as Horizon Europe has set official criteria for HE and Research Organisations to have a gender equality plan (GEP) in place [ 64 ].

6 Conclusion

Our findings highlight the ongoing obstacles that women face in spite of significant efforts and policy initiatives. Women face similar challenges in STEM in both the industry and HE such as a pay gap, sexist comments, struggling with work-life balance and a lack of females in leading positions. Moreover, they usually need to work harder to reach the same recognition as men and some inequalities were noted regarding maternity. Nonetheless, there have been some improvements in the last years regarding the promotion and participation of women and discrimination is not a generalized experience for all women. Our study adds valuable insights to the existing literature, offering nuanced perspectives that pave the way for informed interventions and systemic changes to promote gender equality in STEM. The gender equality paradox, workplace expectations, and the need for comprehensive policies are critical considerations for fostering an inclusive STEM landscape. The findings highlight the urgency of targeted interventions to address specific issues in each setting, from discriminatory practices to the lack of women in leadership roles. Our research not only points out enduring problems, but it also offers a path forward for future action. It advances the more general objective of establishing inclusive and equitable STEM environments by providing a thorough understanding of the experiences of women and men in STEM and supporting focused, evidence-based interventions.

6.1 Implications for practitioners, policymakers and researchers

Several recommendations and suggestions have been voiced for overcoming challenges with regard to gender in HE and the industry, such as:

Providing training. Continuous training of females, teachers and employees can help overcome gender-related stereotypes and put forward a culture of equality.

Offering incentives. Incentives such as encouragement of female students in STEM competitions, gender quotas in research proposals and financial incentives for incorporating family-friendly practices are needed to keep women active in STEM in HE.

Raising awareness. Raising awareness on gender inequality through public events and promotion can help mitigate the challenges and stereotypes for women in STEM in HE and the industry.

Empowering and supporting. Empowering and supporting women to participate and remain active in all fields of STEM can lead to improvement of the quality of life for women, men, families and consequently communities.

Establishing gender equality policies. Local and EU policies and strategies both for HE and the industry need to be revisited and enhanced to tackle gender bias. More specifically, there seems to be an urgent need for strengthening gender-sensitive approaches in education and incorporating gender-sensitive plans in industry and HE. Policies for mitigating gender biases, sexual harassment and gender discrimination should be also employed in institutions and the industry.

Acknowledging women’s achievements. The successes of women in all STEM fields need to be promoted and act as examples for young women to follow and remain active, despite the numerous challenges and obstacles encountered.

7 Limitations

The findings are not generalizable. The convenience sample used in selecting the interviewees and the fact that participants represented STEM fields only consist a limitation. Additionally, as a limited number of male participants from academia and no male participants from the industry took place in this study, we draw attention to the necessity of conducting more gender-balanced research in the future to offer a thorough understanding of the experiences in STEM fields.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Martínez-Cantos JL. Digital skills gaps: a pending subject for gender digital inclusion in the European Union. Eur J Commun. 2017;32:419–38. https://doi.org/10.1177/0267323117718464 .

Article   Google Scholar  

Marsan GA, Sey A. 2021. Women’s participation in the digital economy: improving access to skills, entrepreneurship, and leadership across ASEAN. https://think-asia.org/handle/11540/13359

European Commission. She figures 2018. Research and innovation. 2019. https://op.europa.eu/en/publication-detail/-/publication/9540ffa1-4478-11e9-a8ed-01aa75ed71a1/language-en

Lockhart JW. Paradigms of sex research and women in stem. Gend Soc. 2021;35:449–75. https://doi.org/10.1177/08912432211001384 .

Glaudell R. Women in physics, math and astronomy at Caltech: supporting women in STEM. In: 47th IEEE Photovoltaic Specialists Conference (PVSC). 2020. IEEE; 2020, June. pp. 2147–2150. https://doi.org/10.1109/PVSC45281.2020.9300477 .

Charleston LJ, Adserias RP, Lang NM, Jackson JF. Intersectionality and STEM: the role of race and gender in the academic pursuits of African American women in STEM. J Prog Policy Pract. 2014;2:273–93.

Google Scholar  

Ceci SJ, Williams WM. Why aren’t more women in science. Top researchers debate the evidence. Washington: American Psychological Association; 2007.

Book   Google Scholar  

Correll SJ, Benard S, Paik I. Getting a job: is there a motherhood penalty? Am J Sociol. 2007;112:1297–339. https://doi.org/10.1086/511799 .

