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MOOCSent: a Sentiment Predictor for Massive Open Online Courses

Alsheri, Mohammed A. and Alrajhi, Laila M. and Alamri, Ahmed and Cristea, Alexandra I. (2021) 'MOOCSent: a Sentiment Predictor for Massive Open Online Courses.', 29th International Conference on Information systems and Development (ISD2021) Valencia, Spain, 8-10 Sept 2021.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Date accepted:No date available
Date deposited:03 November 2021
Date of first online publication:August 2021
Date first made open access:03 November 2021

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