Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs

Research Projects
Organizational Units
Journal Issue
MOOCs (Massive Open Online Courses) have usually high dropout rates. Many articles have proposed predictive models in order to early detect learners at risk to alleviate this issue. Nevertheless, existing models do not consider complex high-level variables, such as self-regulated learning (SRL) strategies, which can have an important effect on learners' success. In addition, predictions are often carried out in instructor-paced MOOCs, where contents are released gradually, but not in self-paced MOOCs, where all materials are available from the beginning and users can enroll at any time. For self-paced MOOCs, existing predictive models are limited in the way they deal with the flexibility offered by the course start date, which is learner dependent. Therefore, they need to be adapted so as to predict with little information short after each learner starts engaging with the MOOC. To solve these issues, this paper contributes with the study of how SRL strategies could be included in predictive models for self-paced MOOCs. Particularly, self-reported and event-based SRL strategies are evaluated and compared to measure their effect for dropout prediction. Also, the paper contributes with a new methodology to analyze self-paced MOOCs when carrying out a temporal analysis to discover how early prediction models can serve to detect learners at risk. Results of this article show that event-based SRL strategies show a very high predictive power, although variables related to learners' interactions with exercises are still the best predictors. That is, event-based SRL strategies can be useful to predict if e.g., variables related to learners' interactions with exercises are not available (...)
Data sience applications in education, Distance education and online learning, Lifelong learning, Post-secondary education
Bibliographic citation
Computers & Education (2020), 145, 103728