Sánchez de Madariaga I, de la Rica S, Dolado JJ. White paper on the position of women in science in Spain. Ministry of Science and Innovation; 2011.

European Institute for Gender Equality. How gender equality in STEM education leads to economic growth; 2017.

Malicky D. A literature review on the underrepresentation of women in undergraduate engineering: ability, self efficacy, and the Chilly Climat’’. In: Annual Conference. 2003; 2003. pp. 8–62.

Charlesworth TES, Banaji MR. Gender in science, technology, engineering, and mathematics: issues, causes, solutions. J Neurosci. 2019;39:7228–43. https://doi.org/10.1523/JNEUROSCI.0475-18.2019 .

Wang MT, Degol JL. Gender gap in science, technology, engineering, and mathematics (STEM): current knowledge, implications for practice, policy, and future directions. Educ Psychol Rev. 2017;29:119–40. https://doi.org/10.1007/s10648-015-9355-x .

UNESCO. Women in science. http://uis.unesco.org/sites/default/files/documents/fs51-women-in-science-2018-en.pdf ; 2018. UNESCO Institute for Statistics

Cardador MT, Damian RI, Wiegand JP. Does more mean less? Interest surplus and the gender gap in STEM careers. J Career Assess. 2021;29:76–97. https://doi.org/10.1177/1069072720930658 .

Handley HK, Hillman J, Finch M, Ubide T, Kachovich S, McLaren S, et al. In Australasia, gender is still on the agenda in geosciences. Adv Geosci. 2020;53:205–26. https://doi.org/10.5194/adgeo-53-205-2020 .

García-Holgado A, Mena J, García Peñalvo FJ, Pascual J, Heikkinen M, Harmoinen S et al. Gender equality in STEM programs: A proposal to analyse the situation of a university about the gender gap. In: IEEE Global Engineering Education Conference (EDUCON), 27–30 April2020, Porto, Portugal, 2020. pp. 1824–1830. https://doi.org/10.1109/EDUCON45650.2020.9125326 .

Kouta C, Parmaxi A, Smoleski I. Gender equality in academia, business, technology and health care: a WomEnPower view in cyprus. Int J Caring Sci. 2017;10:1224–31.

Sangar, S. Addressing low female participation in STEM for an inclusive industry 4.0: Mainstreaming gender. Gend. Perspect. Ind. 4.0 Impact Technol. Mainstreaming Female Employ. 2002; 91–106 https://doi.org/10.4018/978-1-7998-8594-8.CH005

Moss-Racusin CA, Sanzari C, Caluori N, Rabasco H. Gender bias produces gender gaps in STEM engagement. Sex Roles. 2018;79:651–70. https://doi.org/10.1007/s11199-018-0902-z .

Ricci A, Crivellaro F, Bolzani D. Perceived employability of highly skilled migrant women in STEM : insights from labor market intermediaries’ professionals. Admin Sci. 2021;11:1–18. https://doi.org/10.3390/admsci11010007 .

Cech EA, Blair-Loy M. The changing career trajectories of new parents in STEM. Proc Natl Acad Sci USA. 2019;116:4182–7. https://doi.org/10.1073/pnas.1810862116 .

Thoman SE, Stephens AK, Robnett RD. “Squeezing the life out of each day”: emerging adult women’s work-family expectations in STEM. Emerg Adulthood. 2022;10:76–89. https://doi.org/10.1177/2167696821990910 .

Peña K, Hinsen K, Wilbur M. Why diversity programs fail and how to fix them. SMPTE Motion Imaging J. 2018;127:56–69. https://doi.org/10.5594/JMI.2018.2860499 .

Yadav A, Seals C. Taking the next step: supporting postdocs to develop an independent path in academia. IJ STEM Ed. 2019;6:1–11. https://doi.org/10.1186/s40594-019-0168-1 .

Denend L, Mccutcheon S, Regan M, Sainz M, Yock P, Azagury D. Analysis of gender perceptions in health technology: a call to action. Ann Biomed Eng. 2020;48:1573–86. https://doi.org/10.1007/s10439-020-02478-0 .

Friedmann E. Increasing women’s participation in the STEM industry: a first step for developing a social marketing strategy. J Soc Mark. 2018;8:442–60.

Moser CE, Branscombe NR. Male allies at work: gender-equality supportive men reduce negative underrepresentation effects among women. Soc Psychol Pers Sci. 2022;13:372–81. https://doi.org/10.1177/19485506211033748 .

