计算机应用研究2024，Vol.41Issue(11)：3265-3271,7.DOI:10.19734/j.issn.1001-3695.2024.04.0093
练习嵌入和学习遗忘特征增强的知识追踪模型
Exercise embeddings and learning-forgetting features boosted knowledge tracing
摘要
Abstract
Most existing KT models evaluate students'future performance centered on concepts,overlooking the differences between exercises containing the same concepts,thus affecting the models'prediction accuracy.Moreover,in constructing the students'knowledge state,existing models fail to fully utilize the learning-forgetting features of students during the answering process,leading to an inaccurate modeling of students'knowledge states.To address these issues,this paper proposed an exercise embeddings and learning-forgetting features boosted knowledge tracing model.The model utilized the explicit relation-ships in the exercise-concept bipartite graph to calculate the implicit relationships within the graph,constructing an exercise-concept relationship heterogeneous graph.To make full use of the rich relationship information in the heterogeneous graph,ELFBKT introduced a relational graph convolutional network(RGCN).Through the processing of RGCN,the model enhanced the quality of exercise embeddings and predicted students'future performance more accurately with an exercise-centric ap-proach.Furthermore,ELFBKT fully utilized various learning-forgetting features to construct two gating-controlled mechanisms,modeling the students'learning and forgetting behaviors respectively,to more accurately model the students'knowledge states.Experiments on two real-world datasets show that ELFBKT outperforms other models in KT tasks.关键词
知识追踪/练习嵌入/学习和遗忘/关系图卷积网络Key words
knowledge tracing(KT)/exercise embedding/learning and forgetting/relational graph convolutional network分类
信息技术与安全科学引用本文复制引用
张维,李志新,龚中伟,罗佩华,宋玲玲..练习嵌入和学习遗忘特征增强的知识追踪模型[J].计算机应用研究,2024,41(11):3265-3271,7.基金项目
国家自然科学基金资助项目(62377024) （62377024）
