华东师范大学学报（自然科学版）Issue(5)：20-31,12.DOI:10.3969/j.issn.1000-5641.2024.05.003
SA-MGKT:基于自注意力融合的多图知识追踪方法
SA-MGKT:Multi-graph knowledge tracing method based on self-attention
摘要
Abstract
This study proposes a multi-graph knowledge tracing method integrated with a self-attention mechanism(SA-MGKT),The aim is to model students'knowledge mastery based on their historical performance on problem-solving exercises and evaluate their future learning performance.Firstly,a heterogeneous graph of student-exercise is constructed to represent the high-order relationships between these two factors.Graph contrastive learning techniques are employed to capture students'answer preferences,and a three-layer LightGCN is utilized for graph representation learning.Secondly,we introduce information from concept association hypergraphs and directed transition graphs,and obtain node embeddings through hypergraph convolutional networks and directed graph convolutional networks.Finally,by incorporating the self-attention mechanism,we successfully fuse the internal information within the exercise sequence and the latent knowledge embedded in the representations learned from multiple graphs,leading to a substantial enhancement in the accuracy of the knowledge tracing model.Experimental outcomes on three benchmark datasets demonstrate promising results,showcasing remarkable improvements of 3.51％,17.91％,and 1.47％ respectively in the evaluation metrics,compared to the baseline models.These findings robustly validate the effectiveness of integrating multi-graph information and the self-attention mechanism in enhancing the performance of knowledge tracing models.关键词
知识追踪/图对比学习/自注意力机制Key words
knowledge tracing/graph contrastive learning/self-attention分类
信息技术与安全科学引用本文复制引用
王畅,马丹,许华容,陈攀峰,陈梅,李晖..SA-MGKT:基于自注意力融合的多图知识追踪方法[J].华东师范大学学报（自然科学版）,2024,(5):20-31,12.基金项目
国家自然科学基金(61462010) （61462010）
贵州省科技计划项目(黔科合重大专项[2024]003,黔科合成果[2023]一般010) （黔科合重大专项[2024]003,黔科合成果[2023]一般010）