Christou E, Parmaxi A. Gender-sensitive tools and materials for women empowerment in STEM: A systematic review with industrial and instructional recommendations and implications. Univ Access Inf Soc. 2023;22:699–714. https://doi.org/10.1007/s10209-022-00881-z .

Christou E, Parmaxi A, Economides AA, Perifanou M, Manchenko M, Mazaj J. Challenges and good practices in STEM: A systematic review and implications for higher education institutions. In: IEEE Integrated STEM Education Conference (ISEC). 2022. IEEE; 2022. pp. 215–220. https://doi.org/10.1109/ISEC54952.2022.10025235 .

Alfred MV, Ray SM, Johnson MA. Advancing women of color in STEM: an imperative for US global competitiveness. Adv Dev Hum Resour. 2019;21:114–32. https://doi.org/10.1177/1523422318814551 .

Botella C, Rueda S, López-Iñesta E, Marzal P. Gender diversity in STEM disciplines: a multiple factor problem. Entropy. 2019;21:1–17. https://doi.org/10.3390/e21010030 .

Carr PL, Helitzer D, Freund K, Westring A, McGee R, Campbell PB, et al. A summary report from the research partnership on women in science careers. J Gen Intern Med. 2019;34:356–62. https://doi.org/10.1007/s11606-018-4547-y .

Makarem Y, Wang J. Career experiences of women in science, technology, engineering, and mathematics fields: a systematic literature review. Hum Resour Dev Q. 2020;31:91–111. https://doi.org/10.1002/hrdq.21380 .

García-Holgado A, García-Peñalvo FJ. A model for bridging the gender gap in STEM in higher education institutions Women in STEM in higher education: good practices of attraction, access and retainment in higher education. Singapore: Springer Nature Singapore. Berlin; 2022.

Casad BJ, et al. Gender inequality in academia: Problems and solutions for women faculty in STEM. J Neurosci Res. 2021;99:13–23.

Blackburn H. The status of women in STEM in higher education: a review of the literature 2007–2017. Sci Technol Libr. 2017;36:235–73. https://doi.org/10.1080/0194262X.2017.1371658 .

Tobar Subía Contento LM, Nohemi Gamez Aparicio B. The Gender Gap broad the path for Women in STEM. In: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality; 2020. pp. 187–192. doi: https://doi.org/10.1145/3434780.3436685 .

Granato S. Early influences and the gender gap in STEM. SSRN J. 2020. https://doi.org/10.2139/ssrn.3845391 .

Kricorian K, Seu M, Lopez D, Ureta E, Equils O. Factors influencing participation of underrepresented students in STEM Fields: matched mentors and mindsets. Int J STEM Educ. 2020. https://doi.org/10.1186/s40594-020-00219-2 .

O’Connell C, McKinnon M. Perceptions of barriers to career progression for academic women in STEM. Societies. 2021;11:27. https://doi.org/10.3390/soc11020027 .

Crenshaw KW. Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanford Law Rev. 1991;43:1241–99. https://doi.org/10.2307/1229039 .

Crenshaw KW. Kimberlé Crenshaw: women of the World Festival 2016 Keynote on intersectionality. YouTube. www.youtube.com/watch?v=-DW4HLgYPlA ; 2016.

DeFelice KA, Diller JW. Intersectional feminism and behavior analysis. Behav Anal Pract. 2019;12:831–8. https://doi.org/10.1007/s40617-019-00341-w .

Cadaret MC, Hartung PJ, Subich LM, Weigold IK. Stereotype threat as a barrier to women entering engineering careers. J Vocat Behav. 2017;99:40–51. https://doi.org/10.1016/j.jvb.2016.12.002 .

Park CJ, Rottinghaus PJ. Academic satisfaction of women students of color in science, technology, engineering, and mathematics: roles of discrimination, proactive personality, and critical consciousness. J Career Assess. 2023;31:298–320. https://doi.org/10.1177/10690727221116872 .

Schmader T. Gender inclusion and fit in STEM. Annu Rev Psychol. 2023;74:219–43. https://doi.org/10.1146/annurev-psych-032720-043052 .

Smith KC, Boakye B, Williams D, Fleming L. The exploration of how identity intersectionality strengthens STEM identity for black female undergraduates attending a historically black college and university (HBCU). J Negro Educ. 2019;88:407–18.

Jackson J, Huang-Saad A, Mondisa JL. The Urgency of Intersectionality: A Review of Racialized Experiences in STEM Entrepreneurship. ASEE Annu. Conf. Expo. Conf. Proc. 2021.

