Massive Open Online Courses Completion Research Articles

Development, Feasibility, and Knowledge Impact of a Massive Open Online Course on Radiation Safety: A Multicentre Prospective Cohort Study

This study reports the development of an innovative, interactive Massive Open Online Course (MOOC) teaching radiation safety principles in the vascular workplace, using stepwise e-learning with multiple choice question tests (MCQs), educational videos, and a serious game. The aim was to study the MOOC impact on radiation safety knowledge and assess its feasibility and acceptability. An international multicentre prospective study included team members active in the hybrid operating room. The MOOC was offered voluntarily via a secured online learning platform. A standardised MCQ test (15 questions) assessed radiation safety knowledge pre- and post-course (range 0 - 100%). Acceptability and feasibility were tested via the previously validated evaluating e-learning system success (EESS) model, using five point Likert scales. In eight centres across four countries, 150 out of 203 invited endovascular team members consented. Over a seven week study period, surgeons (28%, including vascular surgery trainees and consultants), nurses (27%, including scrub/circulating and anaesthesiology nurses), anaesthesiologists (43%, including trainees and consultants), and radiographers (3%) participated. Of those, 67% completed the course. The average radiation knowledge improved by 22.8% (95% CI 19.5 - 26.0%; p < .001) after MOOC completion, from 48 to 71% (standard deviation [SD] 14 and 15% respectively), requiring a mean time investment of 169 minutes (SD 89 minutes). In centres with a radiographer, mean knowledge gain after MOOC completion was significantly smaller (14%, SD 19% vs. 24%, SD 16%, p = .036). The course was deemed feasible and acceptable according to the EESS model with a total mean score of 3.68/5. This newly developed, multimodal MOOC was deemed feasible and effective across multiple international centres. The MOOC significantly contributes to radiation safety education of the entire endovascular team, improving radiation safety knowledge. The course may optimise workplace radiation safety behaviour and therefore enhance team and patient safety.

Motivational Factors that Influence the Course Completion Rate of Massive Open Online Courses in South Africa

Massive open online courses (MOOCs) have increased access to higher education by allowing South Africans to access free, online-based, open content created by higher education institutions worldwide. However, most MOOCs report significant student drop-out rates before completing a course. Higher education institutions must understand learner motivation for completing a MOOC. This paper examines the motivational factors influencing the completion rate of MOOCs in South Africa. This study employed a quantitative approach to collect data using an online questionnaire from South African respondents. A total number of 3147 responses were recorded, and the data were analyzed with SPSS V28. Correlation statistics tests were used to denote the association between the four independent variables and the dependent variable. The study's most important findings are that intrinsic and extrinsic motivating factors, motivation to continue, and the availability of resources positively impact a MOOC's completion rate. The study concludes that these factors will improve the throughput rate of MOOCs. It is recommended that all higher education institutions that offer MOOCs create a conducive online learning environment that offers independence and freedom of learning with plenty of communication and collaboration between students and facilitators. Creating such an environment will encourage active participation in the course and improve throughput rates.

Analyzing Log Data of Students Who Have Achieved Scores Adjacent to the Minimum Passing Grade for a K-MOOC Completion in the Context of Learning Analytics

Collecting and analyzing log data can provide students with individualized learning to maintain their motivation and engagement in learning activities and reduce dropout in Massive Open Online Courses (MOOCs). As online learning becomes more and more important, the demand for learning analytics is surging to design a variety of interventions that can achieve learning success and achieve individual learning goals and targets. In response to significant demand, we intended to derive data standards for learning analytics by specifying more the factors influencing MOOC completion suggested in previous research results. Therefore, this study aims to compare the event logs of students who have achieved scores adjacent to the minimum passing score of Korean Massive Open Online Course (K-MOOC) completion by dividing them into the completion (C) group and the non-completion (NC) group. As a result of analyzing the log data accumulated on the 60 K-MOOCs, what is interesting in the results of this study is that there was no significant difference between the C group and the NC group in video viewing, which is considered the main learning activity on the MOOC platform. On the other hand, there was a statistically significant difference between the C group and the NC group for textbook interactions in the percentage of learners who performed and the average number of logs per learner, as well as problem interactions in the average number of logs per learner. Students’ assertive activities such as textbook interaction and problem interaction might have greater value for MOOC completion than passive activities such as video watching. Therefore, MOOC instructors and developers should explore more specific design guidelines on how to provide problems with individualized hints and feedback and offer effective digital textbooks or reference materials for the large number of students. The results suggest that collecting and analyzing MOOC students’ log data on interactions, for understanding their motivation and engagement, should be investigated to create an individualized learning environment and increase their learning persistence in completing MOOCs. Future studies should focus on investigating meaningful patterns of the event logs on learning activities in massive quantitative and qualitative data sets.

