  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.
