Predictive Modeling of Dropout in MOOCs Using Machine Learning Techniques
Keywords:
Machine Learning, Predictive Modeling, Dropout Prediction, MOOCs, Learning AnalyticsAbstract
The advent of Massive Open Online Courses (MOOCs) has revolutionized education, offering unprecedented access to high-quality learning materials globally. However, high dropout rates pose significant challenges to realizing the full potential of MOOCs. This study explores machine learning techniques for predicting student dropout in MOOCs, utilizing the Open University Learning Analytics Dataset (OULAD). Seven algorithms, including decision tree, random forest, Gaussian naïve Bayes, AdaBoost Classifier, Extra Tree Classifier, XGBoost Classifier, and Multilayer Perceptron (MLP), are employed to predict student outcomes and dropout probabilities. The XGBoost classifier emerges as the top performer, achieving 87% accuracy in pass/fail prediction and 86% accuracy in dropout prediction. Additionally, the study proposes personalized interventions based on individual dropout probabilities to enhance student retention. The findings underscore the potential of machine learning in addressing dropout challenges in MOOCs and offer insights for instructors and educational institutions to proactively support at-risk students.
Downloads
References
Xing, W. and D. Du, Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. Journal of Educational Computing Research, 2018. 57(3): p. 547-570.
Xing, W., et al., Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 2016. 58: p. 119-129.
Lemay, D.J. and T. Doleck, Predicting completion of massive open online course (MOOC) assignments from video viewing behavior. Interactive Learning Environments, 2020: p. 1-12.
Lee, J., A. Hong, and J. Hwang, A Review of Massive Open Online Courses: MOOC's Approach to Bridge the Digital Divide. 2018.
Thulasi, B.S., Analysis on Massive Open Online Course (MOOC) Dropout Prediction Using Machine Learning Techniques The State of the Art. The International journal of analytical and experimental modal analysis, 2020. XII(III): p. 1287-1290.
Joshi, M., Adaptive Learning through Artificial Intelligence. International Journal on Integrated Education, 2024. 7: p. 41-43.
Jeon, B., Park, Namyong , and S. Bang, Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning. arXiv:2002.01598 [cs.LG], 2020.
Feng, W., J. Tang, and T.X. Liu, Understanding Dropouts in MOOCs. Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 33(01): p. 517-524.
Laura, P., The year of the MOOC. The New York Times, in The New York Time 2012.
Mubarak, A.A., H. Cao, and S.A.M. Ahmed, Predictive learning analytics using deep learning model in MOOCs’ courses videos. Education and Information Technologies, 2021. 26(1): p. 371-392.
Gupta, S. and A.S. Sabitha, Deciphering the attributes of student retention in massive open online courses using data mining techniques. Education and Information Technologies, 2019. 24(3): p. 1973-1994.
Sanchez-Gordon, S. and S. Lujan-Mora, How Could MOOCs Become Accessible? The Case of edX and the Future of Inclusive Online Learning. Journal of Universal Computer Science, 22(1), 55–81., 2016. 22(1): p. 55-81.
Gupta, R. and N. Sambyal, An understanding Approach towards MOOCs. International Journal of Emerging Technology and Advanced Engineering, 2013. 3(6): p. 312-315.
Itani, A., L. Brisson, and S. Garlatti. Understanding Learner’s Drop-Out in MOOCs. in Intelligent Data Engineering and Automated Learning – IDEAL 2018. 2018. Cham: Springer International Publishing.
Ali, A.J.I.J.o.M.S. and Arts, Exploring the Transformative Potential of Technology in Overcoming Educational Disparities. 2023. 2(1).
Lambert, S.R., Do MOOCs contribute to student equity and social inclusion? A systematic review 2014–18. Computers & Education, 2020. 145: p. 103693.
Guerrero, M., S. Heaton, and D. Urbano, Building universities’ intrapreneurial capabilities in the digital era: The role and impacts of Massive Open Online Courses (MOOCs). Technovation, 2021. 99: p. 102139.
Biswas, S. and M. Sarkar, MOOC : Challenges & Prospects in Indian Higher Education. Journal of Information and Computational Science, 2022. 10(2): p. 1015-1024.
Mourdi, Y., et al., A Machine Learning Based Approach to Enhance MOOC Users' Classification. Turkish Online Journal of Distance Education, 2020. 21(2): p. 47-68.
Onah, D.F., J. Sinclair, and Boyatt. Dropout rates of massive open online courses: Behavioral pattens of MOOC droput and completion: Existing evalutaion. in 6th International Conference on Education and New Learning Technologies (EDULEARN14). 2014.
Gardner, J. and C. Brooks, Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 2018. 28(2): p. 127-203.
Bergdahl, N., Engagement and disengagement in online learning. Computers & Education, 2022. 188: p. 104561.
Mourdi, Y., et al., A Multi-Layers Perceptron for predicting weekly learner commitment in MOOCs. Journal of Physics: Conference Series, 2021. 1743: p. 012027.
Boyer, S., et al. Data science foundry for MOOCs. in 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 2015.
Taylor, C., K. Veeramachaneni, and U.M. O’Reilly, Likely to stop? Predicting stopout in massive open online courses. arXiv:1408.3382 [cs.CY], 2014.
He, J., et al., Identifying At-Risk Students in Massive Open Online Courses. Proceedings of the AAAI Conference on Artificial Intelligence, 2015. 29(1).
Kizilcec, R.F., C. Piech, and E. Schneider., Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. in Proceedings of the third international conference on learning analytics and knowledge, 2013.
Mubarak, A.A., H. Cao, and W. Zhang, Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 2020: p. 1-20.
Santana, M., et al., Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 2017. 73.
Li, Q., R. Baker, and M. Warschauer, Using clickstream data to measure, understand, and support self-regulated learning in online courses. The Internet and Higher Education, 2020. 45: p. 100727.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