EXPLORING THE IMPACT OF LEARNERS’ DEMOGRAPHIC CHARACTERISTICS ON COURSE COMPLETION AND DROPOUT IN MASSIVE OPEN ONLINE COURSES

MOOCs (Massive Open Online Courses) have the potential to change education by offering high-quality online courses in a variety of disciplines. However, just a few studies have investigated the impact of MOOC students' demographics on their completion rates. In this research, we investigate the impact of demographic features of learners in the completion and dropping rate of MOOCs. The data from a survey is used in this study to determine which learner demographic features may have an impact on MOOC completion and dropout. The data was analyzed using Chi-square test.The findings demonstrate that four factors, including gender, marital status, age, and educational level, have no impact on degree of MOOC completion. It was also discovered that marital status, age, and educational level, have no effect on MOOC dropout. However, we discovered a statistically significant link between gender and MOOC dropout (χ2 = 6.347, df = 1, p = 0.012). These results can be considered in future instructional initiatives.

Measuring online self-regulated learning among early-career medical doctors in a Massive Open Online Course on COVID-19

Introduction: As significant autonomy is given in a Massive Open Online Course (MOOC), online self-regulated learning (SRL) ability is crucial in such courses. We aim to measure the online SRL abilities of early-career medical doctors enrolled in a MOOC. Methods: We performed a cross-sectional study using the Self-Regulated Online Learning Questionnaire-revised version (SOL-Qr). We conducted a three-stage cross-cultural validation of the SOL-Qr, followed by Confirmatory Factor Analysis (CFA). The online SRL ability of 5,432 medical doctors enrolled in a MOOC was measured using the validated SOL-Qr. Results: The CFA of the cross-translated SOL-Qr confirmed its comparability to the original version, with excellent validity &amp; reliability. Participants showed high levels of online SRL during their early careers. Despite high online SRL scores, MOOC completion rate was low. Male participants showed slightly better time management ability than female participants. Participants working in the primary epicentrum for COVID-19 in the country showed lower online SRL scores, while participants who graduated from higher accreditation levels showed better time management ability. Conclusion: The SOL-Qr and its subscales are suitable and valid for measuring the online SRL abilities of medical doctors in a MOOC during their early-career period. Time management ability was associated with previous experience during the medical education period, while other online SRL subscales were mostly associated with workload. However, as the scores did not correlate with the time spent for learning in MOOC, the corresponding learning effort or time spent may be beyond just the commitment to the described MOOC.

Measuring online self-regulated learning among early-career medical doctors in a Massive Open Online Course on COVID-19

