计算机应用研究2025，Vol.42Issue(1)：86-92,7.DOI:10.19734/j.issn.1001-3695.2024.06.0196
知识点表征强化的知识追踪模型
Knowledge tracing via reinforcement of concept representation
摘要
Abstract
Knowledge tracing models mainly use supervised learning paradigm to model the probability distribution of answers given the question information,which cannot adjust the model immediately based on new question information,ultimately af-fecting the prediction performance.To address this issue,this paper proposed a knowledge tracing model with enhanced knowledge representation by integrating reinforcement learning paradigm,which mainly consisted of three parts:a basic net-work,a value network,and a policy network.The basic network modeled the representation of questions and knowledge points,the value network calculated the value of questions and the temporal difference error,and the policy network optimized the prediction results.Experiments conducted with five baseline models on three datasets demonstrate that the proposed model excels in terms of AUC and ACC,especially on the ASSISTments2009 dataset,where AUC is improved by 6.83％～14.34％and ACC by 11.39％～19.74％.Furthermore,the quality of model representation is improved by 2.59％compared to base-line mo-dels,and ablation experiments confirm the effectiveness of the reinforcement learning framework.Finally,applying the proposed model to learning behavior data from three real courses shows its practical usability,as evidenced by its performance compared to baseline models.关键词
知识追踪/知识点/图神经网络/强化学习Key words
knowledge tracing/knowledge point/graph neural network/reinforcement learning分类
信息技术与安全科学引用本文复制引用
张凯,张慧玲,王泽琛,王雪,方洋洋..知识点表征强化的知识追踪模型[J].计算机应用研究,2025,42(1):86-92,7.基金项目
国家自然科学基金资助项目(62077018) （62077018）
湖北省自然科学基金资助项目 (2022CFB132) （2022CFB132）
湖北本科高校省级教学改革研究项目(2023273) （2023273）
长江大学2023年研究生教育教学改革研究立项项目(YJY202341) （YJY202341）