Sparks DM. Analyzing the Intersectional and bicultural experiences of black immigrant women STEM students at a diverse urban university: a phenomenological study. Urban Rev. 2023;55:269–92. https://doi.org/10.1007/s11256-022-00648-z .

Ross M, Hazari Z, Sonnert G, Sadler P. The intersection of being black and being a woman: examining the effect of social computing relationships on computer science career choice. ACM Trans Comput Educ. 2020;20:1–15. https://doi.org/10.1145/3377426 .

Dworkin SL. Sample size policy for qualitative studies using in-depth interviews. Arch Sex Behav. 2012;41:1319–20.

Guest G, Bunce A, Johnson L. How many interviews are enough?: An experiment with data saturation and variability. Field Methods. 2006;18:59–82.

Hennink M, Kaiser BN. Sample sizes for saturation in qualitative research: a systematic review of empirical tests. Soc Sci Med. 2022;292: 114523.

McNiff K. What is qualitative research? | The NVivo blog. http://www.qsrinternational.com/nvivo/nvivo-community/the-nvivo-blog/what-is-qualitative-research ; 2016.

Oliver DG, Serovich JM, Mason TL. Constraints and opportunities with interview transcription: towards reflection in qualitative research. Soc Forces. 2005;84:1273–89. https://doi.org/10.1353/sof.2006.0023 .

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101. https://doi.org/10.1191/1478088706qp063oa .

Menezes DP, Brito C, Anteneodo C 2019. Women in physics: scissors effect from the Brazilian Olympiad of physics to professional life. arXiv preprint arXiv:1901.05536 .

Stoet G, Geary DC. The gender-equality paradox in science, technology, engineering, and mathematics education. Psychol Sci. 2018;29:581–93. https://doi.org/10.1177/0956797617741719 .

European Commission. European citizens’ knowledge and attitudes towards science and technology 2021. https://doi.org/10.2775/071577 .

Christou E, Parmaxi A. 2023. Enhancing Multidisciplinary Skills for a Diverse Clean Energy Transition: A Stakeholder Interview Study. ICERI2023 Proc. https://doi.org/10.2125/ICERI.2023.2550

OECD. Closing the gender gap: act now. OECD publishing. 2012. https://doi.org/10.1787/9789264179370-en .

Neyer G, Lappegård T, Vignoli D. Gender equality and fertility: which equality matters?: egalité de genre et fécondité: de quelle égalité s’agit-il? European Journal of Population/Revue européenne de Démographie. Eur J Population. 2013;29:245–72. https://doi.org/10.1007/s10680-013-9292-7 .

Horizon Europe. Gender equality in horizon Europe. 2021. https://ec.europa.eu/info/research-and-innovation/strategy/strategy-2020-2024/democracy-and-citizens-rights/gender-equality-research-and-innovation_en#gender-equality-in-horizon-europe .

Download references

This work has been funded by the European Union’s Erasmus Plus programme, grant agreement: 2019-1-CY01-KA203-058407 (Project: FeSTEM). This publication reflects the views only of the authors, and the European Commission cannot be held responsible for any use which may be made of the information contained there.

Author information

Authors and affiliations.

Cyprus University of Technology, 30 Arch. Kyprianos Str., 3036, Limassol, Cyprus

Antigoni Parmaxi & Eirini Christou

Magenta Consultoría, Calle Venezuela 1, bajo derecha, 33213, Gijón, Asturias, Spain

Julia Fernández Valdés & Dalia María Puente Hevia

University of Macedonia, Egnatia 156, 54636, Thessaloniki, Greece

Maria Perifanou & Anastasios A. Economides

CESIE, Via Roma 94, 90133, Palermo, Italy

Jelena Mazaj & Maryna Manchenko

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the composition of the manuscript. The authors reviewed and approved the final version of the manuscript for submission. Τhe content of the manuscript has not been published, or submitted for publication elsewhere.

Corresponding author

Correspondence to Eirini Christou .

Ethics declarations

Ethics approval and consent to particiate.

Ethical issues were taken into consideration according to the General Data Protection Regulation, and all interviewees participated voluntarily. All participants were informed about the anonymity of the interviews and about the utilization of data for research purposes ensuring privacy and confidentiality issues. Informed consent was obtained from all subjects. The study participants were informed they could withdraw at any time without any consequences. Participants' data were stored securely and anonymously. There was no harm or discomfort experienced by the participants, and their rights were fully protected. All procedures performed in studies involving human participants were in accordance with the ethical standards of the national research committee.

