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

Alamri, Ahmed and Sun, Zhongtian and Cristea, Alexandra I. and Steward, Craig and Pereira, Filipe Dawn (2021) 'MOOC next week dropout prediction: weekly assessing time and learning patterns.', ITS World Congress Hamburg, Germany.

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.

Item Type:Conference item (Paper)
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF
(307Kb)
Status:Peer-reviewed
Publisher Web site:https://itsworldcongress.com/
Date accepted:13 March 2021
Date deposited:13 April 2021
Date of first online publication:2021
Date first made open access:No date available

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