LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction

• URL: http://arxiv.org/abs/2502.02945v1
• Date: Wed, 05 Feb 2025 07:21:49 GMT
• Title: LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction
• Authors: Ziwei Wang, Jie Zhou, Qin Chen, Min Zhang, Bo Jiang, Aimin Zhou, Qinchun Bai, Liang He,
• Abstract summary: knowledge tracing problem aims to predict whether students can correctly answer the next question based on their past question-answer records. We propose a large language models (LLMs)-based framework for KT, named texttttextbfLLM-KT. For task-level alignment, we design Plug-and-Play instruction to align LLMs with KT, leveraging LLMs' rich knowledge and powerful reasoning capacity. For modality-level alignment, we design the plug-in context and sequence to integrate multiple modalities learned by traditional methods.
• Score: 39.59752235090272
• License:
• Abstract: The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students' sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named \texttt{\textbf{LLM-KT}}, to integrate the strengths of LLMs and traditional sequence interaction models. For task-level alignment, we design Plug-and-Play instruction to align LLMs with KT, leveraging LLMs' rich knowledge and powerful reasoning capacity. For modality-level alignment, we design the plug-in context and sequence to integrate multiple modalities learned by traditional methods. To capture the long context of history records, we present a plug-in context to flexibly insert the compressed context embedding into LLMs using question-specific and concept-specific tokens. Furthermore, we introduce a plug-in sequence to enhance LLMs with sequence interaction behavior representation learned by traditional sequence models using a sequence adapter. Extensive experiments show that \texttt{\textbf{LLM-KT}} obtains state-of-the-art performance on four typical datasets by comparing it with approximately 20 strong baselines.

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