Competing interests

The authors have no potential competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Parmaxi, A., Christou, E., Fernández Valdés, J. et al. Gender equality in science, technology, engineering and mathematics: industrial vis-a-vis academic perspective. Discov Educ 3 , 3 (2024). https://doi.org/10.1007/s44217-023-00082-7

Download citation

Received : 26 July 2023

Accepted : 14 December 2023

Published : 02 January 2024

DOI : https://doi.org/10.1007/s44217-023-00082-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Gender-equality
• STEM challenges
• STEM experiences

Advertisement

• Find a journal
• Publish with us
• Track your research

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A qualitative study on the relationship between faculty mobility and scientific impact: toward the sustainable development of higher education.

1. Introduction

2. literature review, 2.1. faculty mobility, 2.2. scientific impact, 2.3. relationship between faculty mobility and scientific impact, 3. data and research methodology, 3.1. dataset, 3.2. research methodology, 3.2.1. descriptive statistical analysis.

• Mobility Frequency: This study uses the change in the authors’ correspondence addresses as an indicator of mobility frequency. Samples with abnormal data and excessive mobility experiences were excluded, and only samples with 1–5 instances of mobility were included in the subsequent analysis.
• Citation Count: The number of times a paper is cited by other papers after publication is called the citation count. This reflects the referential value and importance of an original paper for subsequent research. Highly cited papers represent the frontier and hot issues in the field.
• Difference in Citation Count ( δ ): This refers to the difference in citation counts of papers by faculty members after mobility compared to the citation counts before mobility.

