MOOC next week dropout prediction: weekly assessing time and learning patterns

Alamri, Ahmed; Sun, Zhongtian; Cristea, Alexandra I.; Steward, Craig; Pereira, Filipe Dawn

Abstract

Although Massive Open Online Course (MOOC) systems have become more prevalent in recent years, associated student attrition rates are still a major drawback. In the past decade, many researchers have sought to explore the reasons behind learner attrition or lack of interest. A growing body of literature recognises the importance of the early prediction of student attrition from MOOCs, since it can lead to timely interventions. Among them, most are concerned with identifying the best features for the entire course dropout prediction. This study focuses on innovations in predicting student dropout rates by examining their next-week-based learning activities and behaviours. The study is based on multiple MOOC platforms including 251,662 students from 7 courses with 29 runs spanning in 2013 to 2018. This study aims to build a generalised early predictive model for the weekly prediction of student completion using machine learning algorithms. In addition, this study is the first to use a ‘learner’s jumping behaviour’ as a feature, to obtain a high dropout prediction accuracy.

Citation

Alamri, A., Sun, Z., Cristea, A. I., Steward, C., & Pereira, F. D. (2021). MOOC next week dropout prediction: weekly assessing time and learning patterns. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (119-130). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_15

• Online Publication Date: Jul 9, 2021
• Publication Date: 2021
• Deposit Date: Apr 12, 2021
• Publicly Available Date: Apr 13, 2021
• Publisher: Springer Verlag
• Pages: 119-130
• Series Title: Lecture Notes in Computer Science
• Series Number: 12677
• Book Title: Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings
• ISBN: 9783030804206
• DOI: https://doi.org/10.1007/978-3-030-80421-3_15
• Public URL: https://durham-repository.worktribe.com/output/1625596
• Contract Date: Mar 13, 2021