2009)
Berge and Mrozowski (2001) reviewed 890 research articles and dissertation abstracts on distance education from 1990 to 1999. The four distance education journals chosen by the authors to represent distance education included, American Journal of Distance Education, Distance Education, Open Learning, and the Journal of Distance Education. This review overlapped in the dates of the Tallent-Runnels et al. (2006) study. Berge and Mrozowski (2001) categorized the articles according to Sherry's (1996) ten themes of research issues in distance education: redefining roles of instructor and students, technologies used, issues of design, strategies to stimulate learning, learner characteristics and support, issues related to operating and policies and administration, access and equity, and costs and benefits.
In the Berge and Mrozowski (2001) study, more than 100 studies focused on each of the three themes: (1) design issues, (2) learner characteristics, and (3) strategies to increase interactivity and active learning. By design issues, the authors focused on instructional systems design and focused on topics such as content requirement, technical constraints, interactivity, and feedback. The next theme, strategies to increase interactivity and active learning, were closely related to design issues and focused on students’ modes of learning. Learner characteristics focused on accommodating various learning styles through customized instructional theory. Less than 50 studies focused on the three least examined themes: (1) cost-benefit tradeoffs, (2) equity and accessibility, and (3) learner support. Cost-benefit trade-offs focused on the implementation costs of distance education based on school characteristics. Equity and accessibility focused on the equity of access to distance education systems. Learner support included topics such as teacher to teacher support as well as teacher to student support.
Tallent-Runnels et al. (2006) reviewed research on online instruction from 1993 to 2004. They reviewed 76 articles focused on online learning by searching five databases, ERIC, PsycINFO, ContentFirst, Education Abstracts, and WilsonSelect. Tallent-Runnels et al. (2006) categorized research into four themes, (1) course environment, (2) learners' outcomes, (3) learners’ characteristics, and (4) institutional and administrative factors. The first theme that the authors describe as course environment ( n = 41, 53.9%) is an overarching theme that includes classroom culture, structural assistance, success factors, online interaction, and evaluation.
Tallent-Runnels et al. (2006) for their second theme found that studies focused on questions involving the process of teaching and learning and methods to explore cognitive and affective learner outcomes ( n = 29, 38.2%). The authors stated that they found the research designs flawed and lacked rigor. However, the literature comparing traditional and online classrooms found both delivery systems to be adequate. Another research theme focused on learners’ characteristics ( n = 12, 15.8%) and the synergy of learners, design of the online course, and system of delivery. Research findings revealed that online learners were mainly non-traditional, Caucasian, had different learning styles, and were highly motivated to learn. The final theme that they reported was institutional and administrative factors (n = 13, 17.1%) on online learning. Their findings revealed that there was a lack of scholarly research in this area and most institutions did not have formal policies in place for course development as well as faculty and student support in training and evaluation. Their research confirmed that when universities offered online courses, it improved student enrollment numbers.
Zawacki-Richter et al. (2009) reviewed 695 articles on distance education from 2000 to 2008 using the Delphi method for consensus in identifying areas and classified the literature from five prominent journals. The five journals selected due to their wide scope in research in distance education included Open Learning, Distance Education, American Journal of Distance Education, the Journal of Distance Education, and the International Review of Research in Open and Distributed Learning. The reviewers examined the main focus of research and identified gaps in distance education research in this review.
Zawacki-Richter et al. (2009) classified the studies into macro, meso and micro levels focusing on 15 areas of research. The five areas of the macro-level addressed: (1) access, equity and ethics to deliver distance education for developing nations and the role of various technologies to narrow the digital divide, (2) teaching and learning drivers, markets, and professional development in the global context, (3) distance delivery systems and institutional partnerships and programs and impact of hybrid modes of delivery, (4) theoretical frameworks and models for instruction, knowledge building, and learner interactions in distance education practice, and (5) the types of preferred research methodologies. The meso-level focused on seven areas that involve: (1) management and organization for sustaining distance education programs, (2) examining financial aspects of developing and implementing online programs, (3) the challenges and benefits of new technologies for teaching and learning, (4) incentives to innovate, (5) professional development and support for faculty, (6) learner support services, and (7) issues involving quality standards and the impact on student enrollment and retention. The micro-level focused on three areas: (1) instructional design and pedagogical approaches, (2) culturally appropriate materials, interaction, communication, and collaboration among a community of learners, and (3) focus on characteristics of adult learners, socio-economic backgrounds, learning preferences, and dispositions.
The top three research themes in this review by Zawacki-Richter et al. (2009) were interaction and communities of learning ( n = 122, 17.6%), instructional design ( n = 121, 17.4%) and learner characteristics ( n = 113, 16.3%). The lowest number of studies (less than 3%) were found in studies examining the following research themes, management and organization ( n = 18), research methods in DE and knowledge transfer ( n = 13), globalization of education and cross-cultural aspects ( n = 13), innovation and change ( n = 13), and costs and benefits ( n = 12).
These three systematic reviews provide a broad understanding of distance education and online learning research themes from 1990 to 2008. However, there is an increase in the number of research studies on online learning in this decade and there is a need to identify recent research themes examined. Based on the previous systematic reviews ( Berge & Mrozowski, 2001 ; Hung, 2012 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ), online learning research in this study is grouped into twelve different research themes which include Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes. Table 2 below describes each of the research themes and using these themes, a framework is derived in Fig. 1 .
Research themes in online learning.
Research Theme | Description | |
---|---|---|
1 | Learner Characteristics | Focuses on understanding the learner characteristics and how online learning can be designed and delivered to meet their needs. Online learner characteristics can be broadly categorized into demographic characteristics, academic characteristics, cognitive characteristics, affective, self-regulation, and motivational characteristics. |
2 | Learner Outcomes | Learner outcomes are statements that specify what the learner will achieve at the end of the course or program. Examining learner outcomes such as success, retention, and dropouts are critical in online courses. |
3 | Engagement | Engaging the learner in the online course is vitally important as they are separated from the instructor and peers in the online setting. Engagement is examined through the lens of interaction, participation, community, collaboration, communication, involvement and presence. |
4 | Course or Program Design and Development | Course design and development is critical in online learning as it engages and assists the students in achieving the learner outcomes. Several models and processes are used to develop the online course, employing different design elements to meet student needs. |
5 | Course Facilitation | The delivery or facilitation of the course is as important as course design. Facilitation strategies used in delivery of the course such as in communication and modeling practices are examined in course facilitation. |
6 | Course Assessment | Course Assessments are adapted and delivered in an online setting. Formative assessments, peer assessments, differentiated assessments, learner choice in assessments, feedback system, online proctoring, plagiarism in online learning, and alternate assessments such as eportfolios are examined. |
7 | Evaluation and Quality Assurance | Evaluation is making a judgment either on the process, the product or a program either during or at the end. There is a need for research on evaluation and quality in the online courses. This has been examined through course evaluations, surveys, analytics, social networks, and pedagogical assessments. Quality assessment rubrics such as Quality Matters have also been researched. |
8 | Course Technologies | A number of online course technologies such as learning management systems, online textbooks, online audio and video tools, collaborative tools, social networks to build online community have been the focus of research. |
9 | Instructor Characteristics | With the increase in online courses, there has also been an increase in the number of instructors teaching online courses. Instructor characteristics can be examined through their experience, satisfaction, and roles in online teaching. |
10 | Institutional Support | The support for online learning is examined both as learner support and instructor support. Online students need support to be successful online learners and this could include social, academic, and cognitive forms of support. Online instructors need support in terms of pedagogy and technology to be successful online instructors. |
11 | Access, Culture, Equity, Inclusion, and Ethics | Cross-cultural online learning is gaining importance along with access in global settings. In addition, providing inclusive opportunities for all learners and in ethical ways is being examined. |
12 | Leadership, Policy and Management | Leadership support is essential for success of online learning. Leaders perspectives, challenges and strategies used are examined. Policies and governance related research are also being studied. |
Online learning research themes framework.
The collection of research themes is presented as a framework in Fig. 1 . The themes are organized by domain or level to underscore the nested relationship that exists. As evidenced by the assortment of themes, research can focus on any domain of delivery or associated context. The “Learner” domain captures characteristics and outcomes related to learners and their interaction within the courses. The “Course and Instructor” domain captures elements about the broader design of the course and facilitation by the instructor, and the “Organizational” domain acknowledges the contextual influences on the course. It is important to note as well that due to the nesting, research themes can cross domains. For example, the broader cultural context may be studied as it pertains to course design and development, and institutional support can include both learner support and instructor support. Likewise, engagement research can involve instructors as well as learners.
In this introduction section, we have reviewed three systematic reviews on online learning research ( Berge & Mrozowski, 2001 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ). Based on these reviews and other research, we have derived twelve themes to develop an online learning research framework which is nested in three levels: learner, course and instructor, and organization.
In two out of the three previous reviews, design, learner characteristics and interaction were examined in the highest number of studies. On the other hand, cost-benefit tradeoffs, equity and accessibility, institutional and administrative factors, and globalization and cross-cultural aspects were examined in the least number of studies. One explanation for this may be that it is a function of nesting, noting that studies falling in the Organizational and Course levels may encompass several courses or many more participants within courses. However, while some research themes re-occur, there are also variations in some themes across time, suggesting the importance of research themes rise and fall over time. Thus, a critical examination of the trends in themes is helpful for understanding where research is needed most. Also, since there is no recent study examining online learning research themes in the last decade, this study strives to address that gap by focusing on recent research themes found in the literature, and also reviewing research methods and settings. Notably, one goal is to also compare findings from this decade to the previous review studies. Overall, the purpose of this study is to examine publication trends in online learning research taking place during the last ten years and compare it with the previous themes identified in other review studies. Due to the continued growth of online learning research into new contexts and among new researchers, we also examine the research methods and settings found in the studies of this review.
The following research questions are addressed in this study.
This five-step systematic review process described in the U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse Procedures and Standards Handbook, Version 4.0 ( 2017 ) was used in this systematic review: (a) developing the review protocol, (b) identifying relevant literature, (c) screening studies, (d) reviewing articles, and (e) reporting findings.
The Education Research Complete database was searched using the keywords below for published articles between the years 2009 and 2018 using both the Title and Keyword function for the following search terms.
“online learning" OR "online teaching" OR "online program" OR "online course" OR “online education”
The initial search of online learning research among journals in the database resulted in more than 3000 possible articles. Therefore, we limited our search to select journals that focus on publishing peer-reviewed online learning and educational research. Our aim was to capture the journals that published the most articles in online learning. However, we also wanted to incorporate the concept of rigor, so we used expert perception to identify 12 peer-reviewed journals that publish high-quality online learning research. Dissertations and conference proceedings were excluded. To be included in this systematic review, each study had to meet the screening criteria as described in Table 3 . A research study was excluded if it did not meet all of the criteria to be included.
Inclusion/Exclusion criteria.
Criteria | Inclusion | Exclusion |
---|---|---|
Focus of the article | Online learning | Articles that did not focus on online learning |
Journals Published | Twelve identified journals | Journals outside of the 12 journals |
Publication date | 2009 to 2018 | Prior to 2009 and after 2018 |
Publication type | Scholarly articles of original research from peer reviewed journals | Book chapters, technical reports, dissertations, or proceedings |
Research Method and Results | There was an identifiable method and results section describing how the study was conducted and included the findings. Quantitative and qualitative methods were included. | Reviews of other articles, opinion, or discussion papers that do not include a discussion of the procedures of the study or analysis of data such as product reviews or conceptual articles. |
Language | Journal article was written in English | Other languages were not included |
Fig. 2 shows the process flow involved in the selection of articles. The search in the database Education Research Complete yielded an initial sample of 3332 articles. Targeting the 12 journals removed 2579 articles. After reviewing the abstracts, we removed 134 articles based on the inclusion/exclusion criteria. The final sample, consisting of 619 articles, was entered into the computer software MAXQDA ( VERBI Software, 2019 ) for coding.
Flowchart of online learning research selection.
A review protocol was designed as a codebook in MAXQDA ( VERBI Software, 2019 ) by the three researchers. The codebook was developed based on findings from the previous review studies and from the initial screening of the articles in this review. The codebook included 12 research themes listed earlier in Table 2 (Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes), four research settings (higher education, continuing education, K-12, corporate/military), and three research designs (quantitative, qualitative and mixed methods). Fig. 3 below is a screenshot of MAXQDA used for the coding process.
Codebook from MAXQDA.
Research articles were coded by two researchers in MAXQDA. Two researchers independently coded 10% of the articles and then discussed and updated the coding framework. The second author who was a doctoral student coded the remaining studies. The researchers met bi-weekly to address coding questions that emerged. After the first phase of coding, we found that more than 100 studies fell into each of the categories of Learner Characteristics or Engagement, so we decided to pursue a second phase of coding and reexamine the two themes. Learner Characteristics were classified into the subthemes of Academic, Affective, Motivational, Self-regulation, Cognitive, and Demographic Characteristics. Engagement was classified into the subthemes of Collaborating, Communication, Community, Involvement, Interaction, Participation, and Presence.
Frequency tables were generated for each of the variables so that outliers could be examined and narrative data could be collapsed into categories. Once cleaned and collapsed into a reasonable number of categories, descriptive statistics were used to describe each of the coded elements. We first present the frequencies of publications related to online learning in the 12 journals. The total number of articles for each journal (collectively, the population) was hand-counted from journal websites, excluding editorials and book reviews. The publication trend of online learning research was also depicted from 2009 to 2018. Then, the descriptive information of the 12 themes, including the subthemes of Learner Characteristics and Engagement were provided. Finally, research themes by research settings and methodology were elaborated.
Publication patterns of the 619 articles reviewed from the 12 journals are presented in Table 4 . International Review of Research in Open and Distributed Learning had the highest number of publications in this review. Overall, about 8% of the articles appearing in these twelve journals consisted of online learning publications; however, several journals had concentrations of online learning articles totaling more than 20%.
Empirical online learning research articles by journal, 2009–2018.
Journal Name | Frequency of Empirical Online Learning Research | Percent of Sample | Percent of Journal's Total Articles |
---|---|---|---|
International Review of Research in Open and Distributed Learning | 152 | 24.40 | 22.55 |
Internet & Higher Education | 84 | 13.48 | 26.58 |
Computers & Education | 75 | 12.04 | 18.84 |
Online Learning | 72 | 11.56 | 3.25 |
Distance Education | 64 | 10.27 | 25.10 |
Journal of Online Learning & Teaching | 39 | 6.26 | 11.71 |
Journal of Educational Technology & Society | 36 | 5.78 | 3.63 |
Quarterly Review of Distance Education | 24 | 3.85 | 4.71 |
American Journal of Distance Education | 21 | 3.37 | 9.17 |
British Journal of Educational Technology | 19 | 3.05 | 1.93 |
Educational Technology Research & Development | 19 | 3.05 | 10.80 |
Australasian Journal of Educational Technology | 14 | 2.25 | 2.31 |
Total | 619 | 100.0 | 8.06 |
Note . Journal's Total Article count excludes reviews and editorials.
The publication trend of online learning research is depicted in Fig. 4 . When disaggregated by year, the total frequency of publications shows an increasing trend. Online learning articles increased throughout the decade and hit a relative maximum in 2014. The greatest number of online learning articles ( n = 86) occurred most recently, in 2018.