Introduction: As significant autonomy is given in a Massive Open Online Course (MOOC), online self-regulated learning (SRL) ability is crucial in such courses. We aim to measure the online SRL abilities of early-career medical doctors enrolled in a MOOC. Methods: We performed a cross-sectional study using the Self-Regulated Online Learning Questionnaire-revised version (SOL-Qr). We conducted a three-stage cross-cultural validation of the SOL-Qr, followed by Confirmatory Factor Analysis (CFA). The online SRL ability of 5,432 medical doctors enrolled in a MOOC was measured using the validated SOL-Qr. Results: The CFA of the cross-translated SOL-Qr confirmed its comparability to the original version, with excellent validity &amp; reliability. Participants showed high levels of online SRL during their early careers. Despite high online SRL scores, MOOC completion rate was low. Male participants showed slightly better time management ability than female participants. Participants working in the primary epicentrum for COVID-19 in the country showed lower online SRL scores, while participants who graduated from higher accreditation levels showed better time management ability. Conclusion: The SOL-Qr and its subscales are suitable and valid for measuring the online SRL abilities of medical doctors in a MOOC during their early-career period. Time management ability was associated with previous experience during the medical education period, while other online SRL subscales were mostly associated with workload. However, as the scores did not correlate with the time spent for learning in MOOC, the corresponding learning effort or time spent may be beyond just the commitment to the described MOOC.

Factors Associated with Student Enrollment, Completion, and Dropout of Massive Open Online Courses in the Sultanate of Oman

This research aims to investigate the elements that influence learners' enrollment in, completion of and dropout from Massive Open Online Courses (MOOCs). MOOCs attract a vast number of participants, with enrollments growing at an unprecedented rate; however, only a small number of students who enroll, complete their study. In this research, data were collected from 79 participants using an online survey. Professional growth, flexible time, free of charge, and the opportunity to obtain certification were recognized as the four main factors that directly influenced student enrollment in MOOCs. The motivational variables revealed in this study that led to the effective completion of MOOCs included good-quality course videos, straightforward and clear tutor instructions, good course delivery, and useful instructor feedback, among others. The most prevalent reasons for dropout included failure to fulfil the deadline, the instructor's vocabulary, challenging assignments, and difficulty in grasping the course content. The findings of this study can provide educators and decision-makers with the information they need to better understand the factors that influence MOOC student enrollment, completion, and dropout rates, as well as relevant actions to improve the success rates of MOOC.

Trends and Future Prospects in MOOC Researches: A Systematic Literature Review 2013–2020

Advent of internet has a revolutionary impact on every sector of economy and so is with the education sector, which has witnessed the evolution of online education through scalable educational technology, namely, Massive Open Online Courses (MOOCs). The purpose of this paper is to summarize the pool of available knowledge related to MOOCs in the scholarly literature, published during the period 2013–2020. One hundred and two available published studies in peer reviewed journals & conferences searched in major academic databases have been reviewed and presented in the systematic literature review. The findings have been divided under the various research themes of MOOCs including geographical contribution of MOOC studies, citation trends, prominent research themes, theoretical frameworks and methodological rigor in the existing scholarly works. The results demonstrate that most empirical research were conducted by the researchers having affiliation to institutions based out of United States followed by institutions in the developing economies of China and India. The most focused area of research in MOOCs is on MOOC adoption. Limited research has happened on poor MOOC completion status, the instructor-related topics and on democratization of MOOC. Thrust areas of MOOC researches and future research directions are also discussed.

Taking Away Excuses to Quit: The Role of Supports in Completion and Learning in Online Professional Development for Teachers

Online courses, particularly in the massive open online course (MOOC) format, have been lauded for their potential to democratize access to educational opportunities but criticized for their markedly low completion rates. Yet educators continue to enroll in online courses, including MOOCs, in high numbers. For teachers at under-resourced schools, free online courses may be the only professional development option. It thus remains important to understand whether online courses, in their various formats, can serve as vehicles to support teacher learning and whether this can happen on a large-scale. Extending prior research that explores the relationship between contextual factors, engagement, and learning in online settings, this mixed-method study examines outcomes in a MOOC designed for teachers of English learners (ELs). In particular, the study identifies and examines structural and social supports that were available to some course participants (release time, stipends, participating with colleagues, and having a facilitator who convened face-to-face meetings) and investigates whether these local conditions were significantly related to completion and learning. Findings indicate that participants who received more supports were significantly more likely to complete the course. While participants, on average, showed evidence of learning, participants receiving supports did not show evidence of learning more than other participants. This is potentially due to omitted variable bias because participants who completed the course without supports may differ from participants who completed the course with supports in important, unaccounted for ways. This study extends prior research about how learning environments impact online learning experiences and suggests that structural and social supports may be useful in facilitating MOOC completion.

