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

Yu, Jialin and Alrajhi, Laila and Harit, Anoushka and Sun, Zhongtian and Cristea, Alexandra I. and Shi, Lei (2021) 'Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums.', Intelligent Tutoring Systems Athens, Greece / Virtual, 7-11 Jun 2021.

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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-030-80421-3_10
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-030-80421-3_10
Date accepted:13 March 2021
Date deposited:13 April 2021
Date of first online publication:09 July 2021
Date first made open access:13 July 2021

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