Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning

被引:0
作者
Liu, Zhaohui [1 ]
Liu, Sainan [1 ]
Gu, Weifeng [1 ]
机构
[1] Univ South China, Sch Comp Sci, Sch Software, Hengyang 421001, Hunan, Peoples R China
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Knowledge engineering; Semantics; Data models; Predictive models; Attention mechanisms; Self-supervised learning; Analytical models; Deep learning; Computer science; Computational modeling; Intelligent systems; self-supervised learning; attention mechanisms; graph convolutional networks; knowledge tracing;
D O I
10.1109/ACCESS.2024.3521883
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As intelligent education advances and online learning becomes more prevalent, Knowledge Tracing (KT) has become increasingly important. KT assesses students' learning progress by analysing their historical performance in related exercises. Despite significant advances in the field, there are still shortcomings in two aspects: first, a lack of effective integration between exercises and knowledge points; second, an overemphasis on nodal information, neglecting deep semantic relationships. To address these, we propose a self-supervised learning approach that uses an enhanced heterogeneous graph attention network to represent and analyse complex relationships between exercises and knowledge points. We introduce an innovative surrogate view generation method to optimise the integration of local structural information and global semantics within the graph, addressing relational inductive bias. In addition, we incorporate the improved representation algorithm into the loss function to handle data sparsity, thereby improving prediction accuracy. Experiments on three real-world datasets show that our model outperforms baseline models.
引用
收藏
页码:10933 / 10943
页数:11
相关论文
共 32 条
  • [1] [Anonymous], 1964, Plastics
  • [2] Atkinson RichardC., 1969, COMPUTER ASSISTED IN
  • [3] AI IN CAI - AN ARTIFICIAL-INTELLIGENCE APPROACH TO COMPUTER-ASSISTED INSTRUCTION
    CARBONEL.JR
    [J]. IEEE TRANSACTIONS ON MAN-MACHINE SYSTEMS, 1970, MM11 (04): : 190 - &
  • [4] Chen JH, 2023, AAAI CONF ARTIF INTE, P14196
  • [5] CORBETT AT, 1994, USER MODEL USER-ADAP, V4, P253, DOI 10.1007/BF01099821
  • [6] NEAREST NEIGHBOR PATTERN CLASSIFICATION
    COVER, TM
    HART, PE
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 1967, 13 (01) : 21 - +
  • [7] Knowledge structure enhanced graph representation learning model for attentive knowledge tracing
    Gan, Wenbin
    Sun, Yuan
    Sun, Yi
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (03) : 2012 - 2045
  • [8] Context-Aware Attentive Knowledge Tracing
    Ghosh, Aritra
    Heffernan, Neil
    Lan, Andrew S.
    [J]. KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2020, : 2330 - 2339
  • [9] Grill J.-B., 2020, ADV NEURAL INFORM PR, V33, P21271
  • [10] Lee N, 2022, AAAI CONF ARTIF INTE, P7372