Take a MOOC and then drop: A systematic review of MOOC engagement pattern and dropout factor

Huang, Hao; Jew, Lihjen; Qi, Dandan

doi:10.1016/j.heliyon.2023.e15220

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…To our knowledge, this is the first analysis that links MOOC participation with changes in WASH service provision. MOOCs often suffer from low rates of completion [25][26][27][28]. Linking MOOC completion with continuing education credits may have contributed to higher completion rates in the DepEd programme than commonly observed in other studies.…”

Section: Discussionmentioning

confidence: 89%

Assessing the effect of a massive open online course (MOOC) on school water, sanitation, and hygiene improvements in the Philippines

Rahaman,

Marquez,

Bick

et al. 2024

Preprint

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Improving the water, sanitation, and hygiene (WASH) services in low-resources setting is a challenge. The Department of Education (DepEd) of the Philippines, supported by GIZ and UNICEF, runs the national WASH in schools program which promotes a stepwise approach to reach national WinS Standards and foster the institutionalization of WASH in the education sector. This includes national-level annual monitoring on WASH service levels in schools, information which is used to set target and allocation resources. Since 2019, the programme has also included a Massive Open Online Course (MOOC) for school staff. This platform provides uniform implementation guidance of WinS in the schools across the country. In this analysis, we use annual WASH monitoring data from the 2017/2018 (baseline) and 2021/2022 (endline) and compare this against school-level information on MOOC enrolment and completion. For each school in our analysis, we calculated baseline and endline overall and domain specific star ranking, a standardized 3-point composite measure of school WASH services adopted by DepEd. Linear regression models assessed the relationship between school staff participation in the MOOC and average change in star ranking between baseline and endline and logistic regression models were used to calculate the odds of improvement in star ranking between baseline and endline. Baseline and endline data were available for 28,779 schools. Of those, 5,980 schools had at least 1 teacher enrolled in the MOOC. Overall, MOOC participation was associated with improvements in both overall and domain specific star ranking, with larger improvements seen for hygiene services. The MOOC is a promising key component of the national WASH stategy complementing the annual monitoring process and warrants further investigation in the school management sector.

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Section: Discussionmentioning

confidence: 89%

Assessing the effect of a massive open online course (MOOC) on school water, sanitation, and hygiene improvements in the Philippines

Rahaman,

Marquez,

Bick

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Subsequent research must substantiate those results, extending the scope across interaction categories and additional achievement groups (e.g., low achievers) to gain more comprehensive insights. Bearing in mind that especially for professional learning, course completion (or attrition) does not account for the diversity of learner enrollment motivations (Moore & Blackmon, 2022;Schwerer & Egloffstein, 2016), alternative achievement groups or engagement patterns (Huang et al, 2023) need to be delineated to develop suitable design recommendations for different learner groups.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Course Design Approaches and Behavioral Patterns in Massive Open Online Courses for Professional Learning

Egloffstein,

Şahin,

Ifenthaler

2023

OLJ

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Despite their growing importance, differential, process-oriented research on Massive Open Online Courses (MOOCs) for professional learning is scarce. This paper explores learner behavior in Enterprise MOOCs using lag sequential analysis. Data from 13 MOOCs on business and technology-related topics with a total of N = 72,668 active learners were examined. Starting from consistent high-level behavioral patterns, a deeper analysis reveals variations in interaction sequences according to the underlying course design approach. Lecture-oriented, system interaction-oriented, and discussion-oriented courses share a set of common patterns but also differ in various interaction sequences. Results point towards an isolated role of video playbacks across all course clusters, consumerist patterns in lecture-oriented courses, and a positive influence of metacognitively oriented interactions on learning outcomes. Accordingly, initial design recommendations include integrating interactive instructional elements in videos, promoting learner engagement in lecture-oriented courses, and fostering metacognition. Connecting interaction and achievement data may uncover promising behavior patterns that can be further supported by course design. Based on the initial findings, implications for future research and development are discussed.

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“…Massive Open Online Courses (MOOCs), a significant innovation in teaching technology, offer a diverse array of high-quality open online courses globally [1], effectively overcoming numerous limitations of traditional offline learning in terms of cost, space, and background [2,3]. Simultaneously, the openness of MOOCs presents a novel opportunity for higher education institutions to create a richer and enhanced learning experience for learners through strengthened collaboration in knowledge sharing [4].…”

Section: Introductionmentioning

confidence: 99%

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Yang,

Liu,

Luo

et al. 2024

Applied Sciences

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As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners’ emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners’ emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses—C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners’ satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality.

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Take a MOOC and then drop: A systematic review of MOOC engagement pattern and dropout factor

Cited by 13 publications

References 35 publications

Assessing the effect of a massive open online course (MOOC) on school water, sanitation, and hygiene improvements in the Philippines

Assessing the effect of a massive open online course (MOOC) on school water, sanitation, and hygiene improvements in the Philippines

Course Design Approaches and Behavioral Patterns in Massive Open Online Courses for Professional Learning

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

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