3.2.2. Normality Test

3.2.3. spearman’s rank correlation coefficient, 3.2.4. wilcoxon signed-rank test, 4.1. spearman’s rank correlation analysis of faculty mobility and citations, 4.1.1. correlation analysis of overall faculty mobility frequency and citations, 4.1.2. correlation analysis of faculty mobility frequency and paper citations by discipline, 4.2. wilcoxon signed-rank test analysis of faculty mobility and paper citations, 4.3. wilcoxon signed-rank test analysis of faculty mobility frequency and paper citations, 4.3.1. analysis of differences in paper citations by overall faculty mobility frequency, 4.3.2. analysis of differences in citations by discipline, 5. discussion and conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Huisman, J. International staff mobility. In Research Handbook on Academic Careers and Managing Academics ; Edward Elgar Publishing: Cheltenham, UK, 2022; pp. 325–337. [ Google Scholar ]
• Kilag, O.K.T.; Malbas, M.H.; Miñoza, J.R.; Ledesma, M.M.R.; Vestal, A.B.E.; Sasan, J.M.V. The Views of the Faculty on the Effectiveness of Teacher Education Programs in Developing Lifelong Learning Competence. Eur. J. High. Educ. Acad. Adv. 2024 , 1 , 92–102. [ Google Scholar ] [ CrossRef ]
• Brotherhood, T.; Hammond, C.D.; Kim, Y. Towards an actor-centered typology of internationalization: A study of junior international faculty in Japanese universities. High. Educ. 2020 , 79 , 497–514. [ Google Scholar ] [ CrossRef ]
• Seggie, F.N.; Çalıkoğlu, A. Changing patterns of international academic mobility: The experiences of Western-origin faculty members in Turkey. Comp. J. Comp. Int. Educ. 2023 , 53 , 1–18. [ Google Scholar ] [ CrossRef ]
• Fitzgerald, A.; Parr, G.; Williams, J.; Wellam, R.; Howard, B.; Zandes, S.; Diug, B. Interfaculty collaboration for improving international mobility experiences: Sustaining a dialogue across difference. Teach. High. Educ. 2023 , 28 , 51–65. [ Google Scholar ] [ CrossRef ]
• Seeber, M.; Debacker, N.; Meoli, M.; Vandevelde, K. Exploring the effects of mobility and foreign nationality on internal career progression in universities. High. Educ. 2023 , 85 , 1041–1081. [ Google Scholar ] [ CrossRef ]
• Verginer, L.; Riccaboni, M. Talent goes to global cities: The world network of scientists’ mobility. Res. Policy 2021 , 50 , 104127. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vaccario, G.; Verginer, L.; Schweitzer, F. Reproducing scientists’ mobility: A data-driven model. Sci. Rep. 2021 , 11 , 10733. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liu, T.; Pei, T.; Fang, Z.; Wu, M.; Liu, X.; Yan, X.; Song, C.; Jiang, J.; Jiang, L.; Chen, J. Spatiotemporal mobility network of global scientists, 1970–2020. Int. J. Geogr. Inf. Sci. 2024 , 1–28. [ Google Scholar ] [ CrossRef ]
• Momeni, F.; Karimi, F.; Mayr, P.; Peters, I.; Dietze, S. The many facets of academic mobility and its impact on scholars’ career. J. Inf. 2022 , 16 , 101280. [ Google Scholar ] [ CrossRef ]
• Bojica, A.M.; Olmos-Peñuela, J.; Alegre, J. A cross-country configurational approach to international academic mobility: Exploring mobility effects on academics’ career progression in EU countries. High. Educ. 2023 , 86 , 1081–1105. [ Google Scholar ] [ CrossRef ]
• Gureyev, V.N.; Mazov, N.A.; Kosyakov, D.V.; Guskov, A.E. Review and analysis of publications on scientific mobility: Assessment of influence, motivation, and trends. Scientometrics 2020 , 124 , 1599–1630. [ Google Scholar ] [ CrossRef ]
• Netz, N.; Hampel, S.; Aman, V. What effects does international mobility have on scientists’ careers? A systematic review. Res. Eval. 2020 , 29 , 327–351. [ Google Scholar ] [ CrossRef ]
• Azoulay, P.; Ganguli, I.; Zivin, J.G. The mobility of elite life scientists: Professional and personal determinants. Res. Policy 2017 , 46 , 573–590. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chang, Y.H.; Huang, M.H. Analysis of factors affecting scientific migration move and distance by academic age, migrant type, and country: Migrant researchers in the field of business and management. J. Inf. 2023 , 17 , 101371. [ Google Scholar ] [ CrossRef ]
• Marini, G.; Xu, X. Big fishes in a big pond: A comparison between foreign and Chinese academics’ research influence in Mainland China. Int. J. Chin. Educ. 2023 , 12 , 2212585X221145244. [ Google Scholar ] [ CrossRef ]
• Liu, M.; Hu, X. Movers’ advantages: The effect of mobility on scientists’ productivity and collaboration. J. Inf. 2022 , 16 , 101311. [ Google Scholar ] [ CrossRef ]
• Gu, J.; Pan, X.; Zhang, S.; Chen, J. International mobility matters: Research collaboration and scientific productivity. J. Inf. 2024 , 18 , 101522. [ Google Scholar ] [ CrossRef ]