Understanding learners' completion intention of massive open online courses (MOOCs): role of personality traits and personal innovativeness

PurposeThe present study investigates the influence of five personality traits based on the Big Five model, i.e. extraversion, neuroticism, conscientiousness, agreeableness and openness to experience and personal innovativeness on learners' intention to complete MOOCs.Design/methodology/approachThe proposed model was empirically tested using cross-sectional design. The primary data were gathered through a survey of 515 students at five higher educational institutions in the National Capital Region of Delhi, India. Structural equation modelling was used to test the hypothesized relationships in the proposed model.FindingsThe study has found significant associations between the personality traits, personal innovativeness and MOOC completion intention. The findings indicate that extraversion, conscientiousness, agreeableness, openness to experience and personal innovativeness have significant positive effects whereas neuroticism has no significant effect on MOOCs continuance intention. Personal innovativeness has been found to mediate the relationships between all the personality traits and MOOCs completion intention of learners.Practical implicationsOur findings can potentially address the issue of drop-out rates of MOOCs in developing countries such as India. The findings of the study are helpful for MOOCs providers and other stakeholders. The MOOC developers should develop courses with wide scope so as to accommodate learners with different personality profiles. Additionally, during the enrolment process, MOOC providers can identify the students' attributes through personality tests. This will help in customizing the study material and teaching pedagogy as per the needs of the students.Originality/valueThe study contributes to the growing area of research in MOOCs usage by exploring the influence of personality traits on learners' behaviour towards completing MOOCs. Since the learners' intention to complete MOOCs is a major concern for MOOC developers, hence the present study makes a worthwhile contribution as it is a relatively under-researched area.

Adoption, completion and continuance of MOOCs: a longitudinal study of students’ behavioural intentions

ABSTRACT The purpose of this study is to investigate the factors underlying the adoption, completion and continuance of massive open online courses (MOOCs), using technology-user-environment (TUE) framework, MOOCs features and MOOCs qualities. The proposed model was empirically tested using longitudinal research design. The primary data was gathered at three time points through a survey of 197 students at an Indian university. Stepwise regression analysis was used to test the hypothesised relationships in the proposed model. The results demonstrate that technological characteristics, user characteristics and MOOCs features influence the students’ decisions in all the stages of MOOCs acceptance including initial adoption, completion and continuance. Environmental factors determine the students’ initial intentions to adopt MOOCs, whereas MOOCs qualities predict their intentions to complete and continue the usage of MOOCs in future. Moreover, the intention to adopt MOOCs significantly predicts the intention to complete MOOCs which in turn significantly determines the intention to continue using MOOCs. These findings will be helpful for MOOCs providers and other stakeholders. The MOOCs providers can focus on the technological characteristics, user characteristics and MOOCs features as they carry a sustained influence on the students’ intentions to adopt, complete as well as continue using MOOCs.

Lessons learned from the first worldwide accessible e-learning in Landscape Ecology

Massive open online courses (MOOCs) are distance learning tools for individualized learning. They allow students to learn at their own pace in a virtual classroom. We describe success and pitfalls of the MOOC Landscape Ecology, designed as an undergraduate University course taught by an international consortium of Professors covering theory and application of the field. The paper describes course performance with summary metrics, illustrates contents and didactic tools, and provides a list of suggestions for instructors who engage in distant learning. We identify the following five key success factors for this and related MOOCs: (1) commitment and passion of an international consortium of lecturers; (2) a sound mixture of theory and practice; (3) numerous field-videos; (4) content and skill-oriented practicums (here using R, GIS, remote sensing); and (5) interactive formats where students discuss and share their opinions. In all runs of our MOOC we experienced some difficulties with peer-assessed writing tasks due to widely differing “review cultures”. The instructor-paced MOOC attracted over 3500 students in 2018 and 2019, and had comparably high completion rates (14% and 11%, respectively), compared to typical MOOC completion rates ranging from 5% to 15%. Completion rates in our self-paced run in 2020 were 8-9% only.

