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

This paper proposes Language model-based Knowledge Tracing (LKT), a novel framework that integrates pre-trained language models with knowledge tracing methods to predict student knowledge states and learning outcomes in online learning platforms. LKT leverages semantic information from question text to outperform traditional numerical-sequence-based knowledge tracing models and addresses the cold-start problem.

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

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