Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning

TL;DR

This paper introduces an attention-based personalized federated learning approach to improve student performance prediction across diverse demographic groups, addressing biases and enhancing accuracy using multi-modal behavioral data.

Contribution

It proposes a novel federated learning framework with subgroup personalization, self-supervised pretraining, and attention mechanisms to reduce bias and improve prediction accuracy.

Findings

Significant performance improvements over baseline models across datasets.

Effective identification of subgroup-specific activity patterns.

Enhanced generalization to underrepresented student groups.

Abstract

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.