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<p>Knowledge Tracing (KT) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students’ responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a <Emphasis Type="BoldItalic">D</Emphasis><i>ifficulty aware</i> <Emphasis Type="BoldItalic">P</Emphasis><i>rogramming</i> <Emphasis Type="BoldItalic">K</Emphasis><i>nowledge</i> <Emphasis Type="BoldItalic">T</Emphasis><i>racing via Large Language Models</i>(DPKT) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students’ s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning.</p>

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Difficulty aware programming knowledge tracing via large language models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月3日

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  • Lina Yang,
  • Xinjie Sun,
  • Hui Li,
  • Ran Xu,
  • Xuqin Wei

摘要

Knowledge Tracing (KT) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students’ responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a Difficulty aware Programming Knowledge Tracing via Large Language Models(DPKT) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students’ s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning.