MOOCSent: a Sentiment Predictor for Massive Open Online Courses

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

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, September). MOOCSent: a Sentiment Predictor for Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain

• 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/