融合遗忘因素与记忆门的图神经网络知识追踪模型

Knowledge tracing model based on graph neural network blending with forgetting factors and memory gate

知识追踪任务根据学生历史学习数据实时诊断学生的认知状态,并预测他未来的答题表现。为准确建模知识追踪中的遗忘行为和答题序列的时序特征,提出一种融合遗忘因素与记忆门的图神经网络知识追踪(GKT-FM)模型。首先,GKT-FM模型通过历史答题记录计算知识点相关性,构建知识图;其次,采用图神经网络(GNN)建模学生的认知状态,综合考虑7个影响遗忘行为的特征;然后,以记忆门结构建模学生答题序列中的时序特征,重构基于GNN的知识追踪更新过程;最后,融合遗忘因素和时序特征得到预测结果。在公开数据集ASSISTments2009和KDDCup2010上的实验结果表明,相较于GKT(Graph-based Knowledge Tracing)模型,GKT-FM模型的平均曲线下面积(AUC)分别提升了6.9%和9.5%,平均精度(ACC)分别提升了5.3%和6.7%,可见,GKT-FM模型能更好地建模学生的遗忘行为、追踪学生的认知状态。

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 timeseries 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.