Attentive Q-Matrix Learning for Knowledge Tracing

TL;DR

This paper introduces QAKT, an end-to-end knowledge tracing model that learns the q-matrix directly from student interaction data, eliminating the need for predefined skill tags and maintaining high performance.

Contribution

The paper presents a novel attentive model that automatically learns the q-matrix from data, improving applicability to large-scale online education platforms without sacrificing accuracy.

Findings

QAKT achieves comparable or better performance than state-of-the-art KT models.

The learned q-matrix is highly model-agnostic and more informative than human-labeled tags.

QAKT demonstrates strong interpretability and effectiveness across multiple datasets.

Abstract

As the rapid development of Intelligent Tutoring Systems (ITS) in the past decade, tracing the students' knowledge state has become more and more important in order to provide individualized learning guidance. This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Plenty of KT models have been proposed and have shown remarkable performance recently. However, the majority of these models use concepts to index questions, which means the predefined skill tags for each question are required in advance to indicate the KCs needed to answer that question correctly. This makes it pretty hard to apply on large-scale online education platforms where questions are often not well-organized by skill tags. In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an end-to-end style model that is able to apply the attentive method to scenes where no predefined skill tags are available without sacrificing its performance. With a novel hybrid embedding method based on the q-matrix and Rasch model, QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Meanwhile, the architecture of QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically validated that our model shows similar or even better performance than state-of-the-art KT methods. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.