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MOOCSent: a Sentiment Predictor for Massive Open Online Courses

Alsheri, Mohammed A.; Alrajhi, Laila M.; Alamri, Ahmed; Cristea, Alexandra I.

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Authors

Mohammed A. Alsheri

Laila Alrajhi laila.m.alrajhi@durham.ac.uk
PGR Student Doctor of Philosophy



Abstract

One key type of Massive Open Online Course (MOOC) data is the learners’ social interaction (forum). While several studies have analysed MOOC forums to predict learning outcomes, analysing learners’ sentiments in education and, specifically, in MOOCs, remains limited. Moreover, most studies focus on one platform only. Here, we propose a cross-platform MOOCs sentiment classifier using almost 1.5 million human-annotated learners’ comments obtained from 633 MOOCs delivered via the Stanford University platform and Coursera -the largest dataset collected for sentiment analysis (SA). We explore not only various state-of-the-art SA tools, but also their confidence level distributions and evaluate their performance. Our results show that the Lexicon and Rulebased (LRB) and Convolutional Neural Network (CNN)-based sentiment tools, trained mainly on social media platforms, may not be suitable for the educational domain. We further introduce MOOCSent1, a BERT-based model for predicting MOOC learners’ sentiments from their comments, which almost doubles the accuracy of the classification results, outperforming the state-of-the-art with a 95% accuracy.

Citation

Alsheri, M. A., Alrajhi, L. M., Alamri, A., & Cristea, A. I. (2021). MOOCSent: a Sentiment Predictor for Massive Open Online Courses.

Presentation Conference Type Conference Paper (Published)
Conference Name 29th International Conference on Information systems and Development (ISD2021)
Start Date Sep 8, 2021
End Date Sep 10, 2021
Publication Date 2021-09
Deposit Date Nov 3, 2021
Publicly Available Date Nov 3, 2021
Publisher Association for Information Systems
Public URL https://durham-repository.worktribe.com/output/1139485
Publisher URL https://aisel.aisnet.org/isd2014/proceedings2021/methodologies/13/

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