Examining the Perceived Consequences and Usage of MOOCs on Learning Effectiveness

Massive Open Online Courses (MOOCs) have recently received a great deal of attention from the academic communities. However, these courses face low completion rates and there are very limited research pertaining to this problem. Therefore, this study uses Triandis theory to better understand variables that are indicative of MOOC completion. Furthermore, this study scrutinizes the quantitative relationship between MOOC usage and learning effectiveness. Two hundred and thirty-four users from selected Coursera participated in this study to evaluate the proposed model. The partial least squares (PLS) were used to analyze the collected data and test the research hypotheses. The results indicated that perceived consequences (including knowledge growth, social interaction, and compatibility) and affect have a significant impact on intention to use MOOC. In contrast, social factors delineated the insignificant effects on intention to use MOOC. The findings indicated that facilitative conditions and intentions to use MOOC have a strong and positive impact on the actual use of MOOC. Hypotheses regarding the influence of perceived consequences and the actual usage of MOOC on learning effectiveness were upheld.

Influence of learner motivational dispositions on MOOC completion

This study examined the role motivational dispositions had on completing a massive open online course (MOOC) using identifiable data from 10,726 students who enrolled in an iteration of the HarvardX MOOC, Super Earths and Life. As part of the course registration process, learners had the option to complete a pre-course survey and self-report information including their level of education, gender and registration motivations. Using these pre-course survey responses, latent profiles linked to learners’ course performance were created. Results showed education background, gender, and motivation were all significantly related to students’ performance. Furthermore, students with intrinsic motivational dispositions performed better than students with extrinsic dispositions, and females performed better than males.

Predicting completion of massive open online course (MOOC) assignments from video viewing behavior

ABSTRACT Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching (ff, rw, pause), replays, stop, and start. Deep learning neural networks are susceptible to overfitting with low data and higher dimensions; hence, we compare various commonly used classification algorithms including logistic regression and demonstrate similar or higher rates of prediction. However, using a path analysis approach we find that the features collectively explain only a small to moderate amount of variance in assignment completion, which suggests that other factors than video-viewing behavior influence assignment completion such as student goal motivation and student self-regulation. Overall, our findings highlight the important contribution of active searching and repeated viewing to successful assignment completion in a MOOC course. Predictive models based on user interactions with the MOOC platform can help target course retention strategies to increase MOOC completion where retention is abysmally low and help to target video viewing strategies to optimize teaching and learning platform functionality using adaptive agents.

How Knowledge Stock Exchanges can increase student success in Massive Open Online Courses.

Massive Open Online Courses (MOOCs) allow lecturers to overcome spatiotemporal boundaries and reach large numbers of participants. However, the completion rates of MOOCs are relatively low, a critical obstacle to their ultimate success. Existing literature suggests that strengthening student interaction has the potential to increase student commitment. The goal of this study is to develop a novel, market-based knowledge-sharing method that fosters student engagement and interaction in MOOCs, addressing the problem of low completion rates and demonstrating how MOOC engagement can lead to greater student success. The proposed method, “Knowledge Stock Exchange” (KSX), is derived from the concept of crowd-based intelligence mechanisms for incentive-compatible information aggregation. Using a popular MOOC as the focus of our empirical study, we show that the KSX method increases student interaction as well as MOOC completion rates. Moreover, we find that KSX participation has a significant positive effect on participants’ exam grades.

