知识追踪任务旨在通过对学生历史学习数据实时准确地追踪学生知识状态,并预测学生未来的答题表现.针对当前研究忽略了题目涵盖知识点中复杂的高阶关系的问题,提出一种融合知识点关系的深度记忆网络知识追踪模型(deep memory network knowledge tracing model incorporating knowledge point relationships,HRGKT).首先,HRGKT使用知识点关系图定义图中节点之间的关系信息,表示知识点之间的丰富信息.使用GAT获取两者之间的高阶关系.然后,学习过程中存在着遗忘,HRGKT综合考虑 4 个影响知识遗忘的因素来更准确地追踪学生知识状态.最后,根据真实在线教育数据集上的实验比较结果,与当前知识追踪模型相比,HRGKT在追踪学生知识掌握状态方面表现更加准确,并且具备更好的预测性能.
Deep Memory Network Incorporating Knowledge Point Relationship for Knowledge Tracing
The knowledge tracing task aims to accurately track students'knowledge status in real time and predict students'future performance by analyzing their historical learning data.This study proposes a deep memory network knowledge tracing model incorporating knowledge point-relationships(HRGKT)to address the problem that current research has neglected complex higher-order relationships in the knowledge points covered by the questions.Firstly,HRGKT uses the knowledge point relationship graph to define the relationship information between nodes in the graph,which represents the rich information between knowledge points.GAT is used to obtain higher-order relationships between them.Then,forgetting exists in the learning process,and HRGKT considers four factors affecting knowledge forgetting to track students'knowledge status more accurately.Finally,based on the experimental comparison results on real online education datasets,HRGKT performs more accurately in tracing students'knowledge mastery status and has better prediction performance than current knowledge tracing models.
intelligent educationknowledge tracinggraph attention network(GAT)knowledge point relationship
NETL
NSTL
万方数据
