摘要
知识追踪模型主要使用监督学习范式建模给定题目信息条件下的作答结果概率分布,无法根据新的题目信息即时调整模型,最终影响了预测效果。针对这一问题,融合强化学习范式,提出知识点表征强化的知识追踪模型,主要包括基础网络、价值网络和策略网络三个部分。基础网络建模题目与知识点表征,价值网络计算题目价值及时间差分误差,策略网络优化预测结果。通过五个基线模型在三个数据集上的实验表明,该模型在AUC和ACC上表现优异,特别是在ASSISTments2009数据集上,AUC提升6.83%~14.34%,ACC提升11.39%~19.74%。进一步地,该模型表示质量较基线模型提高2.59%,还通过消融实验验证了强化学习框架的有效性。最后,将所提模型应用于三门真实课程的学习行为数据以预测学习者的表现,与基线模型的对比结果说明了所提模型在实际场景中的可用性。
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 affecting 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 network,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 baseline 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.
作者
张凯
张慧玲
王泽琛
王雪
方洋洋
Zhang Kai;Zhang Huiling;Wang Zechen;Wang Xue;Fang Yangyang(School of Computer Science,Yangtze University,Jingzhou Hubei 434000,China)
出处
《计算机应用研究》
北大核心
2025年第1期86-92,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(62077018)
湖北省自然科学基金资助项目(2022CFB132)
湖北本科高校省级教学改革研究项目(2023273)
长江大学2023年研究生教育教学改革研究立项项目(YJY202341)。
关键词
知识追踪
知识点
图神经网络
强化学习
knowledge tracing
knowledge point
graph neural network
reinforcement learning
