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.

Recommended Citation

Alshehri, M., Alrajhi, L., Alamri, A., & Cristea, A. (2021). MOOCSent: a Sentiment Predictor for Massive Open Online Courses. In E. Insfran, F. González, S. Abrahão, M. Fernández, C. Barry, H. Linger, M. Lang, & C. Schneider (Eds.), Information Systems Development: Crossing Boundaries between Development and Operations (DevOps) in Information Systems (ISD2021 Proceedings). Valencia, Spain: Universitat Politècnica de València.

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MOOCSent: a Sentiment Predictor for Massive Open Online Courses

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.