Predicting Learning Outcomes with MOOC Clickstreams

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in education. Since 2014, the Ministry of Education in Taiwan has been promoting MOOC programs, with successful results. The ability of students to work at their own pace, however, is associated with low MOOC completion rates and has recently become a focus. The development of a mechanism to effectively improve course completion rates continues to be of great interest to both teachers and researchers. This study established a series of learning behaviors using the video clickstream records of students, through a MOOC platform, to identify seven types of cognitive participation models of learners. We subsequently built practical machine learning models by using K-nearest neighbor (KNN), support vector machines (SVM), and artificial neural network (ANN) algorithms to predict students’ learning outcomes via their learning behaviors. The ANN machine learning method had the highest prediction accuracy. Based on the prediction results, we saw a correlation between video viewing behavior and learning outcomes. This could allow teachers to help students needing extra support successfully pass the course. To further improve our method, we classified the course videos based on their content. There were three video categories: theoretical, experimental, and analytic. Different prediction models were built for each of these three video types and their combinations. We performed the accuracy verification; our experimental results showed that we could use only theoretical and experimental video data, instead of all three types of data, to generate prediction models without significant differences in prediction accuracy. In addition to data reduction in model generation, this could help teachers evaluate the effectiveness of course videos.

Exploring Demographics and Students’ Motivation as Predictors of Completion of a Massive Open Online Course

This paper investigates the degree to which different variables affect the completion of a Massive Open Online Course (MOOC). Data on those variables, such as age, gender, English proficiency, education level, and motivation for course enrollment were first collected through a pre-course survey. Next, course completion records were collected via the Coursera database. Finally, multiple binomial logistic regression models were used to identify factors related to MOOC completion. Although students were grouped according to their preferences, working in groups did not affect students’ likelihood for MOOC completion. Also, other variables such as age, the institution hosting the MOOC, academic program alignment with students’ needs, and students’ intention to complete the course all affected their probability of MOOC completion. This study contributes to the literature by indicating the factors that influence the probability of MOOC completion. Results show that older participants (age &gt; 50 years old) have higher probability of completing the MOOC. Students’ MOOC completion also increases when the MOOC provides experiences that add to students’ current academic backgrounds and when they are hosted by institutions with a strong academic reputation. Based on these factors, this study contributes to research methods in MOOCs by proposing a model that is aligned with the most important factors predicting completion as recommended by the current MOOC literature. For the next phase of assigning learners to work in groups, findings from this study also suggest that MOOC instructors should provide assistance for group work and monitor students’ collaborative processes.

Student’s social e-reputation (“karma”) as motivational factor in MOOC learning

ABSTRACT In this paper, we analyse the role of the student’s digital reputation as a motivational factor for successfully completing Massive Open Online Courses (MOOCs). After a review of the academic literature on the role that the student’s reputation plays in community learning and to understand the role of involving the student that new techniques of gamification have taken in virtual learning, an empirical analysis is performed on the basis of data from a pioneer MOOC of Social Entrepreneurship (5016 registered students, 3250 of whom actually started). Using Classification and Regression Trees (CARTs) and Principal Component Analysis (PCA), we conclude that social reputation is a key factor for the student in successfully completing the course, and that the student’s retention is, along with his e-reputation (“karma”), directly related to his degree of participation, the rewards received, and his correspondence with peers. This shows that one of the factors that explain the student’s MOOC completion is his degree of interaction with other students, and particularly his reputation among them.

Goal Setting and MOOC Completion

Despite providing advanced coursework online to learners around the world, massive open online courses (MOOCs) have had notoriously low completion rates. Self-regulated learning (SRL) frames strategies that students can use to enhance motivation and promote their engagement, persistence, and performance self-monitoring. Understanding which SRL subprocesses are most relevant to the MOOC learning context can guide course designers and instructors on how to incorporate key SRL aspects into the design and delivery of MOOCs. Through surveying 643 MOOC students using the Online Self-Regulated Learning Questionnaire (OSLQ), the present study sought to understand the differences in the use of SRL between those who completed their course and those who did not. MOOC completers were found to have significantly higher applications of one SRL specific subprocess, namely goal setting. Additional SRL subprocesses of task interest/values, causal attribution, time management, self-efficacy, and goal-orientation also emerged from an analysis of open-ended responses as key contributors to course completion. The findings from this study provide further support regarding the role of SRL in MOOC student performance and offer insight into learners’ perceptions on the importance of SRL subprocesses in reaching course completion.