Online learning publication trends by year.
The publications were categorized into the twelve research themes identified in Fig. 1 . The frequency counts and percentages of the research themes are provided in Table 5 below. A majority of the research is categorized into the Learner domain. The fewest number of articles appears in the Organization domain.
Research themes in the online learning publications from 2009 to 2018.
Research Themes | Frequency | Percentage |
---|---|---|
Engagement | 179 | 28.92 |
Learner Characteristics | 134 | 21.65 |
Learner Outcome | 32 | 5.17 |
Evaluation and Quality Assurance | 38 | 6.14 |
Course Technologies | 35 | 5.65 |
Course Facilitation | 34 | 5.49 |
Course Assessment | 30 | 4.85 |
Course Design and Development | 27 | 4.36 |
Instructor Characteristics | 21 | 3.39 |
Institutional Support | 33 | 5.33 |
Access, Culture, Equity, Inclusion, and Ethics | 29 | 4.68 |
Leadership, Policy, and Management | 27 | 4.36 |
The specific themes of Engagement ( n = 179, 28.92%) and Learner Characteristics ( n = 134, 21.65%) were most often examined in publications. These two themes were further coded to identify sub-themes, which are described in the next two sections. Publications focusing on Instructor Characteristics ( n = 21, 3.39%) were least common in the dataset.
The largest number of studies was on engagement in online learning, which in the online learning literature is referred to and examined through different terms. Hence, we explore this category in more detail. In this review, we categorized the articles into seven different sub-themes as examined through different lenses including presence, interaction, community, participation, collaboration, involvement, and communication. We use the term “involvement” as one of the terms since researchers sometimes broadly used the term engagement to describe their work without further description. Table 6 below provides the description, frequency, and percentages of the various studies related to engagement.
Research sub-themes on engagement.
Description | Frequency | Percentage | |
---|---|---|---|
Presence | Learning experience through social, cognitive, and teaching presence. | 50 | 8.08 |
Interaction | Process of interacting with peers, instructor, or content that results in learners understanding or behavior | 43 | 6.95 |
Community | Sense of belonging within a group | 25 | 4.04 |
Participation | Process of being actively involved | 21 | 3.39 |
Collaboration | Working with someone to create something | 17 | 2.75 |
Involvement | Involvement in learning. This includes articles that focused broadly on engagement of learners. | 14 | 2.26 |
Communication | Process of exchanging information with the intent to share information | 9 | 1.45 |
In the sections below, we provide several examples of the different engagement sub-themes that were studied within the larger engagement theme.
Presence. This sub-theme was the most researched in engagement. With the development of the community of inquiry framework most of the studies in this subtheme examined social presence ( Akcaoglu & Lee, 2016 ; Phirangee & Malec, 2017 ; Wei et al., 2012 ), teaching presence ( Orcutt & Dringus, 2017 ; Preisman, 2014 ; Wisneski et al., 2015 ) and cognitive presence ( Archibald, 2010 ; Olesova et al., 2016 ).
Interaction . This was the second most studied theme under engagement. Researchers examined increasing interpersonal interactions ( Cung et al., 2018 ), learner-learner interactions ( Phirangee, 2016 ; Shackelford & Maxwell, 2012 ; Tawfik et al., 2018 ), peer-peer interaction ( Comer et al., 2014 ), learner-instructor interaction ( Kuo et al., 2014 ), learner-content interaction ( Zimmerman, 2012 ), interaction through peer mentoring ( Ruane & Koku, 2014 ), interaction and community building ( Thormann & Fidalgo, 2014 ), and interaction in discussions ( Ruane & Lee, 2016 ; Tibi, 2018 ).
Community. Researchers examined building community in online courses ( Berry, 2017 ), supporting a sense of community ( Jiang, 2017 ), building an online learning community of practice ( Cho, 2016 ), building an academic community ( Glazer & Wanstreet, 2011 ; Nye, 2015 ; Overbaugh & Nickel, 2011 ), and examining connectedness and rapport in an online community ( Bolliger & Inan, 2012 ; Murphy & Rodríguez-Manzanares, 2012 ; Slagter van Tryon & Bishop, 2012 ).
Participation. Researchers examined engagement through participation in a number of studies. Some of the topics include, participation patterns in online discussion ( Marbouti & Wise, 2016 ; Wise et al., 2012 ), participation in MOOCs ( Ahn et al., 2013 ; Saadatmand & Kumpulainen, 2014 ), features that influence students’ online participation ( Rye & Støkken, 2012 ) and active participation.
Collaboration. Researchers examined engagement through collaborative learning. Specific studies focused on cross-cultural collaboration ( Kumi-Yeboah, 2018 ; Yang et al., 2014 ), how virtual teams collaborate ( Verstegen et al., 2018 ), types of collaboration teams ( Wicks et al., 2015 ), tools for collaboration ( Boling et al., 2014 ), and support for collaboration ( Kopp et al., 2012 ).
Involvement. Researchers examined engaging learners through involvement in various learning activities ( Cundell & Sheepy, 2018 ), student engagement through various measures ( Dixson, 2015 ), how instructors included engagement to involve students in learning ( O'Shea et al., 2015 ), different strategies to engage the learner ( Amador & Mederer, 2013 ), and designed emotionally engaging online environments ( Koseoglu & Doering, 2011 ).
Communication. Researchers examined communication in online learning in studies using social network analysis ( Ergün & Usluel, 2016 ), using informal communication tools such as Facebook for class discussion ( Kent, 2013 ), and using various modes of communication ( Cunningham et al., 2010 ; Rowe, 2016 ). Studies have also focused on both asynchronous and synchronous aspects of communication ( Swaggerty & Broemmel, 2017 ; Yamagata-Lynch, 2014 ).
The second largest theme was learner characteristics. In this review, we explore this further to identify several aspects of learner characteristics. In this review, we categorized the learner characteristics into self-regulation characteristics, motivational characteristics, academic characteristics, affective characteristics, cognitive characteristics, and demographic characteristics. Table 7 provides the number of studies and percentages examining the various learner characteristics.
Research sub-themes on learner characteristics.
Learner Characteristics | Description | Frequency | Percentage |
---|---|---|---|
Self-regulation Characteristics | Involves controlling learner's behavior, emotions, and thoughts to achieve specific learning and performance goals | 54 | 8.72 |
Motivational Characteristics | Learners goal-directed activity instigated and sustained such as beliefs, and behavioral change | 23 | 3.72 |
Academic Characteristics | Education characteristics such as educational type and educational level | 19 | 3.07 |
Affective Characteristics | Learner characteristics that describe learners' feelings or emotions such as satisfaction | 17 | 2.75 |
Cognitive Characteristics | Learner characteristics related to cognitive elements such as attention, memory, and intellect (e.g., learning strategies, learning skills, etc.) | 14 | 2.26 |
Demographic Characteristics | Learner characteristics that relate to information as age, gender, language, social economic status, and cultural background. | 7 | 1.13 |
Online learning has elements that are different from the traditional face-to-face classroom and so the characteristics of the online learners are also different. Yukselturk and Top (2013) categorized online learner profile into ten aspects: gender, age, work status, self-efficacy, online readiness, self-regulation, participation in discussion list, participation in chat sessions, satisfaction, and achievement. Their categorization shows that there are differences in online learner characteristics in these aspects when compared to learners in other settings. Some of the other aspects such as participation and achievement as discussed by Yukselturk and Top (2013) are discussed in different research themes in this study. The sections below provide examples of the learner characteristics sub-themes that were studied.
Self-regulation. Several researchers have examined self-regulation in online learning. They found that successful online learners are academically motivated ( Artino & Stephens, 2009 ), have academic self-efficacy ( Cho & Shen, 2013 ), have grit and intention to succeed ( Wang & Baker, 2018 ), have time management and elaboration strategies ( Broadbent, 2017 ), set goals and revisit course content ( Kizilcec et al., 2017 ), and persist ( Glazer & Murphy, 2015 ). Researchers found a positive relationship between learner's self-regulation and interaction ( Delen et al., 2014 ) and self-regulation and communication and collaboration ( Barnard et al., 2009 ).
Motivation. Researchers focused on motivation of online learners including different motivation levels of online learners ( Li & Tsai, 2017 ), what motivated online learners ( Chaiprasurt & Esichaikul, 2013 ), differences in motivation of online learners ( Hartnett et al., 2011 ), and motivation when compared to face to face learners ( Paechter & Maier, 2010 ). Harnett et al. (2011) found that online learner motivation was complex, multifaceted, and sensitive to situational conditions.
Academic. Several researchers have focused on academic aspects for online learner characteristics. Readiness for online learning has been examined as an academic factor by several researchers ( Buzdar et al., 2016 ; Dray et al., 2011 ; Wladis & Samuels, 2016 ; Yu, 2018 ) specifically focusing on creating and validating measures to examine online learner readiness including examining students emotional intelligence as a measure of student readiness for online learning. Researchers have also examined other academic factors such as academic standing ( Bradford & Wyatt, 2010 ), course level factors ( Wladis et al., 2014 ) and academic skills in online courses ( Shea & Bidjerano, 2014 ).
Affective. Anderson and Bourke (2013) describe affective characteristics through which learners express feelings or emotions. Several research studies focused on the affective characteristics of online learners. Learner satisfaction for online learning has been examined by several researchers ( Cole et al., 2014 ; Dziuban et al., 2015 ; Kuo et al., 2013 ; Lee, 2014a ) along with examining student emotions towards online assessment ( Kim et al., 2014 ).
Cognitive. Researchers have also examined cognitive aspects of learner characteristics including meta-cognitive skills, cognitive variables, higher-order thinking, cognitive density, and critical thinking ( Chen & Wu, 2012 ; Lee, 2014b ). Lee (2014b) examined the relationship between cognitive presence density and higher-order thinking skills. Chen and Wu (2012) examined the relationship between cognitive and motivational variables in an online system for secondary physical education.
Demographic. Researchers have examined various demographic factors in online learning. Several researchers have examined gender differences in online learning ( Bayeck et al., 2018 ; Lowes et al., 2016 ; Yukselturk & Bulut, 2009 ), ethnicity, age ( Ke & Kwak, 2013 ), and minority status ( Yeboah & Smith, 2016 ) of online learners.
While engagement and learner characteristics were studied the most, other themes were less often studied in the literature and are presented here, according to size, with general descriptions of the types of research examined for each.
Evaluation and Quality Assurance. There were 38 studies (6.14%) published in the theme of evaluation and quality assurance. Some of the studies in this theme focused on course quality standards, using quality matters to evaluate quality, using the CIPP model for evaluation, online learning system evaluation, and course and program evaluations.
Course Technologies. There were 35 studies (5.65%) published in the course technologies theme. Some of the studies examined specific technologies such as Edmodo, YouTube, Web 2.0 tools, wikis, Twitter, WebCT, Screencasts, and Web conferencing systems in the online learning context.
Course Facilitation. There were 34 studies (5.49%) published in the course facilitation theme. Some of the studies in this theme examined facilitation strategies and methods, experiences of online facilitators, and online teaching methods.
Institutional Support. There were 33 studies (5.33%) published in the institutional support theme which included support for both the instructor and learner. Some of the studies on instructor support focused on training new online instructors, mentoring programs for faculty, professional development resources for faculty, online adjunct faculty training, and institutional support for online instructors. Studies on learner support focused on learning resources for online students, cognitive and social support for online learners, and help systems for online learner support.
Learner Outcome. There were 32 studies (5.17%) published in the learner outcome theme. Some of the studies that were examined in this theme focused on online learner enrollment, completion, learner dropout, retention, and learner success.
Course Assessment. There were 30 studies (4.85%) published in the course assessment theme. Some of the studies in the course assessment theme examined online exams, peer assessment and peer feedback, proctoring in online exams, and alternative assessments such as eportfolio.
Access, Culture, Equity, Inclusion, and Ethics. There were 29 studies (4.68%) published in the access, culture, equity, inclusion, and ethics theme. Some of the studies in this theme examined online learning across cultures, multi-cultural effectiveness, multi-access, and cultural diversity in online learning.
Leadership, Policy, and Management. There were 27 studies (4.36%) published in the leadership, policy, and management theme. Some of the studies on leadership, policy, and management focused on online learning leaders, stakeholders, strategies for online learning leadership, resource requirements, university policies for online course policies, governance, course ownership, and faculty incentives for online teaching.
Course Design and Development. There were 27 studies (4.36%) published in the course design and development theme. Some of the studies examined in this theme focused on design elements, design issues, design process, design competencies, design considerations, and instructional design in online courses.
Instructor Characteristics. There were 21 studies (3.39%) published in the instructor characteristics theme. Some of the studies in this theme were on motivation and experiences of online instructors, ability to perform online teaching duties, roles of online instructors, and adjunct versus full-time online instructors.
The research methods used in the studies were classified into quantitative, qualitative, and mixed methods ( Harwell, 2012 , pp. 147–163). The research setting was categorized into higher education, continuing education, K-12, and corporate/military. As shown in Table A in the appendix, the vast majority of the publications used higher education as the research setting ( n = 509, 67.6%). Table B in the appendix shows that approximately half of the studies adopted the quantitative method ( n = 324, 43.03%), followed by the qualitative method ( n = 200, 26.56%). Mixed methods account for the smallest portion ( n = 95, 12.62%).
Table A shows that the patterns of the four research settings were approximately consistent across the 12 themes except for the theme of Leaner Outcome and Institutional Support. Continuing education had a higher relative frequency in Learner Outcome (0.28) and K-12 had a higher relative frequency in Institutional Support (0.33) compared to the frequencies they had in the total themes (0.09 and 0.08 respectively). Table B in the appendix shows that the distribution of the three methods were not consistent across the 12 themes. While quantitative studies and qualitative studies were roughly evenly distributed in Engagement, they had a large discrepancy in Learner Characteristics. There were 100 quantitative studies; however, only 18 qualitative studies published in the theme of Learner Characteristics.
In summary, around 8% of the articles published in the 12 journals focus on online learning. Online learning publications showed a tendency of increase on the whole in the past decade, albeit fluctuated, with the greatest number occurring in 2018. Among the 12 research themes related to online learning, the themes of Engagement and Learner Characteristics were studied the most and the theme of Instructor Characteristics was studied the least. Most studies were conducted in the higher education setting and approximately half of the studies used the quantitative method. Looking at the 12 themes by setting and method, we found that the patterns of the themes by setting or by method were not consistent across the 12 themes.
The quality of our findings was ensured by scientific and thorough searches and coding consistency. The selection of the 12 journals provides evidence of the representativeness and quality of primary studies. In the coding process, any difficulties and questions were resolved by consultations with the research team at bi-weekly meetings, which ensures the intra-rater and interrater reliability of coding. All these approaches guarantee the transparency and replicability of the process and the quality of our results.
This review enabled us to identify the online learning research themes examined from 2009 to 2018. In the section below, we review the most studied research themes, engagement and learner characteristics along with implications, limitations, and directions for future research.
