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MOOCs Paid Certification Prediction Using Students Discussion Forums

Alshehri, Mohammad; Cristea, Alexandra I.

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Authors



Contributors

Maria Mercedes Rodrigo
Editor

Noburu Matsuda
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

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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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