Learning Assessment and Recommendation

SA-MGKT: Multi-graph knowledge tracing method based on self-attention

  • Chang WANG ,
  • Dan MA ,
  • Huarong XU ,
  • Panfeng CHEN ,
  • Mei CHEN ,
  • Hui LI
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  • 1. State Key Laboratory of Public Big Data, Guiyang 550025, China
    2. School of Computer Science and Technology, Guizhou University, Guiyang 550025, China

Received date: 2024-07-12

  Online published: 2024-09-23

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.

Cite this article

Chang WANG , Dan MA , Huarong XU , Panfeng CHEN , Mei CHEN , Hui LI . SA-MGKT: Multi-graph knowledge tracing method based on self-attention[J]. Journal of East China Normal University(Natural Science), 2024 , 2024(5) : 20 -31 . DOI: 10.3969/j.issn.1000-5641.2024.05.003

References

1 SHEN S, LIU Q, HUANG Z, et al. A survey of knowledge tracing: Models, variants, and applications [EB/OL]. (2024-04-11) [2024-06-19]. https://arxiv.org/pdf/2105.15106.
2 DWIVEDI P, KANT V, BHARADWAJ K K.. Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 2018, 23 (2): 819- 836.
3 DO P, NGUYEN K, VU T N, et al. Integrating knowledge-based reasoning algorithms and collaborative filtering into e-learning material recommendation system [C]// Future Data and Security Engineering: 4th International Conference. 2017: 419-432.
4 WANG S, WU H, KIM J H, et al. Adaptive learning material recommendation in online language education [C]// Artificial Intelligence in Education. 2019: 298-302.
5 ZHAO C, ZHAO H, HE M, et al. Cross-domain recommendation via user interest alignment [C]// Proceedings of the ACM Web Conference 2023. 2023: 887-896.
6 XU B, HUANG Z, LIU J, et al. Learning behavior-oriented knowledge tracing [C]// Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023: 2789-2800.
7 曾凡智, 许露倩, 周燕, 等.. 面向智慧教育的知识追踪模型研究综述. 计算机科学与探索, 2022, 16 (8): 1742- 1763.
8 ABDELRAHMAN G, WANG Q, NUNES B.. Knowledge tracing: A survey. ACM Computing Surveys, 2023, 55 (11): 224.
9 KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL]. (2016-09-29) [2024-06-18]. https://doi.org/10.48550/arXiv.1609.02907.
10 NAKAGAWA H, IWASAWA Y, MATSUO Y. Graph-based knowledge tracing: Modeling student proficiency using graph neural network [C]// IEEE/WIC/ACM International Conference on Web Intelligence. 2019: 156-163.
11 YANG Y, SHEN J, QU Y, et al. GIKT: A graph-based interaction model for knowledge tracing [C]// Machine Learning and Knowledge Discovery in Databases. 2021: 299-315.
12 LUO R, LIU F, LIANG W, et al. DAGKT: Difficulty and attempts boosted graph-based knowledge tracing [C]// International Conference on Neural Information Processing. 2022: 255-266.
13 ZHANG H, BU C, LIU F, et al. APGKT: Exploiting associative path on skills graph for knowledge tracing [C]// Pacific Rim International Conference on Artificial Intelligence. 2022: 353-365.
14 CUI C, YAO Y, ZHANG C, et al.. DGEKT: A dual graph ensemble learning method for knowledge tracing. ACM Transactions on Information Systems, 2024, 42 (3): 78.
15 CORBETT A T, ANDERSON J R.. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-adapted Interaction, 1994, (4): 253- 278.
16 K?SER T, KLINGLER S, SCHWING A G, et al.. Dynamic bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 2017, 10 (4): 450- 462.
17 CEN H, KOEDINGER K, JUNKER B. Learning factors analysis—A general method for cognitive model evaluation and improvement [C]// International Conference on Intelligent Tutoring Systems. 2006: 164-175.
18 PAVLIK P I, CEN H, KOEDINGER K R. Performance factors analysis—A new alternative to knowledge tracing [C]// Artificial Intelligence in Education. 2009: 531-538.
19 VIE J J, KASHIMA H. Knowledge tracing machines: Factorization machines for knowledge tracing [C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2019: 750-757.
20 PIECH C, BASSEN J, HUANG J, et al. Deep knowledge tracing [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015: 505-513.
21 KHAJAH M, LINDSEY R V, MOZER M C. How deep is knowledge tracing? [EB/OL]. (2016-04-08) [2024-06-18]. https://doi.org/10.48550/arXiv.1604.02416.
22 LIU Y, YANG Y, CHEN X, et al. Improving knowledge tracing via pre-training question embeddings [EB/OL]. (2020-12-09) [2024-06-18]. https://doi.org/10.48550/arXiv.2012.05031.
23 ZHANG J, SHI X, KING I, et al. Dynamic key-value memory networks for knowledge tracing [C]// Proceedings of the 26th International Conference on World Wide Web. 2017: 765-774.
24 PANDEY S, KARYPIS G. A self-attentive model for knowledge tracing [EB/OL]. (2019-07-16) [2024-06-18]. https://doi.org/10.48550/arXiv.1907.06837.
25 GHOSH A, HEFFERNAN N, LAN A S. Context-aware attentive knowledge tracing [C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020: 2330-2339.
26 SONG X, LI J, CAI T, et al.. A survey on deep learning based knowledge tracing. Knowledge-Based Systems, 2022, 258, 110036.
27 YE Y, SHAN Z. HGKT: Hypergraph-based knowledge tracing for learner performance prediction [C]// 2023 International Joint Conference on Neural Networks. 2023: 1-9.
28 TONG S, LIU Q, HUANG W, et al. Structure-based knowledge tracing: An influence propagation view [C]// 2020 IEEE International Conference on Data Mining. 2020: 541-550.
29 SONG X, LI J, TANG Y, et al.. JKT: A joint graph convolutional network based deep knowledge tracing. Information Sciences, 2021, 580, 510- 523.
30 HE X, DENG K, WANG X, et al. LightGCN: Simplifying and powering graph convolution network for recommendation [C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020: 639-648.
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