• Han, F.; Zhang, R.; Zhang, S.; Yuan, J. International mobility characteristics, effects of, and effects on elite scientists. J. Inf. 2024 , 18 , 101485. [ Google Scholar ] [ CrossRef ]
• Altbach, P.G.; Reisberg, L.; Rumbley, L.E. Trends in Global Higher Education: Tracking an Academic Revolution ; Brill: Leiden, The Netherlands, 2019; Volume 22. [ Google Scholar ]
• Marini, G. Brexit and the war for talents: Push & pull evidence about competitiveness. High. Educ. 2024 , 1–16. [ Google Scholar ] [ CrossRef ]
• Selmer, J.; Dickmann, M.; Froese, F.J.; Lauring, J.; Reiche, B.S.; Shaffer, M. The potential of virtual global mobility: Implications for practice and future research. J. Glob. Mobil. Home Expatr. Manag. Res. 2022 , 10 , 1–13. [ Google Scholar ] [ CrossRef ]
• Guo, Y.; Jasovska, P.; Rammal, H.G.; Rose, E.L. Global mobility of professionals and the transfer of tacit knowledge in multinational service firms. J. Knowl. Manag. 2020 , 24 , 553–567. [ Google Scholar ] [ CrossRef ]
• Gérard, E.; Lebeau, Y. Trajectories within international academic mobility: A renewed perspective on the dynamics and hierarchies of the global higher education field. Int. J. Educ. Dev. 2023 , 100 , 102780. [ Google Scholar ] [ CrossRef ]
• De Haas, H. A theory of migration: The aspirations-capabilities framework. Comp. Migr. Stud. 2021 , 9 , 8. [ Google Scholar ] [ CrossRef ]
• Cohen, R. Theories of Migration ; Edward Elgar: Cheltenham, UK, 1996. [ Google Scholar ]
• Lee, W.O. Academic migration and reshaping of pedagogy and epistemology: An insider-outsider perspective. In Academic Migration, Discipline Knowledge and Pedagogical Practice: Voices from the Asia-Pacific ; Springer: Berlin/Heidelberg, Germany, 2013; pp. 161–175. [ Google Scholar ]
• Huo, Y. A survey of reading promotion methods in universities in Mainland China. J. Contemp. Educ. Res. 2018 , 2 . [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Chen, Y. Faculty Mobility Patterns in Major Chinese Universities. J. High. Educ. Res. 2019 , 30 , 123–145. [ Google Scholar ]
• Kuzhabekova, A.; Lee, J. Relocation decision of international faculty in Kazakhstan. J. Stud. Int. Educ. 2018 , 22 , 414–433. [ Google Scholar ] [ CrossRef ]
• Lee, J.T.; Kuzhabekova, A. Reverse flow in academic mobility from core to periphery: Motivations of international faculty working in Kazakhstan. High. Educ. 2018 , 76 , 369–386. [ Google Scholar ] [ CrossRef ]
• Sawyer, R.K.; Henriksen, D. Explaining Creativity: The Science of Human Innovation ; Oxford University Press: Oxford, UK, 2024. [ Google Scholar ]
• Whyte, W.H. The organization man. In American Social Character ; Routledge: London, UK, 2019; pp. 70–86. [ Google Scholar ]
• Baruffaldi, S.H.; Landoni, P. Mobility intentions of foreign researchers: The role of non-economic motivations. Ind. Innov. 2016 , 23 , 87–111. [ Google Scholar ] [ CrossRef ]
• Brogaard, J.; Engelberg, J.; Van Wesep, E. Do economists swing for the fences after tenure? J. Econ. Perspect. 2018 , 32 , 179–194. [ Google Scholar ] [ CrossRef ]
• Van Dalen, H.P. How the publish-or-perish principle divides a science: The case of economists. Scientometrics 2021 , 126 , 1675–1694. [ Google Scholar ] [ CrossRef ]
• Stremersch, S.; Winer, R.S.; Camacho, N. Faculty research incentives and business school health: A new perspective from and for marketing. J. Mark. 2021 , 85 , 1–21. [ Google Scholar ] [ CrossRef ]
• Niles, M.T.; Schimanski, L.A.; McKiernan, E.C.; Alperin, J.P. Why we publish where we do: Faculty publishing values and their relationship to review, promotion and tenure expectations. PLoS ONE 2020 , 15 , e0228914. [ Google Scholar ] [ CrossRef ]
• Yang, X.; You, Y. How the world-class university project affects scientific productivity? Evidence from a survey of faculty members in China. High. Educ. Policy 2018 , 31 , 583–605. [ Google Scholar ] [ CrossRef ]
• Yang, X.; Cai, X.; Cai, J. The effect of the tenure track on faculty member’s job insecurity and innovative work behavior. High. Educ. 2024 , 1–20. [ Google Scholar ] [ CrossRef ]
• Mueller, C.M.; Gaudilliere, D.K.; Kin, C.; Menorca, R.; Girod, S. Gender disparities in scholarly productivity of US academic surgeons. J. Surg. Res. 2016 , 203 , 28–33. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, X.; Cai, X.; Li, T. Does the tenure track influence academic research? An empirical study of faculty members in China. Stud. High. Educ. 2024 , 49 , 476–492. [ Google Scholar ] [ CrossRef ]
• Nafukho, F.M.; Wekullo, C.S.; Muyia, M.H. Examining research productivity of faculty in selected leading public universities in Kenya. Int. J. Educ. Dev. 2019 , 66 , 44–51. [ Google Scholar ] [ CrossRef ]
• Mixon, F.G., Jr.; Upadhyaya, K.P. When forgiveness beats permission: Exploring the scholarly ethos of clinical faculty in economics. Am. J. Econ. Sociol. 2024 , 83 , 75–91. [ Google Scholar ] [ CrossRef ]