Three out of the four systematic reviews informing the design of the present study found that online learner characteristics and online engagement were examined in a high number of studies. In this review, about half of the studies reviewed (50.57%) focused on online learner characteristics or online engagement. This shows the continued importance of these two themes. In the Tallent-Runnels et al.’s (2006) study, the learner characteristics theme was identified as least studied for which they state that researchers are beginning to investigate learner characteristics in the early days of online learning.
One of the differences found in this review is that course design and development was examined in the least number of studies in this review compared to two prior systematic reviews ( Berge & Mrozowski, 2001 ; Zawacki-Richter et al., 2009 ). Zawacki-Richter et al. did not use a keyword search but reviewed all the articles in five different distance education journals. Berge and Mrozowski (2001) included a research theme called design issues to include all aspects of instructional systems design in distance education journals. In our study, in addition to course design and development, we also had focused themes on learner outcomes, course facilitation, course assessment and course evaluation. These are all instructional design focused topics and since we had multiple themes focusing on instructional design topics, the course design and development category might have resulted in fewer studies. There is still a need for more studies to focus on online course design and development.
Three out of the four systematic reviews discussed in the opening of this study found management and organization factors to be least studied. In this review, Leadership, Policy, and Management was studied among 4.36% of the studies and Access, Culture, Equity, Inclusion, and Ethics was studied among 4.68% of the studies in the organizational level. The theme on Equity and accessibility was also found to be the least studied theme in the Berge and Mrozowski (2001) study. In addition, instructor characteristics was the least examined research theme among the twelve themes studied in this review. Only 3.39% of the studies were on instructor characteristics. While there were some studies examining instructor motivation and experiences, instructor ability to teach online, online instructor roles, and adjunct versus full-time online instructors, there is still a need to examine topics focused on instructors and online teaching. This theme was not included in the prior reviews as the focus was more on the learner and the course but not on the instructor. While it is helpful to see research evolving on instructor focused topics, there is still a need for more research on the online instructor.
The research themes from this review were compared with research themes from previous systematic reviews, which targeted prior decades. Table 8 shows the comparison.
Comparison of most and least studied online learning research themes from current to previous reviews.
Level | 1990–1999 ( ) | 1993–2004 ( ) | 2000–2008 ( ) | 2009–2018 (Current Study) | |
---|---|---|---|---|---|
Learner Characteristics | L | X | X | X | |
Engagement and Interaction | L | X | X | X | |
Design Issues/Instructional Design | C | X | X | ||
Course Environment Learner Outcomes | C L | X X | |||
Learner Support | L | X | |||
Equity and Accessibility | O | X | X | ||
Institutional& Administrative Factors | O | X | X | ||
Management and Organization | O | X | X | ||
Cost-Benefit | O | X |
L = Learner, C=Course O=Organization.
In this review there is a greater concentration of studies focused on Learner domain topics, and reduced attention to broader more encompassing research themes that fall into the Course and Organization domains. There is a need for organizational level topics such as Access, Culture, Equity, Inclusion and Ethics, and Leadership, Policy and Management to be researched on within the context of online learning. Examination of access, culture, equity, inclusion and ethics is very important to support diverse online learners, particularly with the rapid expansion of online learning across all educational levels. This was also least studied based on Berge and Mrozowski (2001) systematic review.
The topics on leadership, policy and management were least studied both in this review and also in the Tallent-Runnels et al. (2006) and Zawacki-Richter et al. (2009) study. Tallent-Runnels categorized institutional and administrative aspects into institutional policies, institutional support, and enrollment effects. While we included support as a separate category, in this study leadership, policy and management were combined. There is still a need for research on leadership of those who manage online learning, policies for online education, and managing online programs. In the Zawacki-Richter et al. (2009) study, only a few studies examined management and organization focused topics. They also found management and organization to be strongly correlated with costs and benefits. In our study, costs and benefits were collectively included as an aspect of management and organization and not as a theme by itself. These studies will provide research-based evidence for online education administrators.
As with any systematic review, there are limitations to the scope of the review. The search is limited to twelve journals in the field that typically include research on online learning. These manuscripts were identified by searching the Education Research Complete database which focuses on education students, professionals, and policymakers. Other discipline-specific journals as well as dissertations and proceedings were not included due to the volume of articles. Also, the search was performed using five search terms “online learning" OR "online teaching" OR "online program" OR "online course" OR “online education” in title and keyword. If authors did not include these terms, their respective work may have been excluded from this review even if it focused on online learning. While these terms are commonly used in North America, it may not be commonly used in other parts of the world. Additional studies may exist outside this scope.
The search strategy also affected how we presented results and introduced limitations regarding generalization. We identified that only 8% of the articles published in these journals were related to online learning; however, given the use of search terms to identify articles within select journals it was not feasible to identify the total number of research-based articles in the population. Furthermore, our review focused on the topics and general methods of research and did not systematically consider the quality of the published research. Lastly, some journals may have preferences for publishing studies on a particular topic or that use a particular method (e.g., quantitative methods), which introduces possible selection and publication biases which may skew the interpretation of results due to over/under representation. Future studies are recommended to include more journals to minimize the selection bias and obtain a more representative sample.
Certain limitations can be attributed to the coding process. Overall, the coding process for this review worked well for most articles, as each tended to have an individual or dominant focus as described in the abstracts, though several did mention other categories which likely were simultaneously considered to a lesser degree. However, in some cases, a dominant theme was not as apparent and an effort to create mutually exclusive groups for clearer interpretation the coders were occasionally forced to choose between two categories. To facilitate this coding, the full-texts were used to identify a study focus through a consensus seeking discussion among all authors. Likewise, some studies focused on topics that we have associated with a particular domain, but the design of the study may have promoted an aggregated examination or integrated factors from multiple domains (e.g., engagement). Due to our reliance on author descriptions, the impact of construct validity is likely a concern that requires additional exploration. Our final grouping of codes may not have aligned with the original author's description in the abstract. Additionally, coding of broader constructs which disproportionately occur in the Learner domain, such as learner outcomes, learner characteristics, and engagement, likely introduced bias towards these codes when considering studies that involved multiple domains. Additional refinement to explore the intersection of domains within studies is needed.
One of the strengths of this review is the research categories we have identified. We hope these categories will support future researchers and identify areas and levels of need for future research. Overall, there is some agreement on research themes on online learning research among previous reviews and this one, at the same time there are some contradicting findings. We hope the most-researched themes and least-researched themes provide authors a direction on the importance of research and areas of need to focus on.
The leading themes found in this review is online engagement research. However, presentation of this research was inconsistent, and often lacked specificity. This is not unique to online environments, but the nuances of defining engagement in an online environment are unique and therefore need further investigation and clarification. This review points to seven distinct classifications of online engagement. Further research on engagement should indicate which type of engagement is sought. This level of specificity is necessary to establish instruments for measuring engagement and ultimately testing frameworks for classifying engagement and promoting it in online environments. Also, it might be of importance to examine the relationship between these seven sub-themes of engagement.
Additionally, this review highlights growing attention to learner characteristics, which constitutes a shift in focus away from instructional characteristics and course design. Although this is consistent with the focus on engagement, the role of the instructor, and course design with respect to these outcomes remains important. Results of the learner characteristics and engagement research paired with course design will have important ramifications for the use of teaching and learning professionals who support instruction. Additionally, the review also points to a concentration of research in the area of higher education. With an immediate and growing emphasis on online learning in K-12 and corporate settings, there is a critical need for further investigation in these settings.
Lastly, because the present review did not focus on the overall effect of interventions, opportunities exist for dedicated meta-analyses. Particular attention to research on engagement and learner characteristics as well as how these vary by study design and outcomes would be logical additions to the research literature.
This systematic review builds upon three previous reviews which tackled the topic of online learning between 1990 and 2010 by extending the timeframe to consider the most recent set of published research. Covering the most recent decade, our review of 619 articles from 12 leading online learning journal points to a more concentrated focus on the learner domain including engagement and learner characteristics, with more limited attention to topics pertaining to the classroom or organizational level. The review highlights an opportunity for the field to clarify terminology concerning online learning research, particularly in the areas of learner outcomes where there is a tendency to classify research more generally (e.g., engagement). Using this sample of published literature, we provide a possible taxonomy for categorizing this research using subcategories. The field could benefit from a broader conversation about how these categories can shape a comprehensive framework for online learning research. Such efforts will enable the field to effectively prioritize research aims over time and synthesize effects.
Florence Martin: Conceptualization; Writing - original draft, Writing - review & editing Preparation, Supervision, Project administration. Ting Sun: Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Carl Westine: Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Supervision
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
1 Includes articles that are cited in this manuscript and also included in the systematic review. The entire list of 619 articles used in the systematic review can be obtained by emailing the authors.*
Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.compedu.2020.104009 .
Research Themes by the Settings in the Online Learning Publications
Research Theme | Higher Ed ( = 506) | Continuing Education ( = 58) | K-12 ( = 53) | Corporate/Military ( = 3) |
---|---|---|---|---|
Engagement | 153 | 15 | 12 | 0 |
Presence | 46 | 2 | 3 | 0 |
Interaction | 35 | 4 | 4 | 0 |
Community | 19 | 2 | 4 | 0 |
Participation | 16 | 5 | 0 | 0 |
Collaboration | 16 | 1 | 0 | 0 |
Involvement | 13 | 0 | 1 | 0 |
Communication | 8 | 1 | 0 | 0 |
Learner Characteristics | 106 | 18 | 9 | 1 |
Self-regulation Characteristics | 43 | 9 | 2 | 0 |
Motivation Characteristics | 18 | 3 | 2 | 0 |
Academic Characteristics | 17 | 0 | 2 | 0 |
Affective Characteristics | 12 | 3 | 1 | 1 |
Cognitive Characteristics | 11 | 1 | 2 | 0 |
Demographic Characteristics | 5 | 2 | 0 | 0 |
Evaluation and Quality Assurance | 33 | 3 | 2 | 0 |
Course Technologies | 33 | 2 | 0 | 0 |
Course Facilitation | 30 | 3 | 1 | 0 |
Institutional Support | 24 | 0 | 8 | 1 |
Learner Outcome | 24 | 7 | 1 | 0 |
Course Assessment | 23 | 2 | 5 | 0 |
Access, Culture, Equity, Inclusion and Ethics | 26 | 1 | 2 | 0 |
Leadership, Policy and Management | 17 | 5 | 5 | 0 |
Course Design and Development | 21 | 1 | 4 | 1 |
Instructor Characteristics | 16 | 1 | 4 | 0 |
Research Themes by the Methodology in the Online Learning Publications
Research Theme | Mixed Method ( = 95) | Quantitative ( = 324) | Qualitative ( = 200) |
---|---|---|---|
Engagement | 32 | 78 | 69 |
Presence | 11 | 25 | 14 |
Interaction | 9 | 20 | 14 |
Community | 2 | 9 | 14 |
Participation | 6 | 8 | 7 |
Collaboration | 2 | 5 | 10 |
Involvement | 2 | 6 | 6 |
Communication | 0 | 5 | 4 |
Learner Characteristics | 16 | 100 | 18 |
Self-regulation Characteristics | 5 | 43 | 6 |
Motivation Characteristics | 4 | 15 | 4 |
Academic Characteristics | 1 | 15 | 3 |
Affective Characteristics | 2 | 12 | 3 |
Cognitive Characteristics | 4 | 8 | 2 |
Demographic Characteristics | 1 | 6 | 0 |
Evaluation and Quality Assurance | 5 | 22 | 11 |
Course Technologies | 4 | 20 | 11 |
Course Facilitation | 7 | 14 | 13 |
Institutional Support | 12 | 9 | 12 |
Learner Outcome | 3 | 23 | 6 |
Course Assessment | 5 | 20 | 5 |
Access, Culture, Equity, Inclusion & Ethics | 3 | 13 | 13 |
Leadership, Policy and Management | 5 | 9 | 13 |
Course Design and Development | 2 | 8 | 17 |
Instructor Characteristics | 1 | 8 | 12 |
The following are the Supplementary data to this article:
BMC Medical Education volume 21 , Article number: 495 ( 2021 ) Cite this article
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The COVID-19 pandemic forced dental schools to close their campuses and move didactic instruction online. The abrupt transition to online learning, however, has raised several issues that have not been resolved. While several studies have investigated dental students’ attitude towards online learning during the pandemic, mixed results have been reported. Additionally, little research has been conducted to identify and understand factors, especially pedagogical factors, that impacted students’ acceptance of online learning during campus closure. Furthermore, how online learning during the pandemic impacted students’ learning performance has not been empirically investigated. In March 2020, the dental school studied here moved didactic instruction online in response to government issued stay-at-home orders. This first-of-its-kind comparative study examined students’ perceived effectiveness of online courses during summer quarter 2020, explored pedagogical factors impacting their acceptance of online courses, and empirically evaluated the impact of online learning on students’ course performance, during the pandemic.
The study employed a quasi-experimental design. Participants were 482 pre-doctoral students in a U.S dental school. Students’ perceived effectiveness of online courses during the pandemic was assessed with a survey. Students’ course grades for online courses during summer quarter 2020 were compared with that of a control group who received face-to-face instruction for the same courses before the pandemic in summer quarter 2019.
Survey results revealed that most online courses were well accepted by the students, and 80 % of them wanted to continue with some online instruction post pandemic. Regression analyses revealed that students’ perceived engagement with faculty and classmates predicted their perceived effectiveness of the online course. More notably, Chi Square tests demonstrated that in 16 out of the 17 courses compared, the online cohort during summer quarter 2020 was equally or more likely to get an A course grade than the analogous face-to-face cohort during summer quarter 2019.
This is the first empirical study in dental education to demonstrate that online courses during the pandemic could achieve equivalent or better student course performance than the same pre-pandemic in-person courses. The findings fill in gaps in literature and may inform online learning design moving forward.
Peer Review reports
Research across disciplines has demonstrated that well-designed online learning can lead to students’ enhanced motivation, satisfaction, and learning [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. A report by the U.S. Department of Education [ 8 ], based on examinations of comparative studies of online and face-to-face versions of the same course from 1996 to 2008, concluded that online learning could produce learning outcomes equivalent to or better than face-to-face learning. The more recent systematic review by Pei and Wu [ 9 ] provided additional evidence that online learning is at least as effective as face-to-face learning for undergraduate medical students.
To take advantage of the opportunities presented by online learning, thought leaders in dental education in the U.S. have advocated for the adoption of online learning in the nation’s dental schools [ 10 , 11 , 12 ]. However, digital innovation has been a slow process in academic dentistry [ 13 , 14 , 15 ]. In March 2020, the COVID-19 pandemic brought unprecedented disruption to dental education by necessitating the need for online learning. In accordance with stay-at-home orders to prevent the spread of the virus, dental schools around the world closed their campuses and moved didactic instruction online.
The abrupt transition to online learning, however, has raised several concerns and question. First, while several studies have examined dental students’ online learning satisfaction during the pandemic, mixed results have been reported. Some studies have reported students’ positive attitude towards online learning [ 15 , 16 , 17 , 18 , 19 , 20 ]. Sadid-Zadeh et al. [ 18 ] found that 99 % of the surveyed dental students at University of Buffalo, in the U.S., were satisfied with live web-based lectures during the pandemic. Schlenz et al. [ 15 ] reported that students in a German dental school had a favorable attitude towards online learning and wanted to continue with online instruction in their future curriculum. Other studies, however, have reported students’ negative online learning experience during the pandemic [ 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, dental students at Harvard University felt that learning during the pandemic had worsened and engagement had decreased [ 23 , 24 ]. In a study with medical and dental students in Pakistan, Abbasi et al. [ 21 ] found that 77 % of the students had negative perceptions about online learning and 84 % reported reduced student-instructor interactions.
