Mohammad Alshehri mohammad.a.alshehri@durham.ac.uk
PGR Student Doctor of Philosophy
MOOCs Paid Certification Prediction Using Students Discussion Forums
Alshehri, Mohammad; Cristea, Alexandra I.
Authors
Professor Alexandra Cristea alexandra.i.cristea@durham.ac.uk
Professor
Contributors
Maria Mercedes Rodrigo
Editor
Noburu Matsuda
Editor
Professor Alexandra Cristea alexandra.i.cristea@durham.ac.uk
Editor
Vania Dimitrova
Editor
Abstract
Massive Open Online Courses (MOOCs) have been suffering a very level of low course certification (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate), although MOOC platforms have been offering low-cost knowledge for both learners and content providers. While MOOCs discussion forums’ rich numeric and textual data are typically utilised to address many MOOCs challenges, e.g., high dropout rate, identifying intervention-needed learners, analysing learners’ forum discussion and interaction to predict certification remains limited. Thus, this paper investigates if MOOC discussion forum-based data can predict learners’ purchasing decisions (certification). We use a relatively large dataset of 23 runs of 5 FutureLearn MOOCs for temporal (weekly-based) prediction, achieving promising accuracies in this challenging task: 76% on average, across the five courses.
Citation
Alshehri, M., & Cristea, A. I. (2022). MOOCs Paid Certification Prediction Using Students Discussion Forums. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (542-545). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_111
Online Publication Date | Jul 26, 2022 |
---|---|
Publication Date | 2022 |
Deposit Date | Sep 26, 2022 |
Publicly Available Date | Jul 27, 2023 |
Publisher | Springer Verlag |
Pages | 542-545 |
Series Title | Lecture Notes in Computer Science |
Series Number | 13356 |
Book Title | Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium |
ISBN | 978-3-031-11646-9 |
DOI | https://doi.org/10.1007/978-3-031-11647-6_111 |
Public URL | https://durham-repository.worktribe.com/output/1621003 |
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Copyright Statement
The final authenticated version is available online at https://doi.org/10.1007/978-3-031-11647-6_111
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