计算机工程2024，Vol.50Issue(7)：23-31,9.DOI:10.19678/j.issn.1000-3428.0069345
融入课程知识图谱的KMAKT预测
Prediction Using KMAKT Integrated with Course Knowledge Graph
摘要
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.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Key words
performance prediction/course knowledge graph/attention mechanism/knowledge tracing/Long Short-Term Memory(LSTM)network/semantic feature分类
信息技术与安全科学引用本文复制引用
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):23-31,9.基金项目
国家自然科学基金(62377010) （62377010）
湖南省教育厅科学研究重点项目(22A0021). （22A0021）
