Prediction Using KMAKT Integrated with Course Knowledge Graph

doi:10.19678/j.issn.1000-3428.0069345

Abstract

Abstract:

Most existing deep knowledge tracking models have weak interpretability of knowledge tracking results and overlook the impact of the inherent correlation between exercises and knowledge points on the effectiveness of knowledge tracking and prediction results. To address these issues, this study proposes a course Knowledge graph and Multi-head Attention mechanism-based Knowledge Tracing (KMAKT) model to predict student performance. First, Word2Vec and Bidirectional Long Short-Term Memory (BiLSTM) networks are used to convert exercise answering sequence data into dense low dimensional vectors. The graph embedding model TransR is used to embed the course knowledge graph representation, and a multi-head attention mechanism is used to calculate the contribution of past exercise answering sequences to the current knowledge state. Subsequently, the influence of the precursor knowledge on the prediction results is explored using attention networks. Finally, prediction results are obtained using multi-layer neural networks, and the interpretability and prediction accuracy of the model are improved. The experimental results show that on the ASSISTments2017 dataset, the Area Under receiver operating Characteristic (AUC), accuracy, and F1 value of the KMAKT model are improved by approximately 5.20, 4.20, and 2.40 percentage points, respectively, as compared to conventional Deep Knowledge Tracking (DKT). Therefore, The KMAKT model has good prediction performance. The visualization results of knowledge tracking on the Hunan University Signal and System (HNU_SYS) sub-dataset verify that the KMAKT model conform to the cognitive laws of education and have a certain degree of interpretability.

Key words: performance prediction, course knowledge graph, attention mechanism, knowledge tracing, Long Short-Term Memory (LSTM) network, semantic feature

摘要：

现有多数深度知识追踪模型的知识追踪结果的可解释性弱, 且忽视了习题与知识点的内在关联性对知识追踪效果与预测结果的影响。针对上述问题, 提出一种用于学生表现预测的结合课程知识图谱与多头注意力机制的知识追踪(KMAKT)模型。首先, 采用Word2Vec和双向长短期记忆(BiLSTM)网络将习题作答序列数据转换为低维稠密向量, 利用图嵌入模型TransR进行课程知识图谱嵌入表示, 并使用多头注意力机制计算过往习题作答序列对当前知识状态的贡献程度; 然后, 通过注意力网络挖掘前驱知识对预测结果的影响程度; 最后, 通过多层神经网络获取预测结果, 提高模型的可解释性与预测精度。实验结果表明, KMAKT模型在ASSISTments2017数据集上的受试者工作特征曲线下的面积(AUC)、准确率和F1值相比于深度知识追踪(DKT)模型分别提升了约5.20、4.20和2.40个百分点, 具有较好的预测性能。在湖南大学信号与系统(HNU_SYS)子数据集上的知识追踪可视化结果验证了KMAKT模型的知识追踪结果符合教育学认知规律且具备一定程度的可解释性。

关键词: 表现预测, 课程知识图谱, 注意力机制, 知识追踪, 长短期记忆网络, 语义特征

Lianhong WANG, Feipeng LIN, Xiaoyao LI, Guizhi CHEN, Li ZHOU. Prediction Using KMAKT Integrated with Course Knowledge Graph[J]. Computer Engineering, 2024, 50(7): 23-31.

王炼红, 林飞鹏, 李潇瑶, 谌桂枝, 周莉. 融入课程知识图谱的KMAKT预测[J]. 计算机工程, 2024, 50(7): 23-31.

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Figures/Tables 11

Fig.1 Structure of LSTM network unit

Fig.2 Structure of course knowledge graph

Fig.3 Overall framework of KMAKT model

Fig.4 Structure of BiLSTM network

Fig.5 Structure of multi-head attention mechanism

Fig.6 Results of comparative experiments

Fig.7 Visualization display of heat maps for knowledge tracking results

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