Automatic Assessment of Students' Classroom Engagement with Bias Mitigated Multi-task Model

TL;DR

This paper presents a bias-mitigated multi-task model for automatically assessing student engagement in online learning, improving fairness and interpretability while maintaining high accuracy.

Contribution

It introduces a novel attribute-orthogonal regularization technique to reduce bias related to sensitive features in engagement prediction models.

Findings

Significant reduction in prediction disparity between sensitive groups.

High correlation (0.999) achieved with bias mitigation compared to 0.897 without.

Enhanced interpretability of engagement prediction models.

Abstract

With the rise of online and virtual learning, monitoring and enhancing student engagement have become an important aspect of effective education. Traditional methods of assessing a student's involvement might not be applicable directly to virtual environments. In this study, we focused on this problem and addressed the need to develop an automated system to detect student engagement levels during online learning. We proposed a novel training method which can discourage a model from leveraging sensitive features like gender for its predictions. The proposed method offers benefits not only in the enforcement of ethical standards, but also to enhance interpretability of the model predictions. We applied an attribute-orthogonal regularization technique to a split-model classifier, which uses multiple transfer learning strategies to achieve effective results in reducing disparity in the distribution of prediction for sensitivity groups from a Pearson correlation coefficient of 0.897 for the unmitigated model, to 0.999 for the mitigated model. The source code for this project is available on https://github.com/ashiskb/elearning-engagement-study .