In addition to these mixed results, little attention has been given to factors affecting students’ acceptance of online learning during the pandemic. With the likelihood that online learning will persist post pandemic [ 27 ], research in this area is warranted to inform online course design moving forward. In particular, prior research has demonstrated that one of the most important factors influencing students’ performance in any learning environment is a sense of belonging, the feeling of being connected with and supported by the instructor and classmates [ 28 , 29 , 30 , 31 ]. Unfortunately, this aspect of the classroom experience has suffered during school closure. While educational events can be held using a video conferencing system, virtual peer interaction on such platforms has been perceived by medical trainees to be not as easy and personal as physical interaction [ 32 ]. The pandemic highlights the need to examine instructional strategies most suited to the current situation to support students’ engagement with faculty and classmates.
Furthermore, there is considerable concern from the academic community about the quality of online learning. Pre-pandemic, some faculty and students were already skeptical about the value of online learning [ 33 ]. The longer the pandemic lasts, the more they may question the value of online education, asking: Can online learning during the pandemic produce learning outcomes that are similar to face-to-face learning before the pandemic? Despite the documented benefits of online learning prior to the pandemic, the actual impact of online learning during the pandemic on students’ academic performance is still unknown due to reasons outlined below.
On one hand, several factors beyond the technology used could influence the effectiveness of online learning, one of which is the teaching context [ 34 ]. The sudden transition to online learning has posed many challenges to faculty and students. Faculty may not have had adequate time to carefully design online courses to take full advantage of the possibilities of the online format. Some faculty may not have had prior online teaching experience and experienced a deeper learning curve when it came to adopting online teaching methods [ 35 ]. Students may have been at the risk of increased anxiety due to concerns about contracting the virus, on time graduation, finances, and employment [ 36 , 37 ], which may have negatively impacted learning performance [ 38 ]. Therefore, whether online learning during the pandemic could produce learning outcomes similar to those of online learning implemented during more normal times remains to be determined.
Most existing studies on online learning in dental education during the pandemic have only reported students’ satisfaction. The actual impact of the online format on academic performance has not been empirically investigated. The few studies that have examined students’ learning outcomes have only used students’ self-reported data from surveys and focus groups. According to Kaczmarek et al. [ 24 ], 50 % of the participating dental faculty at Harvard University perceived student learning to have worsened during the pandemic and 70 % of the students felt the same. Abbasi et al. [ 21 ] reported that 86 % of medical and dental students in a Pakistan college felt that they learned less online. While student opinions are important, research has demonstrated a poor correlation between students’ perceived learning and actual learning gains [ 39 ]. As we continue to navigate the “new normal” in teaching, students’ learning performance needs to be empirically evaluated to help institutions gauge the impact of this grand online learning experiment.
In March 2020, the University of the Pacific Arthur A. Dugoni School of Dentistry, in the U.S., moved didactic instruction online to ensure the continuity of education during building closure. This study examined students’ acceptance of online learning during the pandemic and its impacting factors, focusing on instructional practices pertaining to students’ engagement/interaction with faculty and classmates. Another purpose of this study was to empirically evaluate the impact of online learning during the pandemic on students’ actual course performance by comparing it with that of a pre-pandemic cohort. To understand the broader impact of the institutional-wide online learning effort, we examined all online courses offered in summer quarter 2020 (July to September) that had a didactic component.
This is the first empirical study in dental education to evaluate students’ learning performance during the pandemic. The study aimed to answer the following three questions.
How well was online learning accepted by students, during the summer quarter 2020 pandemic interruption?
How did instructional strategies, centered around students’ engagement with faculty and classmates, impact their acceptance of online learning?
How did online learning during summer quarter 2020 impact students’ course performance as compared with a previous analogous cohort who received face-to-face instruction in summer quarter 2019?
This study employed a quasi-experimental design. The study was approved by the university’s institutional review board (#2020-68).
The study was conducted at the Arthur A. Dugoni School of Dentistry, University of the Pacific. The program runs on a quarter system. It offers a 3-year accelerated Doctor of Dental Surgery (DDS) program and a 2-year International Dental Studies (IDS) program for international dentists who have obtained a doctoral degree in dentistry from a country outside the U.S. and want to practice in the U.S. Students advance throughout the program in cohorts. IDS students take some courses together with their DDS peers. All three DDS classes (D1/DDS 2023, D2/DDS 2022, and D3/DDS 2021) and both IDS classes (I1/IDS 2022 and I2/IDS 2021) were invited to participate in the study. The number of students in each class was: D1 = 145, D2 = 143, D3 = 143, I1 = 26, and I2 = 25. This resulted in a total of 482 student participants.
During campus closure, faculty delivered remote instruction in various ways, including live online classes via Zoom @ [ 40 ], self-paced online modules on the school’s learning management system Canvas @ [ 41 ], or a combination of live and self-paced delivery. For self-paced modules, students studied assigned readings and/or viewings such as videos and pre-recorded slide presentations. Some faculty also developed self-paced online lessons with SoftChalk @ [ 42 ], a cloud-based platform that supports the inclusion of gamified learning by insertion of various mini learning activities. The SoftChalk lessons were integrated with Canvas @ [ 41 ] and faculty could monitor students’ progress. After students completed the pre-assigned online materials, some faculty held virtual office hours or live online discussion sessions for students to ask questions and discuss key concepts.
Student survey.
Students’ perceived effectiveness of summer quarter 2020 online courses was evaluated by the school’s Office of Academic Affairs in lieu of the regular course evaluation process. A total of 19 courses for DDS students and 10 courses for IDS students were evaluated. An 8-question survey developed by the researchers (Additional file 1 ) was administered online in the last week of summer quarter 2020. Course directors invited student to take the survey during live online classes. The survey introduction stated that taking the survey was voluntary and that their anonymous responses would be reported in aggregated form for research purposes. Students were invited to continue with the survey if they chose to participate; otherwise, they could exit the survey. The number of students in each class who took the survey was as follows: D1 ( n = 142; 98 %), D2 ( n = 133; 93 %), D3 ( n = 61; 43 %), I1 ( n = 23; 88 %), and I2 ( n = 20; 80 %). This resulted in a total of 379 (79 %) respondents across all classes.
The survey questions were on a 4-point scale, ranging from Strongly Disagree (1 point), Disagree (2 points), Agree (3 points), and Strongly Agree (4 points). Students were asked to rate each online course by responding to four statements: “ I could fully engage with the instructor and classmates in this course”; “The online format of this course supported my learning”; “Overall this online course is effective.”, and “ I would have preferred face-to-face instruction for this course ”. For the first three survey questions, a higher mean score indicated a more positive attitude toward the online course. For the fourth question “ I would have preferred face-to-face instruction for this course ”, a higher mean score indicated that more students would have preferred face-to-face instruction for the course. Two additional survey questions asked students to select their preferred online delivery method for fully online courses during the pandemic from three given choices (synchronous online/live, asynchronous online/self-paced, and a combination of both), and to report whether they wanted to continue with some online instruction post pandemic. Finally, two open-ended questions at the end of the survey allowed students to comment on the aspects of online format that they found to be helpful and to provide suggestion for improvement. For the purpose of this study, we focused on the quantitative data from the Likert-scale questions.
Descriptive data such as the mean scores were reported for each course. Regression analyses were conducted to examine the relationship between instructional strategies focusing on students’ engagement with faculty and classmates, and their overall perceived effectiveness of the online course. The independent variable was student responses to the question “ I could fully engage with the instructor and classmates in this course ”, and the dependent variable was their answer to the question “ Overall, this online course is effective .”
Using Chi-square tests, student course grade distributions (A, B, C, D, and F) for summer quarter 2020 online courses were compared with that of a previous cohort who received face-to-face instruction for the same course in summer quarter 2019. Note that as a result of the school’s pre-doctoral curriculum redesign implemented in July 2019, not all courses offered in summer quarter 2020 were offered in the previous year in summer quarter 2019. In other words, some of the courses offered in summer quarter 2020 were new courses offered for the first time. Because these new courses did not have a previous face-to-face version to compare to, they were excluded from data analysis. For some other courses, while course content remained the same between 2019 and 2020, the sequence of course topics within the course had changed. These courses were also excluded from data analysis.
After excluding the aforementioned courses, it resulted in a total of 17 “comparable” courses that were included in data analysis (see the subsequent section). For these courses, the instructor, course content, and course goals were the same in both 2019 and 2020. The assessment methods and grading policies also remained the same through both years. For exams and quizzes, multiple choice questions were the dominating format for both years. While some exam questions in 2020 were different from 2019, faculty reported that the overall exam difficulty level was similar. The main difference in assessment was testing conditions. The 2019 cohort took computer-based exams in the physical classroom with faculty proctoring, and the 2020 cohort took exams at home with remote proctoring to ensure exam integrity. The remote proctoring software monitored the student during the exam through a web camera on their computer/laptop. The recorded video file flags suspicious activities for faculty review after exam completion.
Table 1 summarized data on DDS students’ perceived effectiveness of each online course during summer quarter 2020. For the survey question “ Overall, this online course is effective ”, the majority of courses received a mean score that was approaching or over 3 points on the 4-point scale, suggesting that online learning was generally well accepted by students. Despite overall positive online course experiences, for many of the courses examined, there was an equal split in student responses to the question “ I would have preferred face-to-face instruction for this course .” Additionally, for students’ preferred online delivery method for fully online courses, about half of the students in each class preferred a combination of synchronous and asynchronous online learning (see Fig. 1 ). Finally, the majority of students wanted faculty to continue with some online instruction post pandemic: D1class (110; 78.60 %), D2 class (104; 80 %), and D3 class (49; 83.10 %).
While most online courses received favorable ratings, some variations did exist among courses. For D1 courses, “ Anatomy & Histology ” received lower ratings than others. This could be explained by its lab component, which didn’t lend itself as well to the online format. For D2 courses, several of them received lower ratings than others, especially for the survey question on students’ perceived engagement with faculty and classmates.
DDS students’ preferred online delivery method for fully online courses
Table 2 summarized IDS students’ perceived effectiveness of each online course during summer quarter 2020. For the survey question “ Overall, this online course is effective ”, all courses received a mean score that was approaching or over 3 points on a 4-point scale, suggesting that online learning was well accepted by students. For the survey question “ I would have preferred face-to-face instruction for this course ”, for most online courses examined, the percentage of students who would have preferred face-to-face instruction was similar to that of students who preferred online instruction for the course. Like their DDS peers, about half of the IDS students in each class also preferred a combination of synchronous and asynchronous online delivery for fully online courses (See Fig. 2 ). Finally, the majority of IDS students (I1, n = 18, 81.80 %; I2, n = 16, 84.20 %) wanted to continue with some online learning after the pandemic is over.
IDS students’ preferred online delivery method for fully online courses
For all 19 online courses taken by DDS students, regression analyses indicated that there was a significantly positive relationship between students’ perceived engagement with faculty and classmates and their perceived effectiveness of the course. P value was 0.00 across all courses. The ranges of effect size (r 2 ) were: D1 courses (0.26 to 0.50), D2 courses (0.39 to 0.650), and D3 courses (0.22 to 0.44), indicating moderate to high correlations across courses.
For 9 out of the 10 online courses taken by IDS students, there was a positive relationship between students’ perceived engagement with faculty and classmates and their perceived effectiveness of the course. P value was 0.00 across courses. The ranges of effect size were: I1 courses (0.35 to 0.77) and I2 courses (0.47 to 0.63), indicating consistently high correlations across courses. The only course in which students’ perceived engagement with faculty and classmates didn’t predict perceived effective of the course was “ Integrated Clinical Science III (ICS III) ”, which the I2 class took together with their D3 peers.
Chi square test results (Table 3 ) indicated that in 4 out of the 17 courses compared, the online cohort during summer quarter 2020 was more likely to receive an A grade than the face-to-face cohort during summer quarter 2019. In 12 of the courses, the online cohort were equally likely to receive an A grade as the face-to-face cohort. In the remaining one course, the online cohort was less likely to receive an A grade than the face-to-face cohort.
Survey results revealed that students had generally positive perceptions about online learning during the pandemic and the majority of them wanted to continue with some online learning post pandemic. Overall, our findings supported several other studies in dental [ 18 , 20 ], medical [ 43 , 44 ], and nursing [ 45 ] education that have also reported students’ positive attitudes towards online learning during the pandemic. In their written comments in the survey, students cited enhanced flexibility as one of the greatest benefits of online learning. Some students also commented that typing questions in the chat box during live online classes was less intimidating than speaking in class. Others explicitly stated that not having to commute to/from school provided more time for sleep, which helped with self-care and mental health. Our findings are in line with previous studies which have also demonstrated that online learning offered higher flexibility [ 46 , 47 ]. Meanwhile, consistent with findings of other researchers [ 19 , 21 , 46 ], our students felt difficulty engaging with faculty and classmates in several online courses.
There were some variations among individual courses in students’ acceptance of the online format. One factor that could partially account for the observed differences was instructional strategies. In particular, our regression analysis results demonstrated a positive correlation between students’ perceived engagement with faculty and classmates and their perceived overall effectiveness of the online course. Other aspects of course design might also have influenced students’ overall rating of the online course. For instance, some D2 students commented that the requirements of the course “ Integrated Case-based Seminars (ICS II) ” were not clear and that assessment did not align with lecture materials. It is important to remember that communicating course requirements clearly and aligning course content and assessment are principles that should be applied in any course, whether face-to-face or online. Our results highlighted the importance of providing faculty training on basic educational design principles and online learning design strategies. Furthermore, the nature of the course might also have impacted student ratings. For example, D1 course “ Anatomy and Histology ” had a lab component, which did not lend itself as well to the online format. Many students reported that it was difficult to see faculty’s live demonstration during Zoom lectures, which may have resulted in a lower student satisfaction rating.
As for students’ preferred online delivery method for fully online courses during the pandemic, about half of them preferred a combination of synchronous and asynchronous online learning. In light of this finding, as we continue with remote learning until public health directives allow a return to campus, we will encourage faculty to integrate these two online delivery modalities. Finally, in view of the result that over 80 % of the students wanted to continue with some online instruction after the pandemic, the school will advocate for blended learning in the post-pandemic world [ 48 ]. For future face-to-face courses on campus after the pandemic, faculty are encouraged to deliver some content online to reduce classroom seat time and make learning more flexible. Taken together, our findings not only add to the overall picture of the current situation but may inform learning design moving forward.
