Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task

TL;DR

This paper introduces Language Model-based Knowledge Tracing (LKT), a framework that combines pre-trained language models with knowledge tracing to improve performance, interpretability, and address cold-start issues in online learning systems.

Contribution

It presents a novel integration of pre-trained language models with KT, leveraging semantic textual information to enhance accuracy and interpretability over existing models.

Findings

LKT significantly outperforms previous KT models on benchmark datasets.

LKT effectively addresses the cold-start problem in knowledge tracing.

Enhanced interpretability through attention analysis and explanation techniques.

Abstract

Knowledge Tracing (KT) is a critical task in online learning for modeling student knowledge over time. Despite the success of deep learning-based KT models, which rely on sequences of numbers as data, most existing approaches fail to leverage the rich semantic information in the text of questions and concepts. This paper proposes Language model-based Knowledge Tracing (LKT), a novel framework that integrates pre-trained language models (PLMs) with KT methods. By leveraging the power of language models to capture semantic representations, LKT effectively incorporates textual information and significantly outperforms previous KT models on large benchmark datasets. Moreover, we demonstrate that LKT can effectively address the cold-start problem in KT by leveraging the semantic knowledge captured by PLMs. Interpretability of LKT is enhanced compared to traditional KT models due to its use of text-rich data. We conducted the local interpretable model-agnostic explanation technique and analysis of attention scores to interpret the model performance further. Our work highlights the potential of integrating PLMs with KT and paves the way for future research in KT domain.