Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method

TL;DR

This paper introduces an attention-based knowledge tracing method that incorporates domain knowledge of concept routes, improving personalized learning analysis by considering dependencies between knowledge concepts.

Contribution

The paper proposes a novel attention-based approach that integrates knowledge concept routes into knowledge tracing models, enhancing their ability to predict student performance.

Findings

Outperforms seven SOTA deep learning models on the XES3G5M dataset

Effectively incorporates domain knowledge of concept routes

Improves understanding of student learning outcomes

Abstract

Emerging Knowledge Tracing (KT) models, particularly deep learning and attention-based Knowledge Tracing, have shown great potential in realizing personalized learning analysis via prediction of students' future performance based on their past interactions. The existing methods mainly focus on immediate past interactions or individual concepts without accounting for dependencies between knowledge concept, referred as knowledge concept routes, that can be critical to advance the understanding the students' learning outcomes. To address this, in this paper, we propose an innovative attention-based method by effectively incorporating the domain knowledge of knowledge concept routes in the given curriculum. Additionally, we leverage XES3G5M dataset, a benchmark dataset with rich auxiliary information for knowledge concept routes, to evaluate and compare the performance of our proposed method to the seven State-of-the-art (SOTA) deep learning models.