To reiterate, we found that students’ perceived engagement with faculty and classmates predicted their perceived overall effectiveness of the online course. This aligns with the larger literature on best practices in online learning design. Extensive research prior to the pandemic has confirmed that the effectiveness of online learning is determined by a number of factors beyond the tools used, including students’ interactions with the instructor and classmates [ 49 , 50 , 51 , 52 ]. Online students may feel isolated due to reduced or lack of interaction [ 53 , 54 ]. Therefore, in designing online learning experiences, it is important to remember that learning is a social process [ 55 ]. Faculty’s role is not only to transmit content but also to promote the different types of interactions that are an integral part of the online learning process [ 33 ]. The online teaching model in which faculty uploads materials online but teach it in the same way as in the physical classroom, without special effort to engage students, doesn’t make the best use of the online format. Putting the “sage on the screen” during a live class meeting on a video conferencing system is not different from “sage on the stage” in the physical classroom - both provide limited space for engagement. Such one-way monologue devalues the potentials that online learning presents.
In light of the critical role that social interaction plays in online learning, faculty are encouraged to use the interactive features of online learning platforms to provide clear channels for student-instructor and student-student interactions. In the open-ended comments, students highlighted several instructional strategies that they perceived to be helpful for learning. For live online classes, these included conducting breakout room activities, using the chat box to facilitate discussions, polling, and integrating gameplay with apps such as Kahoot! @ [ 56 ]. For self-paced classes, students appreciated that faculty held virtual office hours or subsequent live online discussion sessions to reinforce understanding of the pre-assigned materials.
This study provided empirical evidence in dental education that it was possible to ensure the continuity of education without sacrificing the quality of education provided to students during forced migration to distance learning upon building closure. To reiterate, in all but one online course offered in summer quarter 2020, students were equally or more likely to get an A grade than the face-to-face cohort from summer quarter 2019. Even for courses that had less student support for the online format (e.g., the D1 course “ Anatomy and Histology ”), there was a significant increase in the number of students who earned an A grade in 2020 as compared with the previous year. The reduced capacity for technical training during the pandemic may have resulted in more study time for didactic content. Overall, our results resonate with several studies in health sciences education before the pandemic that the quality of learning is comparable in face-to-face and online formats [ 9 , 57 , 58 ]. For the only course ( Integrated Case-based Seminars ICS II) in which the online cohort had inferior performance than the face-to-face cohort, as mentioned earlier, students reported that assessment was not aligned with course materials and that course expectations were not clear. This might explain why students’ course performance was not as strong as expected.
This study used a pre-existing control group from the previous year. There may have been individual differences between students in the online and the face-to-face cohorts, such as motivation, learning style, and prior knowledge, that could have impacted the observed outcomes. Additionally, even though course content and assessment methods were largely the same in 2019 and 2020, changes in other aspects of the course could have impacted students’ course performance. Some faculty may have been more compassionate with grading (e.g., more flexible with assignment deadlines) in summer quarter 2020 given the hardship students experienced during the pandemic. On the other hand, remote proctoring in summer quarter 2020 may have heightened some students’ exam anxiety knowing that they were being monitored through a webcam. The existence and magnitude of effect of these factors needs to be further investigated.
This present study only examined the correlation between students’ perceived online engagement and their perceived overall effectiveness of the online course. Other factors that might impact their acceptance of the online format need to be further researched in future studies. Another future direction is to examine how students’ perceived online engagement correlates with their actual course performance. Because the survey data collected for our present study are anonymous, we cannot match students’ perceived online engagement data with their course grades to run this additional analysis. It should also be noted that this study was focused on didactic online instruction. Future studies might examine how technical training was impacted during the COVID building closure. It was also out of the scope of this study to examine how student characteristics, especially high and low academic performance as reflected by individual grades, affects their online learning experience and performance. We plan to conduct a follow-up study to examine which group of students are most impacted by the online format. Finally, this study was conducted in a single dental school, and so the findings may not be generalizable to other schools and disciplines. Future studies could be conducted in another school or disciplines to compare results.
This study revealed that dental students had generally favorable attitudes towards online learning during the COVID-19 pandemic and that their perceived engagement with faculty and classmates predicted their acceptance of the online course. Most notably, this is the first study in dental education to demonstrate that online learning during the pandemic could achieve similar or better learning outcomes than face-to-face learning before the pandemic. Findings of our study could contribute significantly to the literature on online learning during the COVID-19 pandemic in health sciences education. The results could also inform future online learning design as we re-envision the future of online learning.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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MZ is an Associate Professor of Learning Sciences and Senior Instructional Designer at School of Dentistry, University of the Pacific. She has a PhD in Education, with a specialty on learning sciences and technology. She has dedicated her entire career to conducting research on online learning, learning technology, and faculty development. Her research has resulted in several peer-reviewed publications in medical, dental, and educational technology journals. MZ has also presented regularly at national conferences.
DB is an Assistant Dean for Academic Affairs at School of Dentistry, University of the Pacific. He has an EdD degree in education, with a concentration on learning and instruction. Over the past decades, DB has been overseeing and delivering faculty pedagogical development programs to dental faculty. His research interest lies in educational leadership and instructional innovation. DB has co-authored several peer-reviewed publications in health sciences education and presented regularly at national conferences.
CL is Associate Dean of Oral Healthcare Education, School of Dentistry, University of the Pacific. She has a Doctor of Dental Surgery (DDS) degree and an EdD degree with a focus on educational leadership. Her professional interest lies in educational leadership, oral healthcare education innovation, and faculty development. CL has co-authored several publications in peer-reviewed journals in health sciences education and presented regularly at national conferences.
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MZ analyzed the data and wrote the initial draft of the manuscript. DB and CL both provided assistance with research design, data collection, and reviewed and edited the manuscript. The author(s) read and approved the final manuscript.
Correspondence to Meixun Zheng .
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Zheng, M., Bender, D. & Lyon, C. Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: empirical evidence from a school-wide comparative study. BMC Med Educ 21 , 495 (2021). https://doi.org/10.1186/s12909-021-02909-z
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This research aims to explore and investigate potential factors influencing students’ academic achievements and satisfaction with using online learning platforms. This study was constructed based on Transactional Distance Theory (TDT) and Bloom’s Taxonomy Theory (BTT). This study was conducted on 243 students using online learning platforms in higher education. This research utilized a quantitative research method. The model of this research illustrates eleven factors on using online learning platforms to improve students’ academic achievements and satisfaction. The findings showed that the students’ background, experience, collaborations, interactions, and autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, remembering, understanding, analyzing, and satisfaction was positively aligned with students’ academic achievements. Consequently, the empirical findings present a strong support to the integrative association between TDT and BTT theories in relation to using online learning platforms to improve students’ academic achievements and satisfaction, which could help decision makers in universities and higher education and colleges to plan, evaluate, and implement online learning platforms in their institutions.
Higher education organizations over the previous two decades have offered full courses online as an integral part of their curricula, besides encouraging the completion throughout the online courses. Additionally, the number of students who are not participating in any courses online has continued to drop over the past few years. Similarly, it is perfectly possible to state that learning online is obviously an educational platform (Allen, Seaman, Poulin, & Straut, 2016 ). Courses online are trying to connect social networking components, experts’ content, because online resources are growing on daily basis. Such courses depend on active participation of a significant number of learners who participate independently in accordance with their education objectives, skills, and previous background and experience (McAuley, Stewart, Siemens, & Cormier, 2010 ). Nevertheless, learners differ in their previous background and experience, along with their education techniques, which clearly influence their online courses results besides their achievement (Kauffman, 2015 ). Consequently, despite the online learning evolution, learning online possibly will not be appropriate for each learner (Bouhnik & Carmi, 2013 ). Nevertheless, while online learning application among academic world has grown rapidly, not enough is identified regarding learners’ previous background and experience in learning online. Not so long ago, investigation concentrated on particular characteristics of learners’ experiences along with beliefs, for instance collaboration with their own instructor, online course quality, or studying with a certain learning management system (LMS) (Alexander & Golja, 2007 ; (Lester & King, 2009 ). Generally, limited courses or a single institution were investigated (Coates, James, & Baldwin, 2005 ; Lee, Yoon, & Lee, 2009 ). Few studies examined bigger sample sizes between one or more particular institutes (Alexander & Golja, 2007 ). Additionally, there is a shortage of researches that examine learners’ previous background and experience comparing face-to-face along with learning online elements, e.g., (Bliuc, Goodyear, & Ellis, 2007 ). The development of learners’ previous background and experience, skills, are realized to be the major advantages for administrative level for learning online.
Similarly, learners’ satisfaction and academic achievement towards learning online attracted considerable attention from scholars who employed several theoretical models in order to evaluate learners’ satisfaction and academic achievements (Abuhassna, Megat, Yahaya, Azlina, & Al-rahmi, 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi, Othman, & Yusuf, 2015a ; Al-Rahmi, Othman, & Yusuf, 2015b ). This present study highlights the effects of online learning platforms on student’s satisfaction, in relation to their background and prior experiences towards online learning platforms to identify learners that are going to be satisfied toward online course. Furthermore, this research explores the effects of transactional distance theory (TDT); student collaboration, student- instructor dialogue or communication, and student autonomy in relation to their satisfaction. Accordingly, this study investigates students’ academic achievements within online platforms, utilizing Bloom theory to measure students’ achievements through four main components, namely, understanding, remembering, applying, and analyzing. This study could have a significant influence on online course design and development. Additionally, this research may influence not only academic online courses but then other educational organizations according to the fact that several organizations offer training courses and solutions online. Both researchers and Instructors will be able to utilize and elaborate in accordance with the preliminary model, which was developed throughout this research, on the effects of online platforms on student’s satisfaction and academic achievements. Advantages of online learning and along with its applications were mentioned in earlier correlated literature (Abuhassna et al., 2020 ;Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). However, despite the growing usage of online platforms, there is a shortage of employing this technology, which creates an issue in itself (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). Consequently, the research problem lies in the point that a model needs to be created to locate the significant evidence based on the data of student’s background, experiences and interactions within online learning environments which influence their academic performance and satisfaction. Thus, this developed model must be as a guidance for instructors and decision makers in the online education industry in terms of using online platforms to improve students learning experience through online platforms. Bearing in mind these conditions, our major problem was: how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?
The major research question that are anticipated to be answered is:
how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?
To be able to answer this question, it is required to examine numerous sub-questions which have been stated as follow:
Q1: What is the relationship between students’ background and students’ satisfaction?
Q2: What is the relationship between students’ experience and students’ satisfaction?
Q3: What is the relationship between students’ collaboration and students’ satisfaction?
Q4: What is the relationship between students’ interaction and students’ satisfaction?
Q5: What is the relationship between students’ autonomy and students’ satisfaction?
Q6: What is the relationship between students’ satisfaction and students’ academic achievements?
Q7: What is the relationship between students’ application and students’ academic achievements?
Q8: What is the relationship between students’ remembering and students’ academic achievements?
Q9: What is the relationship between students’ understanding and students’ academic achievements?
Q10: What is the relationship between students’ analyzing and students’ academic achievements?
When designing web-courses within online learning instructions or mechanisms in general, educators are left with several decisions and considerations to face, which accordingly affect how students experience instruction, how they construct and process knowledge, how students could be satisfied through this experiment, and how web-based learning courses could enhance their academic achievements. In this study, we construct our theoretical framework according to Moore transactional distance theory (TDT) to measure student’s satisfaction, in addition to Bloom theory components to measure students’ academic achievements. Though the origins of TDT can be traced to the work of Dewey, it is Michael Moore who is identified as the innovator of this theory that first appeared in 1972. In his study and development of the theory, he acknowledged three main components of TDT that work as the base for much of the research on DL. Also, Bloom’s Taxonomy was established in 1956 under the direction of educational psychologist to measure students’ academic achievement (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956 ). TDT theory has been selected in this study since Transactional distance’s term indicates the geographical space between the student and instructor. Based on the learning understanding, which happens through learner’s interaction with his environment. This theory considers the role of each of these elements (Student’s autonomy, Dialogue, and class structure) whereas these three elements could help to investigate student’s satisfaction. Moore’s ( 1990 ) notion of ‘Transactional Distance’ adopt the distance that happens in all relations in education. The distance in the theory is mainly specified the dialogue’s amount which happens between the student and the teacher, and the structure’s amount in the course design. Which serves the main goal of this study as to enhance students online learning experience in relation to their satisfaction. Whereas, Bloom Theory has been selected in this study in addition to TDT to enhance students online learning experience in relation to their student’s achievements. In a conclusion both methods were implemented to develop and hypothesis this study hypothesis. See Fig. 1 .
Research Model and Hypotheses
H1: There is a significant relationship between students’ background and students’ satisfaction.
H2: There is a significant relationship between students’ experience and students’ satisfaction.
H3: There is a significant relationship between students’ collaboration and students’ satisfaction.
H4: There is a significant relationship between students’ interaction and students’ satisfaction.
H5: There is a significant relationship between students’ autonomy and students’ satisfaction.
H6: There is a significant relationship between students’ satisfaction and students’ academic achievements.
H7: There is a significant relationship between students’ application and students’ academic achievements.
H8: There is a significant relationship between students’ remembering and students’ academic achievements.
H9: There is a significant relationship between students’ understanding and students’ academic achievements.
H10: There is a significant relationship between students’ analyzing and students’ academic achievements.
This Section of the study will discuss the study hypothesis and relates each hypothesis to its related studies from the literature.
Students’ background regarding online platforms in this study is referred to as their readiness and willingness to use and adapt to different online platforms, providing them with the needed support and assistance. Students’ background towards online learning is a crucial component throughout this process, as prior research revealed that there are implementation issues, for instance; the deficiency of qualified lecturers, infrastructure and facilities, in addition to students’ readiness, besides students’ resistance to accept online learning platforms in addition to the Learning Management System (LMS) platforms, as educational tools (Azhari & Ming, 2015 ). However, student demand continued to increase, spreading to global audiences due to its exceptional functionality, flexibility and eventual accessibility (Azhari & Ming, 2015 ). There have been persistent apprehensions regarding online learning quality compared with traditional learning settings. In their research, (Paechter & Maier, 2010 ; Panyajamorn, Suthathip, Kohda, Chongphaisal, & Supnithi, 2018 ) have discovered that Austrian learners continue to prefer traditional learning environments due to communication goals, along with the interpersonal relations preservation. Moreover, (Lau & Shaikh, 2012 ) have discovered that Malaysian learners’ internet efficiency and computer skills, along with their personal demographics like gender, background, level of the study, as well as their financial income lead to a significant difference in their readiness towards online learning platforms. Abuhassna and Yahaya ( 2018 ) claimed that the current technologies in education play an essential role in providing a full online learning experience which is close enough to a face-to-face class in spite of the physical separation of the students from their educator, along with other students. Platforms of online learning lend themselves towards a less hierarchical methodology in education, fulfilling the learning desires of individuals which do not approach new information in a linear or a systematic manner. Platforms of online learning additionally are the most suitable ways for autonomous students (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Paechter & Maier, 2010 ; Panyajamorn et al., 2018 ).
