Differentiating Student Feedbacks for Knowledge Tracing

TL;DR

This paper introduces a novel framework for knowledge tracing that reweights student responses based on their discriminative power and adaptively fuses predictive scores, improving model accuracy and personalized tracking.

Contribution

It proposes a response reweighting and adaptive score fusion framework to address imbalanced response discrimination in knowledge tracing models.

Findings

Enhanced KT accuracy on multiple datasets

Improved balance between knowledge mastery and question difficulty

Outperforms existing KT methods

Abstract

Knowledge tracing (KT) is a crucial task in computer-aided education and intelligent tutoring systems, predicting students' performance on new questions from their responses to prior ones. An accurate KT model can capture a student's mastery level of different knowledge topics, as reflected in their predicted performance on different questions. This helps improve the learning efficiency by suggesting appropriate new questions that complement students' knowledge states. However, current KT models have significant drawbacks that they neglect the imbalanced discrimination of historical responses. A significant proportion of question responses provide limited information for discerning students' knowledge mastery, such as those that demonstrate uniform performance across different students. Optimizing the prediction of these cases may increase overall KT accuracy, but also negatively impact the model's ability to trace personalized knowledge states, especially causing a deceptive surge of performance. Towards this end, we propose a framework to reweight the contribution of different responses based on their discrimination in training. Additionally, we introduce an adaptive predictive score fusion technique to maintain accuracy on less discriminative responses, achieving proper balance between student knowledge mastery and question difficulty. Experimental results demonstrate that our framework enhances the performance of three mainstream KT methods on three widely-used datasets.