关键词: 知识追踪, 图神经网络, 教育数据挖掘, 智慧教育, 遗忘因素

Abstract:

The knowledge tracing task diagnoses a student’s cognitive state in real time based on historical learning data， and predicts the future performance of the student in answering questions. In order to accurately model the forgetting behaviors and the time-series characteristics of the answering sequence in knowledge tracing， a Graph neural network-based Knowledge Tracing blending with Forgetting factors and Memory gate （GKT-FM） model was proposed. Firstly， through the answering record， the correlations of knowledge points were calculated and a knowledge graph was constructed. Then， Graph Neural Network （GNN） was used to model the cognitive state of the student， and seven characteristics that affect forgetting behaviors were considered comprehensively. After that， the memory gate structure was used to model the time-series characteristics in the student’s answering sequence， and the update process of GNN-based knowledge tracing was reconstructed. Finally， the prediction results were obtained by integrating the forgetting factors and the time-series characteristics. Experimental results on public datasets ASSISTments2009 and KDDCup2010 show that compared with GKT （Graph-based Knowledge Tracing） model， GKT-FM model improves the average AUC （Area Under Curve） by 6.9% and 9.5% respectively， and the average ACC （ACCuarcy） by 5.3% and 6.7% respectively， indicating that GKT-FM model can better model students’ forgetting behaviors and trace their cognitive states.

Key words: knowledge tracing, Graph Neural Network (GNN), educational data mining, smart education, forgetting factor