Students’ experience in the current research indicates that learners must have prior experience in relation to utilizing online learning platform in their education settings. Thus, students experience towards online learning offers several advantages among themselves and their instructors in strengthening students’ learning experiences especially for isolated learners (Jaques & Salmon, 2007 ; Lau & Shaikh, 2012 ; Salmon, 2011 ; Salmon, 2014 ). Regardless of student recognition of the advantages towards supporting their learning throughout utilizing the technology, difficulties may occur through the boundaries about their technical capabilities and prior experiences towards utilizing the software itself from the perspective of its functionality. As demonstrated over learner’s experience and feedback from several online sessions over the years, this may frequently become a frustration source between both learners and their instructors, as this may make typically uncomplicated duties, for instance, watching a video, uploading a document, and other simple tasks to be progressively complicated for them, having no such prior experience. Furthermore, when filling out evaluations, for instance, online group presentations, the relatively limited capability to communicate face-to-face then to rely on a non-verbal signal along with audience’s body language might be a discouraging component. Nonetheless, the significance of being in a position to participate with other colleagues employing online sessions, which are occasionally nonvisual, for instance; teleconference format is a progressively significant skill in the modern workplace, thus affirming the importance of concise, clear, intensive interactions skills (Salmon, 2011 ; Salmon, 2014 ).
Students’ collaborations in the current study refers to the communication and feedback among themselves in online platforms. To refine and measure transactional distance using a survey tool, (Rabinovich, 2009 ) created a survey instrument to measure transactional distance in a higher education setting. A survey was sent to 235 students enrolled in a synchronous web-based graduate class in business regarding transactional distance and Collaborations (Rabinovich, 2009 ). The synchronous learning environment was described as a place where “live on-campus classes are conveyed simultaneously to both in-class students on campus and remote students on the Web who join via virtual classroom Web collaboration software” (Rabinovich, 2009 ). The virtual classroom software is similar to the characteristics of the two different software described by (Falloon, 2011 ; Mathieson, 2012 ) that it allows for students to interact with the educator and fellow students in real-time (Rabinovich, 2009 ). Moreover, (Kassandrinou, Angelaki, & Mavroidis, 2014 ) reported that the instructor plays a crucial role as interaction and communication helpers, as they are tasked with fostering, reassuring and assisting communication and interaction among students. Face-to-face tutorials have proven to be a vast opportunity for a multitude of students to interchange ideas, argue the content of the course and its related concerns (Vasala & Andreadou, 2010 ).
Purposeful interaction or (dialogue) in the current study describes communication that is learner-learner and learner-instructor which is designed to improve the understanding of the student. According to (Shearer, 2010 ) communication should also be constructive in that it builds upon ideas and work from others, as well as assists others in learning. (Moore, 1972 ) affirmed that learners also must realize that, and value the importance of the learning interactions as a vital part of the learning process. In a manner similar to (Benson & Samarawickrema, 2009 ] study of teacher preparatory students, (Falloon, 2011 ) investigated the use of digital tools in a case study at a teacher education program in New Zealand. (Mathieson, 2012 ) also explored the role dialogue plays in digital learning environments. She created a digital survey that examined students’ perception of audio-visual feedback in courses that utilize screen casting digital tools. (Moore, 2007 ) discusses autonomous learners searching for courses that do not stress structure and dialogue in order explain and enhance their learning progression. (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2015b ; Al-Rahmi, Othman, & Yusuf, 2015d ; Furnborough, 2012 ) concluded that the feeling of cooperation that learners’ share with their fellow students effect their reaction concerning their collaboration with their peers.
Student autonomy in the current study refers to their independence and motivation towards learning. The learner is the motivation of the way toward learning, along with their expectations and requirements, thinking about everyone as a unique individual and hence investigating their own capacities and possibilities. Thus, extraordinary importance is attributed to autonomy in DL environments, since the option of instructive intercession offered in distance education empowers students towards learning autonomy (Massimo, 2014 ). In this respect, the connection between autonomy of student and explicit parts of the learning procedure are in the center of consideration as mentioned. (Madjar, Nave, & Hen, 2013 ) concluded that a learners’ autonomy-supportive environment provides these learners with adoption of a more aims guided learning, leading to more learning achievements. This is why autonomy is desired in the online settings for both individual development and greater achievement in academic environments. The researchers also indicate in their research that while autonomy supports outcomes in goals and aims guiding, educator practices mainly lead to goals which necessary cannot adapt. Thus, supportive-autonomy learning process needs to be designed with affective elements consideration as well. However, (Stroet, Opdenakker, & Minnaert, 2013 ) efficiently surveyed 71 experimental studies on the impacts of autonomy supportive teaching on motivation of learner and discovered a clear positive correlation. Similar to attribution theory, the relationship between learner control and inspiration involves the possibility of learners adjusting their own inspirations, for example, learners may be competent to change self-determined extrinsic motivation to intrinsic motivation. However, (Jacobs, Renandya, & Power, 2016 ) further indicated that learners will not reach the same level of autonomy without reviewing learner’s autonomy insights, reflecting on their learning experiences, sharing these experiences and reflections with other learners, and realizing the elements influencing all these processes, and the process of learning as well.
Student satisfaction in the current study refers to the fact that there are many factors that play a role in determining the learner’s satisfaction, such as faculty, institution, individual learner element, interaction/communication elements, the course elements, and learning environment. Discussion of the elements also related to the role of the instructor, with the learner’s attitude, social presence, usefulness, and effectiveness of Online Platforms. (Yu, 2015 ) investigated that student satisfaction was positively associated with interaction, self-efficacy and self-regulation without significant gender variations. (Choy & Quek, 2016 ). examined the relationships between the learners’ perceived teaching, social, and cognitive element. In addition, satisfaction, academic performance, and achievement can be measured using a revised form of the survey instrument. (Kirmizi, 2014 ) studied connection between 6 psychosocial scales: personal relevance, educator assistance, student interaction and collaboration, student autonomy, authentic learning, along with active learning. A moderate level of correlation was found between these mentioned variables. Learner satisfaction predictors were educator support, personal relevance and authentic learning, while authentic learning was the only academic success predictor. Findings of (Bordelon, 2013 ) determined and described a positive correlation between both achievement and satisfaction. He demonstrated that the reasons behind these conclusions could be cultural variations in learner’s satisfaction which point out learning accession Zhu ( 2012 ). Scholars in the field of student satisfaction emphasis on the delivery besides the operational side of the student’s experience in the teaching process (Al-Rahmi, Othman, & Yusuf, 2015e ).
Students achievements in this study refers to Bloom’s main four components of achievements, which are remembering, understanding, applying, and analyzing. Finding in a study conducted by (Whitmer, 2013 ) revealed the relationships between student academic achievement and the LMS usage, thus the findings showed a highly systematic association ( p < .0000) in relation to every variable. These variables described 12% and 23% of variations within the final course marks, which indicates that learners who employed the LMS more often obtained higher marks than the others. Thus, the correlation techniques examined these variables separately to ascertain their association with the final mark. Moreover, it is not the technology itself; it is the educational methods in relation to which technology has been utilized that create a change in learners’ achievement. Instruments used are significant in identifying the technology impact, moreover, it is the implementation of those instruments under specific activities and for certain purposes which indicates whether or not they are effective. In contrast, a study conducted by (Barkand, 2017 ) revealed that LMS tools were not considered to have an effect on semester final grades when categorized by school year. In his study, semester final grades were a measure of student achievement, which has subjective elements. To account for the subjective elements in semester final grades, the study also included objective post test scores to evaluate student learning. Additionally, in this study, we refer to Bloom’s Taxonomy established in 1956 under the direction of educational psychologist for measuring students’ academic achievement (Bloom et al., 1956 ). Moreover, in this study, we selected fours domains of Blooms Taxonomy in order to achieve this study objectives, which are; application: which refers to using a concept in new context, for instance; applying what has been learned inside the classroom into different circumstances; remembering, which refers to recalling or retrieving prior learned knowledge; understanding, which refers to realizing the meaning, then clarification of problems instructions; analyzing, which refers to separating concepts or material into parts in such a way that its structure can be distinguished, understood among inferences and facts.
Applying involves “carrying out or using a procedure through executing or implementing” (Anderson & Krathwohl, 2001 ). Applying in this study refers to the student’s ability to use online platforms, such as how to log in, how to end session, how to download materials, how to access links and videos. Students can exchange information about a specific topic in online platforms such as Moodle, Google Documents, Wikis and apply knowledge to create and participate in online platforms.
Remembering is defined as “retrieving, recognizing, and recalling relevant knowledge from long-term memory” (Anderson & Krathwohl, 2001 ). In this study, remembering is referred to the ability to organize and remember online resources to easily find information on the internet. Moreover, students can easily cooperate with their colleagues and educator, contributing to the educational process and justifying their study procedure. Anderson and Krathwohl ( 2001 ) In their review of Bloom’s taxonomy, Anderson and Krathwohl ( 2001 ) recognized greater learning levels as creating, evaluating, and analyzing, with the lower learning levels as applying, understanding, and remembering.
Understanding involves “constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining” (Anderson & Krathwohl, 2001 ). In this study, understanding is referred to as understanding regarding a subject then putting forward new suggestions about online settings, for instance; understanding how e-learning works, or LMS. For example, students use online platforms to review concepts, courses, and prominent resources are being used inside the classroom environment.
Analyzing includes “breaking material into constituent parts, determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing” (Anderson & Krathwohl, 2001 ). Analyzing refers to the student’s ability to connect, discuss, mark-up, then evaluate the information received into one certain workplace or playground. Solomon and Schrum ( 2010 ) claim that educators have started employing online platforms for a range of activities, since they have become more familiar and there are ways for learners to benefit from using them. Generally, the purpose and goal are to publicize the development types, innovation, as well as additional activities that their learners usually do independently. Such instruments have also provided instructors ways to encourage and promote genuine cooperation in their project’s development (Solomon & Schrum, 2010 ).
A quantitative approach was implemented in this study to provide an inclusive insight in relation to students online learning experience and how to enhance both their satisfaction and academic achievements using a questionnaire. Two experts were referred for the evaluation of the questionnaire’s content. Before the collection of the data, permission regarding the current research purpose has been obtained from Universiti Teknologi Malaysia (UTM). In relation to the sampling and population, this research was conducted among undergraduate learners who have been online learning users. Learners, who had manually obtained the questionnaires, have been requested to fill in their details, then fill their own assessments regarding online learning platforms and its effects towards their academic achievements. Thus, for data analysis, the data that were attained from questionnaires were then analyzed using the Statistical Package for the Social Sciences (SPSS). Specifically, Structural Equation Modeling (SEM- Amos), which has been employed as a primary data analysis tool. Moreover, utilizing SEM-Amos process involves two main phases: evaluating construct validity, the convergent validity, along with the discriminant validity of the measurements; then analyzing the structural model. These mentioned two phases followed the recommendations of (Bagozzi, Yi, & Nassen, 1998; Hair, Sarstedt, Ringle, & Mena, 2012a , 2012b ).
A total of 283 questionnaires were distributed manually; of these, only 264, which make up 93.3% of the total number, were returned to the authors. Excluding the 26 incomplete questionnaires, 264 were evaluated employing SPSS. A total of 21 questionnaires have been excluded: 14 were incomplete and 7 having outliners. Thus, the overall number of valid questionnaires was 243 following this exclusion. This exclusion step is being supported by Hair et al. ( 2012a , 2012b ) . Moreover, Venkatesh, Thong, & Xu, 2012 who pointed out that this procedure is essential to be implemented as the existence of outliers could be a reason for inaccurate results. Regarding the respondent’s demographic details: 91 (37.4%) were males, and 152 (62.6%) were females. 149 (61.3%) were in the age range of 18 t0 20 years old, 77 (31.7%) were in the age range of 21 to 24 years old, and 17 (7.0%) were in the age range of 25 to 29 years old. Regarding level of study: 63 (25.9%) were from level 1, 72 (29.6%) were from level 2, 50 (20.6%) were from level 3, and 58 (23.9%) were from level 4.
The questionnaire in this study has been developed to fit the study hypothesis. Consequently, it was developed based into both theories that have been utilized in this study. The questionnaire has two main sections, first section aims to measure student satisfaction which is based on the TDT theory variables. Second section of the questionnaire has been developed to measure students’ academic achievement based on Bloom theory. According to Bloom theory there are four variables that measure students’ achievements, which are application, remembering, understanding, analyzing. On that basis the questionnaire has been developed to measure both students’ satisfaction and academic achievements . The construct items were adapted to ensure content validity. This questionnaire consisted of two main sections. First part covered the demographic details of the respondents’ including age, gender, educational level. The second part comprises 51 items which were adapted from previous researches as following; student background, five items, student experience, five items adapted from (Akaslan & Law, 2011 ), student collaborations, and, student interactions items adapted from (Bolliger & Inan, 2012 ), student autonomy, five items adapted from (Barnard et al., 2009 ; Pintrich, Smith, Garcia, & McKeachie, 1991 ), student satisfaction, six items adapted from (The blended learning impact evaluation at UCF is conducted by Research Initiative for Teaching Effectiveness, n.d. ). Moreover, effects of the students’ application, four items, students’ remembering, four items, students’ understanding, four items, students’ analyzing, four items, and students’ academic achievements, four items adapted from (Pekrun, Goetz, & Perry, 2005 ). The questionnaire has been distributed to the students after taking the online course.
Cronbach’s Alpha reliability coefficient result was 0.917 among all research model factors. Thus, the discriminant validity (DV) assessment was carried out through utilizing three criteria, which are: index between variables, which is expected to be less than 0.80 (Bagozzi, Yi, & Nassen, 1988 ); each construct AVE value must be equal to or higher than 0.50; square of (AVE) between every construct should be higher, in value, than the inter construct correlations (IC) associated with the factor [49]. Furthermore, the crematory factor analysis (CFA) findings along with factor loading (FL) should therefore be 0.70 or above although the Cronbach’s Alpha (CA) results are confirmed to be ≥0.70 [50]. Researchers have also added that composite reliability (CR) is supposed to be ≥0.70.
Current research employed AMOS 23 to analyze the data. Both structural equation modeling (SEM) as well as confirmatory factor analysis (CFA) have been employed as the main analysis tools. Uni-dimensionality, reliability, convergent validity along with discriminant validity have been employed to assess the measurement model. (Bagozzi et al., 1988 ; Byrne, 2010 ; Kline, 2011 ) highlighted that goodness-of-fit guidelines, such as the normed chi-square, chi-square/degree of freedom, normed fit index (NFI), relative fit index (RFI), Tucker-Lewis coefficient (TLI) comparative fit index (CFI), incremental fit index (IFI), the parsimonious goodness of fit index (PGFI), thus, the root mean square error of approximation (RMSEA) besides the root mean-square residual (RMR). All these are tools which could be utilized as the assessment procedures for the model estimation. See Table 1 & Fig. 2 .
