Mohammad Alshehri mohammad.a.alshehri@durham.ac.uk
PGR Student Doctor of Philosophy
Predicting Certification in MOOCs based on Students’ Weekly Activities
Alshehri, Mohammad; Alamri, Ahmed; Cristea, Alexandra I.
Authors
Ahmed Sarhan Alamri ahmed.s.alamri@durham.ac.uk
PGR Student Doctor of Philosophy
Professor Alexandra Cristea alexandra.i.cristea@durham.ac.uk
Professor
Contributors
Professor Alexandra Cristea alexandra.i.cristea@durham.ac.uk
Editor
Christos Troussas
Editor
Abstract
Massive Open Online Courses (MOOCs) have been growing rapidly, offering low-cost knowledge for both learners and content providers. However, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). This can impact seriously the business model of MOOCs. Nevertheless, MOOC research on learners’ purchasing behaviour on MOOCs remains limited. Thus, the umbrella question that this work tackles is if learner’s data can predict their purchasing decision (certification). Our fine-grained analysis attempts to uncover the latent correlation between learner activities and their decision to purchase. We used a relatively large dataset of 5 courses of 23 runs obtained from the less studied MOOC platform of FutureLearn to: (1) statistically compare the activities of non-paying learners with course purchasers, (2) predict course certification using different classifiers, optimising for this naturally strongly imbalanced dataset. Our results show that learner activities are good predictors of course purchasibility; still, the main challenge was that of early prediction. Using only student number of step accesses, attempts, correct and wrong answers, our model achieve promising accuracies, ranging between 0.81 and 0.95 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue
Citation
Alshehri, M., Alamri, A., & Cristea, A. I. (2021). Predicting Certification in MOOCs based on Students’ Weekly Activities. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (173-185). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_20
Online Publication Date | Jul 9, 2021 |
---|---|
Publication Date | 2021 |
Deposit Date | Apr 12, 2021 |
Publicly Available Date | Apr 13, 2021 |
Publisher | Springer Verlag |
Pages | 173-185 |
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_20 |
Public URL | https://durham-repository.worktribe.com/output/1648916 |
Contract Date | Mar 13, 2021 |
Files
Accepted Book Chapter
(227 Kb)
PDF
Copyright Statement
The final authenticated version is available online at https://doi.org/10.1007/978-3-030-80421-3_20
You might also like
Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs
(2021)
Journal Article
MOOCs Paid Certification Prediction Using Students Discussion Forums
(2022)
Book Chapter
How is Learning Fluctuating? FutureLearn MOOCs Fine-grained Temporal Analysis and Feedback to Teachers and Designers
(2018)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search