mlsanityData to 7 Oct 2026

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

arXiv 2104.12643 ↗First posted 26 Apr 2021cs.CL cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 46 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

19 of 46 references.

References · 19

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  3. 2016Variational Inference: A Review for Statisticianscited by 1,466
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  11. 2015Neural Variational Inference for Text Processingcited by 111
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7 more
  1. 2015Predictive Entropy Search for Bayesian Optimization with Unknown Constraintscited by 51
  2. 2017Deep and Confident Prediction for Time Series at Ubercited by 50
  3. 2018A Tutorial on Deep Latent Variable Models of Natural Languagecited by 10
  4. 2019EduBERT: Pretrained Deep Language Models for Learning Analyticscited by 3
  5. 2020Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activitiescited by 2
  6. 2015Learning Instructor Intervention from MOOC Forums: Early Results and Issuescited by 1
  7. 2020Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approachcited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2024Explainability through uncertainty: Trustworthy decision-making with neural networkscited by 12
  2. 2023Towards Generalizable Detection of Urgency of Discussion Forum Postscited by 3
  3. 2022Incorporating Emotions into Health Mention Classification Task on Social Mediacited by 0
  4. 2022Religion and Spirituality on Social Media in the Aftermath of the Global Pandemiccited by 0