Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning
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...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10812715/ |
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| author | Zhaohui Liu Sainan Liu Weifeng Gu |
| author_facet | Zhaohui Liu Sainan Liu Weifeng Gu |
| author_sort | Zhaohui Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c80a978f3b7048e1a00b27bb5bff04f9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c80a978f3b7048e1a00b27bb5bff04f92025-01-21T00:01:10ZengIEEEIEEE Access2169-35362025-01-0113109331094310.1109/ACCESS.2024.352188310812715Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised LearningZhaohui Liu0Sainan Liu1https://orcid.org/0009-0002-4209-1476Weifeng Gu2School of Computer Science, School of Software, University of South China, Hengyang, Hunan, ChinaSchool of Computer Science, School of Software, University of South China, Hengyang, Hunan, ChinaSchool of Computer Science, School of Software, University of South China, Hengyang, Hunan, ChinaAs 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.https://ieeexplore.ieee.org/document/10812715/Intelligent systemsself-supervised learningattention mechanismsgraph convolutional networksknowledge tracing |
| spellingShingle | Zhaohui Liu Sainan Liu Weifeng Gu Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning IEEE Access Intelligent systems self-supervised learning attention mechanisms graph convolutional networks knowledge tracing |
| title | Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning |
| title_full | Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning |
| title_fullStr | Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning |
| title_full_unstemmed | Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning |
| title_short | Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning |
| title_sort | graph knowledge structure for attentional knowledge tracing with self supervised learning |
| topic | Intelligent systems self-supervised learning attention mechanisms graph convolutional networks knowledge tracing |
| url | https://ieeexplore.ieee.org/document/10812715/ |
| work_keys_str_mv | AT zhaohuiliu graphknowledgestructureforattentionalknowledgetracingwithselfsupervisedlearning AT sainanliu graphknowledgestructureforattentionalknowledgetracingwithselfsupervisedlearning AT weifenggu graphknowledgestructureforattentionalknowledgetracingwithselfsupervisedlearning |