Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums

Yu, Jialin; Alrajhi, Laila; Harit, Anoushka; Sun, Zhongtian; Cristea, Alexandra I.; Shi, Lei

Abstract

Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This problem has been studied as a Natural Language Processing (NLP) problem recently, and is known to be challenging, due to the imbalance of the data and the complex nature of the task. In this paper, we explore for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference, as a new solution to assessing the need of instructor interventions for a learner’s post. We compare models based on our proposed methods with probabilistic modelling to its baseline non-Bayesian models under similar circumstances, for different cases of applying prediction. The results suggest that Bayesian deep learning offers a critical uncertainty measure that is not supplied by traditional neural networks. This adds more explainability, trust and robustness to AI, which is crucial in education-based applications. Additionally, it can achieve similar or better performance compared to non-probabilistic neural networks, as well as grant lower variance.

Citation

Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021, June). Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Intelligent Tutoring Systems
• Start Date: Jun 7, 2021
• End Date: Jun 11, 2021
• Acceptance Date: Mar 13, 2021
• Online Publication Date: Jul 9, 2021
• Publication Date: 2021
• Deposit Date: Apr 13, 2021
• Publicly Available Date: Apr 13, 2021
• Print ISSN: 0302-9743
• Pages: 78-90
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743
• Book Title: Intelligent Tutoring Systems
• DOI: https://doi.org/10.1007/978-3-030-80421-3_10
• Public URL: https://durham-repository.worktribe.com/output/1141101