Measurement Model
Such type of validity is commonly employed to specify the size difference between a concept and its indicators and other concepts (Hair et al., 2012a , 2012b ). Through analysis in this context, discriminant validity has proven to be positive over all concepts given that values have been over 0.50 (cut-off value) from p = 0.001 according to Fornell and Larcker ( 1981 ). In line with Hair et al. ( 2012a , 2012b ) . Bagozzi, Yi, & Nassen, (1998), the correlation between items at any two specified constructs must not exceed the square root of the average variance that is shared between them in a single construct. The outcomes values of composite reliability (CR) besides those of Cronbach’s Alpha (CA) remained about 0.70 and over, while the outcomes of the average variance extracted (AVE) remained about 0.50 and higher, indicating that all factor loadings (FL) were significant, thereby fulfilling conventions in the current assessment Bagozzi, Yi, & Nassen, (1998), and Byrne ( 2010 ). Following sections expand on the results of the measurement model. Findings of validity, reliability, average variance extracted (AVE), composite reliability (CR) as well as Cronbach’s Alpha (CA) have all been accepted, which also demonstrated determining the discriminant validity. It is determined that all the values of (CR) vary between 0.812 and 0.917, meaning they are above the cut-off value of 0.70. The (CA) result values also varied between 0.839 and 0.897 exceeding the cut-off value of 0.70. Thus, the (AVE) was similarly higher than 0.50, varying between 0.610 and 0.684. All these findings are positive, thus indicating significant (FLs) and they comply with the conventional assessment guidelines Bagozzi, Yi, & Nassen, (1998), along with Fornell and Larcker ( 1981 ). See Table 2 and Additional file 1 .
In the current study, the path modeling analysis has been utilized to examine the impact of students’ academic achievements among higher education institutions through the following factors (students’ background, students’ experience, students’ collaborations, students’ interaction, students’ autonomy, students’ remembering, students’ understanding, students’ analyzing, students’ application, students’ satisfaction), which is based on online learning. The findings are displayed then compared in hypothesis testing discussion. Subsequently, as the second stage, factor analysis (CFA) has being conducted on structural equation modeling (SEM) in order to assess the proposed hypotheses as demonstrated in Fig. 3 .
Findings for the Proposed Model Path analysis
As shown in both Figs. 3 and 4 , all hypotheses have been accepted. Moreover, Table 3 below shows that the fundamental statistics of the model was good, which indicates model validity along with the testing results of the hypotheses through demonstrating the values of unstandardized coefficients besides standard errors of the structural model.
Findings for the Proposed Model T.Values
The first direct five assumptions, students’ background, students’ experience, students’ collaborations, students’ interaction; students’ autonomy with students’ satisfaction, were addressed. In accordance with Fig. 4 and Table 3 , relations between students’ background and students’ satisfaction was (β = .281, t = 5.591, p < 0.001), demonstrating that the first hypothesis (H1) has suggested a positive and significant relationship. Following hypothesis illustrated the relationship between students’ experience and students’ satisfaction (β = .111, t = 1.951, p < 0.001), demonstrating that the second hypothesis (H2) proposed a positive and significant relationship. Third hypothesis illustrated the relationship between students’ collaborations and students’ satisfaction (β = .123, t = 2.584, p < 0.001) demonstrating that the third hypothesis (H3) has suggested a positive and significant relationship. Additionally, the relationship between students’ background and students’ satisfaction was (β = .116, t = 2.212, p < 0.001), indicating that the fourth hypothesis (H4) has suggested a positive and significant relationship. Further to the above-mentioned findings, the relationship between students’ autonomy and students’ satisfaction was (β = .470, t = 7.711, p < 0.001), demonstrating that the fifth hypothesis (H5) has suggested a positive and significant relationship. Moreover, in the second section, five assumptions were discussed, which are students’ satisfaction, students’ remembering, students’ understanding, students’ analyzing, students’ application along with students’ academic achievements.
As shown in Fig. 4 and Table 3 , the association between students’ satisfaction and students’ academic achievements was (β = .135, t = 3.473, p < 0.001), demonstrating that the sixth hypothesis (H6) has suggested a positive and significant relationship. Following hypothesis indicated the relationship between students’ application and students’ academic achievements (β = .215, t = 6.361, p < 0.001), indicating that the seventh hypothesis (H7) has suggested a positive and significant relationship. Thus, the eighth hypothesis indicated the relationship between students’ remembering and students’ academic achievements was (β = .154, t = 4.228, p < 0.001), demonstrating that the eight hypothesis (H8) has suggested a positive and significant relationship. Additionally, the correlation between students’ understanding and students’ academic achievements was (β = .252, t = 6.513, p < 0.001), demonstrating that the ninth hypothesis (H9) has suggested a positive and significant relationship. Finally, the relationship between students’ analyzing and students’ academic achievements was (β = .179, t = 6.215, p < 0.001), demonstrating that the tenth hypothesis (H10) has suggested a positive and significant relationship. Accordingly, this current model demonstrated student’s compatibility to use online learning platforms to improve students’ academic achievements and satisfaction. This is in accordance with earlier investigations (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ; Al-rahmi, Othman, & Yusuf, 2015c ; Barkand, 2017 ; Madjar et al., 2013 ; Salmon, 2014 ).
Developing a new hybrid technology acceptance model through combining TDT and BTT has been the major objective of the current research, which aimed to investigate the guiding factors towards utilizing online learning platforms to improve students’ academic achievements and satisfaction in higher education institutions. The current research is intensifying a step forward by implementing TDT along with a BTT model. Using the proposed model, the current research examined how students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. The current research found that students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy were influenced by students’ satisfaction. Also, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. This conclusion is consistent with earlier correlated literature. Thus, this reveals that learners first make sure whether using platforms of online learning were able to meet their study requirements, or that using platforms of online learning are relevant to their study process before considering employing such technology in their study. Learners have been noted to perceive that platforms of online learning is more useful only once they discover that such a technology is actually better than the traditional learning which does not include online learning platforms (Choy & Quek, 2016 ; Illinois Online Network, 2003 ). Using the proposed model, the current research examined how to improve students’ academic achievements and satisfaction. Thus, the following section will be a comparison between this study results and previous research, as follows.
The first hypotheses of this study demonstrated a positive and significant association between students’ prior background towards online platforms with their satisfaction. As clearly investigated in Osika and Sharp ( 2002 ) study, numerous learners deprived of these main skills enroll in the courses, struggle, and subsequently drop out. In addition, Bocchi, Eastman, and Swift ( 2004 ) investigation claimed that prior knowledge of students’ concerns, demands along with their anticipations is crucial in constructing an efficient instruction. Thus, to clarify, students must have prior knowledge and background before letting them into the online platforms. On the other hand, there are constant concerns about the online learning platforms quality in comparison to a face-to-face learning environment, as students do not have the essential skills required toward using online learning platforms (Illinois Online Network, 2003 ). Moreover, a study by Alalwan et al. ( 2019 ) discovered that Austrian learners still would rather choose face-to-face learning for communication purposes, and the preservation of interpersonal relations. This is due to the fact that learners do not as yet have the background knowledge and skills needed towards using online learning platforms. Additional research by Orton-Johnson ( 2009 ) among UK learners claimed that learners have not accepted online materials, and continue to prefer traditional context materials as the medium for their learning, which also indicates the importance of prior knowledge and background towards online platforms before going through such a technology.
The second hypotheses of this study proposed a positive and significant association between students’ experience along with students’ satisfaction, which revealed that putting the students in such an experience would provide and support them with the ability to overcome all difficulties that arise through the limits around the technical ability of the online platforms. This is in line with some earlier researches regarding the reasons that lead to people’s technology acceptance behavior. One reason is the notion of “conformity,” which means the degree to which an individual take into consideration that an innovation is consistent with their existing demands, experiences, values and practices (Chau & Hu, 2002 ; Moore & Benbasat, 1991 ; Rogers, 2003 ; Taylor & Todd, 1995 ). Moreover, (Anderson & Reed, 1998 ; Galvin, 2003 ; Lewis, 2004 ) claimed that most students who had prior experience with online education tended to exhibit positive attitudes toward online education, and it affects their attitudes toward online learning platforms.
The third hypotheses of this study demonstrated a positive and significant association among student collaboration with themselves in online platforms, which indicates the key role of collaboration between students in order to make the experiment more realistic and increase their ability to feel more involved and active. This is agreement with Al-rahmi, Othman, and Yusuf ( 2015f ) who claimed that type, quality, and amount of feedback that each student received was correlated to a student’s sense of success or course satisfaction. Moreover, Rabinovich ( 2009 ) found that all types of dialogue were important to transactional distance, which make it easier for the student to adapt to online learning platform. Also, online learning platforms enable learners to share then exchange information among their colleagues Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ).
The fourth hypothesis of this study proposed a positive and significant correlation between students’ collaborations and students’ satisfaction, which indicates the significance of the communication between students and their instructor throughout the online platforms experiment. These results agree with (Mathieson, 2012 ) results, which stated that the ability of communication between students and their instructor lowered the sense of separation between learner and educator. Moreover, in line with (Kassandrinou et al., 2014 ), communication guides learners to undergo constructive emotions, for example relief, satisfaction and excitement, which assist them to achieve their educational goals. In addition, (Furnborough, 2012 ) draws conclusion that learners’ feeling of cooperating with their fellow students effects their reaction concerning their collaboration with their peers. Moreover, Kassandrinou et al., 2014 focused on the instructor as crucial part as interaction and communication helpers, as they are thought to constantly foster, reassure and assist communication and interaction amongst students.
The fifth hypotheses of this study proposed a positive and significant relationship between student’s autonomy and online learning platforms, which indicates that students need a sense of dependence towards online platforms, which agrees with Madjar et al. ( 2013 ) who concluded that a learners’ autonomy-supportive environment provides these learners with adoption of more aims, leading to more learning achievements. Moreover, Stroet et al. ( 2013 ) found a clear positive correlation on the impacts of autonomy supportive teaching on motivation of learner. O’Donnell, Chang, and Miller ( 2013 ) also argues that autonomy is the ability of the learners to govern themselves, especially in the process of making decisions and setting their own course and taking responsibility for their own actions.
The sixth hypotheses of this study proposed a positive and significant correlation between student’s satisfaction with online learning platforms, which indicates a level of acceptance by the students to adapt into online learning platforms. This is in agreement with Zhu ( 2012 ) who reported that student’s satisfaction in online platforms is a statement of confidence with the system. Moreover, Kirmizi ( 2014 ) study revealed that the predictors of the learners’ satisfaction were educator’s support, personal relevance and authentic learning, whereas the authentic learning is only the predictor of academic success. Furthermore, the findings of Bordelon ( 2013 ) stated and determined a positive correlation between both satisfaction and achievement. In addition, the results of Mahle ( 2011 ) clarified that student satisfaction occurs when it is realized that the accomplishment has met the learners’ expectations, which is then considered a short-term attitude toward the learning procedure.
Hypotheses seven, eight, nine and ten of this study proposed a positive and significant relationship between student’s academic achievements with online learning platforms, which indicates the key main role of online platform with students’ academic achievements. This agrees with Whitmer ( 2013 ) findings, which revealed that the associations between student usage of the LMS and academic achievement exposed a highly systematic relationship. In contrast, Barkand ( 2017 ) found that there is no significant difference in students’ academic achievements in utilizing online platforms regarding students’ academic achievements, which is due to the fact that academic achievement towards online learning platforms requires a certain set of skills and knowledge as mentioned in the above sections in order to make such technology a success.
The seventh hypotheses of this study proposed a positive and significant correlation between students’ application and students’ academic achievements, which indicates the major key of applying in the learning process as an effected element. This is in line with the Computer Science Teachers’ Association (CSTA) taskforce in the U. S (Computer Science Teachers’ Association (CSTA), 2011 ), where they mentioned that applying elements of computer skills is essential in all state curricula, directing to their value for improving pupils’ higher order thinking in addition to general problem-solving abilities. Moreover, Gouws, Bradshaw, and Wentworth ( 2013 ) created a theoretical framework which drawn education computational thoughts compared to cognitive levels established from Bloom’s Taxonomy of Learning Purposes. Four thinking skill levels have been utilized to assess the ‘cognitive demands’ initiated by computational concepts for instance abstraction, modelling, developing algorithms, generating automated processes. Through the iPad app, LightBot. thinking skills remained recognizing (which means recognize and recall expertise correlating to the problem); Understanding (interpret, compare besides explain the problem); whereas, applying (make use of computer skills to create a solution) then Assimilating (critically decompose and analyses the problem).
The eighth hypotheses of this study proposed a positive and significant correlation between students’ remembering and students’ academic achievements, which indicates the importance of remembering as a process of retrieving information relating to what needed to be done and/or outcome attributes) over the procedure of learning according to Bloom’s Taxonomy of Educational Objectives. Additionally, Falloon ( 2016 ) claimed that responding to data indicated the use of general thinking skills to clarify and understand steps and stages needed to complete a task (average 29%); recalling or remembering information about a task or available tools (average 13%); and discussing and understanding success criteria (average 3%).
The ninth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which indicates its significance with the academic achievements as a process of criticizing the task or the problem faced by the students into phases or activities to help understanding of how to resolve the problem. The current results agree with Falloon ( 2016 ) who demonstrated the necessity to build understanding over the thinking processes employed by students once they are engaged in their work. In addition, Falloon ( 2016 ) suggested that the purpose and nature of questioning was broader than this, with questioning of self and others being an important strategy in solution development. In many respects, the questioning for those students was not much a perspective, although more a practice, to the degree that assisted them to understand their tasks, analyze intended or developed explanations and to evaluate their outcomes.
The tenth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which reveals the importance of analysis as a process of employing general thinking besides computational knowledge in order to realize the challenges through using online platforms, in addition to predictive thinking to categorize, explore and fix any possible errors throughout the whole process. Falloon ( 2016 ) claimed that analyzing was often a collaborative procedure between pairs receiving and giving counseling from others to assist in solving complications. On the other hand, online learning platforms are highly dependent on connecting and sharing as a basic strategy that needs to be employed over all stages of online learning settings, whether between students and students, or between students and their instructor. Moreover, Falloon ( 2016 ) findings showed that Analyzing (average 17%) was present in various phases of these online students’ work, which is based on what phase they were at together with their tasks, despite the fact that most analysis was associated with students depending on themselves during online process.
In this investigation, both transactional distance theory (TDT) and Bloom’s Taxonomy theory (BTT) have been validated in the educational context, providing further understanding towards the students’ prospective perceptions on using online learning platforms to improve students’ academic achievement and satisfaction. The contribution that the current research might have to the field of online learning platforms have been discussed and explained. Additional insights towards students’ satisfactions and students’ academic achievements have also been presented. The current research emphasizes that the incorporation of both TDT and BTT can positively influence the research outcome. The current research has determined that numerous stakeholders, for instance developers, system designers, along with institutional users of online learning platforms reasonably consider student demands and needs, then ensure that the such a system is effectively meeting their requirements and needs. Adoption among users of online learning platforms could be broadly clarified by the eleven factor features which is based on this research model. Thus, the current research suggests more investigation be carried out to examine relationships among the complexity of online learning platforms combined with technology acceptance model (TAM).
Based on the study findings, the first recommendation would be for administrators of higher institution. In order to implement online learning, there must be more interest given to the course structure design, whereas it should be based on theories and prior literature. Moreover, instructor and course developer need to be trained and skilled to achieve online learning platforms goals. Workshops and training sessions must be given for both instructors and students to make them more familiar in order to take the most advantages of the learning management system like Moodle and LMS. The software itself is not enough for creating an online learning environment that is suitable for students and instructors. If instructors were not trained and unaware of utilizing the software (e.g. Moodle) in the class, then the quality of education imparted to students will be jeopardized. Training and assessing the class instructor and making modifications to the software could result in a good environment for the instructor and a quality education for the student. Both students’ satisfaction and academic achievements depends on their prior knowledge and experience in relation to online learning. This current research intended to investigate student satisfaction and academic achievements in relation to online learning platforms in on of the higher education in Malaysia. Future research could integrate more in relation to blended learning settings.