• Clancy, M.; Correa, D.; Dworkin, J.; Niehaus, P.; Watney, C.; Williams, H. Want to speed up scientific progress? First understand how science policy works. Nature 2023 , 620 , 724–726. [ Google Scholar ] [ CrossRef ]
• Zweig, D. Competing for talent: China’s strategies to reverse the brain drain. Int. Lab. Rev. 2006 , 145 , 65. [ Google Scholar ] [ CrossRef ]
• Zhao, J.; Wei, C.; Li, J. Is the research performance of Chinese returnees better than that of their local counterparts? Scientometrics 2023 , 128 , 3091–3105. [ Google Scholar ] [ CrossRef ]
• Marini, G.; Yang, L. Globally bred Chinese talents returning home: An analysis of a reverse brain-drain flagship policy. Sci. Public Policy 2021 , 48 , 541–552. [ Google Scholar ] [ CrossRef ]
• Zhao, Z.; Bu, Y.; Kang, L.; Min, C.; Bian, Y.; Tang, L.; Li, J. An investigation of the relationship between scientists’ mobility to/from China and their research performance. J. Inf. 2020 , 14 , 101037. [ Google Scholar ] [ CrossRef ]
• Cao, C.; Baas, J.; Wagner, C.S.; Jonkers, K. Returning scientists and the emergence of China’s science system. Sci. Public Policy 2020 , 47 , 172–183. [ Google Scholar ] [ CrossRef ]
• Shi, D.; Liu, W.; Wang, Y. Has China’s Young Thousand Talents program been successful in recruiting and nurturing top-caliber scientists? Science 2023 , 379 , 62–65. [ Google Scholar ] [ CrossRef ]
• Halevi, G.; Moed, H.F.; Bar-Ilan, J. Researchers’ mobility, productivity and impact: Case of top producing authors in seven disciplines. Publ. Res. Q. 2016 , 32 , 22–37. [ Google Scholar ] [ CrossRef ]
• Ryazanova, O.; McNamara, P. Choices and consequences: Impact of mobility on research-career capital and promotion in business schools. Acad. Manag. Learn. Educ. 2019 , 18 , 186–212. [ Google Scholar ] [ CrossRef ]
• Fu, Y.C.; Vásquez, J.J.M.; Macasaet, B.T.; Hou, A.Y.C.; Powell, J.J. Game of brains: Examining researcher brain gain and brain drain and research university policy. High. Educ. Policy 2023 , 18 , 1–22. [ Google Scholar ] [ CrossRef ]
• Jin, C.; Ma, Y.; Uzzi, B. Scientific prizes and the extraordinary growth of scientific topics. Nat. Commun. 2021 , 12 , 5619. [ Google Scholar ] [ CrossRef ]
• Santos, J.M.; Horta, H.; Li, H. Are the strategic research agendas of researchers in the social sciences determinants of research productivity? Scientometrics 2022 , 127 , 3719–3747. [ Google Scholar ] [ CrossRef ]
• Petersen, A.M. Multiscale impact of researcher mobility. J. R. Soc. Interface 2018 , 15 , 20180580. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Finocchi, I.; Ribichini, A.; Schaerf, M. An analysis of international mobility and research productivity in computer science. Scientometrics 2023 , 128 , 6147–6175. [ Google Scholar ] [ CrossRef ]
• Ejermo, O.; Fassio, C.; Källström, J. Does mobility across universities raise scientific productivity? Oxf. Bull. Econ. Stat. 2020 , 82 , 603–624. [ Google Scholar ] [ CrossRef ]
• Iversen, E.J.; Woolley, R. International mobility and career progression of European academics. In Proceedings of the 27th International Conference on Science, Technology and Innovation Indicators (STI 2023), Leiden, The Netherlands, 27–29 September 2023. [ Google Scholar ]
• Wang, D.; Song, C.; Barabási, A.L. Quantifying long-term scientific impact. Science 2013 , 342 , 127–132. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hunter, R.S.; Oswald, A.J.; Charlton, B.G. The elite brain drain. Econ. J. 2009 , 119 , F231–F251. [ Google Scholar ]
• Gibson, J.; McKenzie, D. The economic consequences of ‘brain drain’of the best and brightest: Microeconomic evidence from five countries. Econ. J. 2012 , 122 , 339–375. [ Google Scholar ]
• De Rassenfosse, G.; Murovana, T.; Uhlbach, W.H. The effects of war on Ukrainian research. Humanit. Soc. Sci. Commun. 2023 , 10 , 1–11. [ Google Scholar ] [ CrossRef ]

Share and Cite

Zhang, J.; Su, X.; Wang, Y. A Qualitative Study on the Relationship between Faculty Mobility and Scientific Impact: Toward the Sustainable Development of Higher Education. Sustainability 2024 , 16 , 7739. https://doi.org/10.3390/su16177739

Zhang J, Su X, Wang Y. A Qualitative Study on the Relationship between Faculty Mobility and Scientific Impact: Toward the Sustainable Development of Higher Education. Sustainability . 2024; 16(17):7739. https://doi.org/10.3390/su16177739

Zhang, Jun, Xiaoyan Su, and Yifei Wang. 2024. "A Qualitative Study on the Relationship between Faculty Mobility and Scientific Impact: Toward the Sustainable Development of Higher Education" Sustainability 16, no. 17: 7739. https://doi.org/10.3390/su16177739

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals