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

arXiv: https://arxiv.org/abs/2104.12643 · HTML: https://mlsanity.com/paper/2104.12643/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 19 of 19)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks](https://mlsanity.com/paper/1502.05336/) (2015)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](https://mlsanity.com/paper/1512.05287/) (2015)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Quantifying Uncertainties in Natural Language Processing Tasks](https://mlsanity.com/paper/1811.07253/) (2018)
- [Predictive Entropy Search for Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1502.05312/) (2015)
- [Deep and Confident Prediction for Time Series at Uber](https://mlsanity.com/paper/1709.01907/) (2017)
- [A Tutorial on Deep Latent Variable Models of Natural Language](https://mlsanity.com/paper/1812.06834/) (2018)
- [EduBERT: Pretrained Deep Language Models for Learning Analytics](https://mlsanity.com/paper/1912.00690/) (2019)
- [Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities](https://mlsanity.com/paper/2008.05849/) (2020)
- [Learning Instructor Intervention from MOOC Forums: Early Results and Issues](https://mlsanity.com/paper/1504.07206/) (2015)
- [Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach](https://mlsanity.com/paper/2008.05209/) (2020)

## Built on by (most cited first, 4 of 4)

- [Explainability through uncertainty: Trustworthy decision-making with neural networks](https://mlsanity.com/paper/2403.10168/) (2024)
- [Towards Generalizable Detection of Urgency of Discussion Forum Posts](https://mlsanity.com/paper/2307.07614/) (2023)
- [Incorporating Emotions into Health Mention Classification Task on Social Media](https://mlsanity.com/paper/2212.05039/) (2022)
- [Religion and Spirituality on Social Media in the Aftermath of the Global Pandemic](https://mlsanity.com/paper/2212.11121/) (2022)

## 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.