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Abuhassna, H., Al-Rahmi, W.M., Yahya, N. et al. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. Int J Educ Technol High Educ 17 , 38 (2020). https://doi.org/10.1186/s41239-020-00216-z
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Online teaching–learning methods have been followed by world-class universities for more than a decade to cater to the needs of students who stay far away from universities/colleges. But during the COVID-19 pandemic period, online teaching–learning helped almost all universities, colleges, and affiliated students. An attempt is made to find the effectiveness of online teaching–learning methods for university and college students by conducting an online survey. A questionnaire has been specially designed and deployed among university and college students. About 450 students from various universities, engineering colleges, medical colleges in South India have taken part in the survey and submitted responses. It was found that the following methods promote effective online learning: animations, digital collaborations with peers, video lectures delivered by faculty handling the subject, online quiz having multiple-choice questions, availability of student version software, a conducive environment at home, interactions by the faculty during lectures and online materials provided by the faculty. Moreover, online classes are more effective because they provide PPTs in front of every student, lectures are heard by all students at the sound level of their choice, and walking/travel to reach classes is eliminated.
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Critical thinking and creativity of students increase with innovative educational methods according to the world declaration on higher education in the twenty-first century [ 1 ]. Innovative educational strategies and educational innovations are required to make the students learn. There are three vertices in the teaching–learning process viz., teaching, communication technology through digital tools, and innovative practices in teaching. In the first vertex, the teacher is a facilitator and provides resources and tools to students and helps them to develop new knowledge and skills. Project-based learning helps teachers and students to promote collaborative learning by discussing specific topics. Cognitive independence is developed among students. To promote global learning, teachers are required to innovate permanently. It is possible when university professors and researchers are given space to new educational forms in different areas of specializations. Virtual classrooms, unlike traditional classrooms, give unlimited scope for introducing teaching innovation strategies. The second vertex refers to the use of Information and Communication Technology (ICT) tools for promoting innovative education. Learning management systems (LMS) help in teaching, learning, educational administration, testing, and evaluation. The use of ICT tools promotes technological innovations and advances in learning and knowledge management. The third vertex deals with innovations in teaching/learning to solve problems faced by teachers and students. Creative use of new elements related to curriculum, production of something new, and transformations emerge in classrooms resulting in educational innovations. Evaluations are necessary to improve the innovations so that successful methods can be implemented in all teaching and learning community in an institution [ 2 ]. The pandemic has forced digital learning and job portal Naukri.com reports a fourfold growth for teaching professionals in the e-learning medium [ 3 ]. The initiatives are taken by the government also focus on online mode as an option in a post-covid world [ 4 ]. A notable learning experience design consultant pointed out that, educators are entrusted to lead the way as the world changes and are actively involved in the transformation [ 5 ]. Weiss notes that an educator needs to make the lectures more interesting [ 6 ].
This paper presents the online teaching–learning tools, methods, and a survey on the innovative practices in teaching and learning. Advantages and obstacles in online teaching, various components on the effective use of online tools, team-based collaborative learning, simulation, and animation-based learning are discussed in detail. The outcome of a survey on the effectiveness of online teaching and learning is included. The following sections present the online teaching–learning tools, the details of the questionnaire used for the survey, and the outcome of the survey.
The four essential parts of online teaching [ 7 ] are virtual classrooms, individual activities, assessments in real-time, and collaborative group work. Online teaching tools are used to facilitate faculty-student interaction as well as student–student collaborations [ 8 ]. The ease of use, the satisfaction level, the usefulness, and the confidence level of the instructor is crucial [ 9 ] in motivating the instructor to use online teaching tools. Higher education institutes recognize the need to accommodate wide diverse learners and Hilliard [ 10 ] points out that technical support and awareness to both faculty and student is essential in the age of blended learning. Data analytics tool coupled with the LMS is essential to enhance [ 11 ] the quality of teaching and improve the course design. The effective usage of online tools is depicted in Fig. 1 comprising of an instructor to student delivery, collaboration among students, training for the tools, and data analytics for constant improvement of course and assessment methods.
The various components of effective usage of online tools
A plethora of online teaching tools are available and this poses a challenge for decision-makers to choose the tools that best suits the needs of the course. The need for the tools, the cost, usability, and features determine which tools are adopted by various learners and institutions. Many universities have offered online classes for students. These are taken up by students opting for part-time courses. This offers them flexibility in timing and eliminates the need for travel to campus. The pandemic situation in 2019 has forced many if not all institutions to completely shift classes online. LMS tools are packaged as Software as a Service (SaaS) and the pricing generally falls into 4 categories: (i) per learner, per month (ii) per learner, per use (iii) per course (iv) licensing fee for on-premise installation [ 12 ].
Online teaching/learning as part of the ongoing semester is typically part of a classroom management tool. GSuite for education [ 13 ] and Microsoft Teams [ 14 ] are both widely adopted by schools and colleges during the COVID-19 pandemic to effectively shift regular classes online. Other popular learning management systems that have been adopted as part of blended learning are Edmodo [ 15 ], Blackboard [ 16 ], and MoodleCloud [ 17 ]. Davis et al. [ 18 ] point out advantages and obstacles for both students and instructors about online teaching shown in Table 1 .
The effectiveness of course delivery depends on using the appropriate tools in the course design. This involves engaging the learners and modifying the course design to cater to various learning styles.
The questionnaire aims to identify the effectiveness of various online tools and technologies, the preferred learning methods of students, and other factors that might influence the teaching–learning process. The parameters were based on different types of learners, advantages, and obstacles to online learning [ 10 , 18 ]. Questions 1–4 are used to comprehend the learning style of the student. Questions 5–7 are posed to find out the effectiveness of the medium used for teaching and evaluation. Questions 8–12 are framed to identify the various barriers to online learning faced by students.
This methodology is adopted as most of the students are attending online courses from home and polls of this kind will go well with the students from various universities. Students participated in the survey and answered most of the questionnaire enthusiastically. The only challenge was a suitable environment and free time for them to answer the questionnaire, as they are already loaded with lots of online work. Students from various universities pursuing professional courses like engineering and medicine took part in this survey. They are from various branches of sciences and technologies. Students are from private universities, colleges, and government institutions. Figure 2 shows the institution-wise respondents. Microsoft Teams and Google meet platforms were used for this survey among university, medical college, and engineering college students. About 450 students responded to this survey. 52% of the respondents are from VIT University Vellore, Tamil Nadu, 23% of the respondents are from CMR Institute of Technology (CMRIT), Bangalore, 15% of the respondents are from medical colleges and 10% are from other engineering colleges. During this pandemic period, VIT students are staying with parents who are living in different states of India like Andhra, Telangana, Kerala, Karnataka, MP, Haryana, Punjab, Maharashtra, Andaman, and so on. Only a few students are living in Tamil Nadu. Some of the students are staying with parents in other countries like Dubai, Oman, South Africa, and so on. Some of the students of CMRIT Bangalore are living in Bangalore and others in towns and villages of Karnataka state. Students of medical colleges are living in different parts of Tamil Nadu and students of engineering colleges are living in different parts of Andhra Pradesh. Hence, the survey is done in a wider geographical region.
Institution-wise respondents
Figure 3 shows the branch-wise respondents. It is shown that 158 students belong to mechanical/civil engineering. 108 respondents belong to computer science and engineering, 68 students belong to medicine, 58 students belong to electrical & electronics engineering, and electronics & communication engineering. 58 students belong to other disciplines.
Branch-wise respondents
Students were assured of their confidentiality and were promised that their names would not appear in the document. A list of the questions asked as part of the survey is given below.
Questionnaire:
Sample group: B Tech students from different branches of sciences across various engineering institutions and MBBS medical students.
Which of the methods engage you personally to learn digitally ?
Individual assignment
Small group (No. 5 students) work
Large group (No. 10 students and more) work
Project-based learning
Which of the digital collaborations enables you to work on a specific task at ease
Two by two (2 member team)
Small group workgroup (No. 5 students) work
Which of the digital approaches motivate you to learn
Whiteboard and pen
PowerPoint presentation
Digital pen and slate
My experience with online learning from home digitally
I am learning at my own pace comfortably
My situational challenges are not suitable
I can learn better with uninterrupted network connectivity
I am distracted with various activities at home, viz. TV, chatting, etc.
Which type of recorded video lecture is more effective for learning ?
delivered by my faculty
delivered by NPTEL
delivered by reputed Overseas Universities
delivered by unknown experts
Which type of quiz is more effective for testing the understanding?
Traditional—pen and paper—MCQ
Traditional—pen and paper—short answers
Online quiz—MCQ
Online quiz—short answers
Student version software downloaded from the internet is useful for learning
Unable to decide
Online teaching – learning takes place effectively because:
Every student can hear the lecture clearly
PPTs are available right in front of every student
Students can ask doubts without much reservation
Students need not walk long distances before reaching the class
Which of the following statements is true of online learning off-campus ?
No one disturbs me during my online learning.
My friend/family member/roommate/neighbor occasionally disturb me
My friend/family member/roommate/neighbor constantly disturb me
At home/place of residence, how many responsibilities do you have?
I don’t have many responsibilities.
I have a moderate amount of responsibilities, but I have sufficient time for online learning.
I have many responsibilities; I don’t have any time left for online learning.
What is your most preferred method for clearing doubts in online learning?
Ask the professor during/after an online lecture
Post the query in a discussion forum of your class and get help from your peers
Go through online material providing an additional explanation.
Which of the following devices do you use for your online learning?
A laptop/desktop computer
A smartphone
Other devices
Students would prefer to work in a group of 5 students to engage personally in digital learning as seen from Fig. 4 .
Personal engagement in digital learning
Digital collaboration to enable students to work at ease on a specific task is to allow them to work in small groups of 5 students as seen in Fig. 5 .
Digital collaboration to enable students to work at ease
Animations are found to be the best digital approach motivating many students to learn as seen in Fig. 6 .
Digital approaches that motivate students to learn
The online learning experience of students is shown in Fig. 7 . The majority of students have said that they can learn at their own pace comfortably through online learning.
The online learning experience of students
The effectiveness of the recorded video lecture is shown in Fig. 8 . The majority of students agree that the video lectures delivered by his/her faculty teaching the subject help students to learn effectively.
More effective recorded video lecture
Online quiz having multiple-choice questions (MCQ) is preferred by most of the students for testing their understanding of the subject as seen in Fig. 9 .
More effective quiz for testing the understanding
The usefulness of the student version of the software downloaded from the internet is shown in Fig. 10 . 45.7% of the students agree that it is useful for learning whereas 45.2% of them are unable to decide. The rest of the students feel that the student version of the software is not useful.
The usefulness of the student version of the software
The reasons for the effectiveness of online teaching–learning are shown in Fig. 11 . The majority of the students, feel that the PPTs are available right in front of every student so that following the lecture makes the learning effective. In universities where a fully flexible credit system (FFCS) is followed, students need to walk long distances for reaching their classrooms. Day Scholars in universities as well as engineering colleges are required to travel a considerable distance before reaching the first-hour class. According to many students, online learning is more effective since walking/traveling is completed eliminated. If the voice of the faculty member is feeble, students sitting in the last few rows of the class would not hear the lecture completely. Some students feel that online learning is more effective since the lecture is reaching every student irrespective of the number of students in a virtual classroom.
Reasons for the effectiveness of online teaching–learning
50.3% of students agree that they do not have any disturbance during online learning and it is more effective. Many of them feel that occasionally their friends or relatives disturb students during their online learning as shown in Fig. 12 .
Disturbances during online learning
Figure 13 shows the environment at home for online learning. 76.9% of the respondents stated that they have a moderate amount of responsibilities at home but they have sufficient time for online learning. 16.1% of them have said that they do not have many responsibilities whereas 7% of them claimed that they have many responsibilities at home and they do not have any time left for online learning.
The environment at home for online learning
Figure 14 shows the methods adopted for clearing doubts in online learning. 43.2% of the respondents ask the Professor and get their doubts clarified during online lectures. 25.5% of them post queries in the discussion forum and help from peers. 31.3% of them go through the online materials providing additional explanation and get their doubts clarified.
Methods adopted for clearing doubts in online learning
Figure 15 shows the devices used by students for online learning. Most of the students use laptop/desktop computers, many of them use smartphones and very few students use tablets.
Devices used for online learning
The association between responses 1 and 2 is tested using the chi-square test. The results are presented in Table 2 which shows the observed cell totals, expected cell values, and chi-square statistic for each cell. It is seen that association exists between several responses between questions.
The observed cell values indicate that the highest association is found between responses 1b and 2b since both these responses are related to a small working group having 5 members. The lowest association is found between the responses of 1c and 2a having the lowest observed cell value and expected cell value. The reason for this is response 1c shows the work done by a 10 member team and the response 2a shows a two-member team. The chi-square statistic is 65.6025. The p value is < 0.00001. The result is significant at p < 0.05.
The outcome of a survey on the effectiveness of innovations in online teaching–learning methods for university and college students is presented. About 450 students belonging to VIT Vellore, CMRIT Bangalore, Medical College, Pudukkottai, and engineering colleges have responded to the survey. A questionnaire designed for taking is survey is presented. The chi-square statistic is 65.6025. The p value is < 0.00001. The result is significant at p < 0.05. Associations between several responses of questions exist. The survey undertaken provides an estimate of the effectiveness and pitfalls of online teaching during the online teaching that has been taking place during the pandemic. The study done paves the way for educators to understand the effectiveness of online teaching. It is important to redesign the course delivery in an online mode to make students engaged and the outcome of the survey supports these aforementioned observations.
The outcome of the survey is given below:
A small group of 5 students would help students to have digital collaboration and engage personally in digital learning.
Animations are found to be the best digital approach for effective learning.
Online learning helps students to learn at their own pace comfortably.
Students prefer to learn from video lectures delivered by his/her faculty handling the subject.
Online quiz having multiple-choice questions (MCQ) preferred by students.
Student version software is useful for learning.
Online classes are more effective because they provide PPTs in front of every student, lectures are heard by all students at the sound level of their choice, and walking/travel to reach classes is eliminated.
Students do not have any disturbances or distractions which make learning more effective.
But for a few students, most of the students have no or limited responsibilities at home which provides a good ambiance and a nice environment for effective online learning.
Students can get their doubts clarified during lectures, by posting queries in discussion forums and by referring to online materials provided by the faculty.
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Department of Computer Science and Engineering, CMR Institute of Technology, Bangalore, 560037, India
Preethi Sheba Hepsiba Darius
School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014, India
Edison Gundabattini & Darius Gnanaraj Solomon
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Darius, P.S.H., Gundabattini, E. & Solomon, D.G. A Survey on the Effectiveness of Online Teaching–Learning Methods for University and College Students. J. Inst. Eng. India Ser. B 102 , 1325–1334 (2021). https://doi.org/10.1007/s40031-021-00581-x
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Received : 10 August 2020
Accepted : 18 March 2021
Published : 05 April 2021
Issue Date : December 2021
DOI : https://doi.org/10.1007/s40031-021-00581-x
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