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research-article

Heterogeneous graph-based knowledge tracing with spatiotemporal evolution

Authors:

Qiang ZhuAuthors Info & Claims

Volume 238, Issue PF

https://doi.org/10.1016/j.eswa.2023.122249

Published: 27 February 2024 Publication History

Abstract

Knowledge tracing (KT), in which the future performance of students is estimated by tracing their knowledge states based on their responses to exercises, is widely applied in the field of intelligent education. However, existing mainstream KT models explore the importance of knowledge relations but ignore the key role of cognitive factors. According to the knowledge construction theory, the human cognitive system performs both spatial accommodation and temporal assimilation to internalize knowledge. In this paper, we propose an innovative heterogeneous graph-based Knowledge tracing method with spatiotemporal evolution (TSKT), in which knowledge state evolution is traced along both temporal and spatial dimensions. We construct a heterogeneous graph with multiple exercise attributes, including content, concepts, and difficulty, to obtain a knowledge space with richer exercise representations through hierarchical aggregation. We design a spatial updating module in which each interaction updates the current node’s state of the knowledge space and transfers its influence to its neighbors. We also design a temporal updating module to further update the knowledge state through short-term memory enhancing and long-term memory forgetting. Finally, we stack these modules to obtain deeper features by using alternate spatiotemporal updating. Extensive experiments on three datasets reveal the superiority of the proposed method and its variants in terms of future performance prediction.

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Cited By

Hu PLi GLv YWan MZhang P(2024)DHKFNInternational Journal of Knowledge Management10.4018/IJKM.35801120:1(1-16)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.4018/IJKM.358011
Sun JDu SZhou JYuan XShen XLiang R(2024)Question Embedding on Weighted Heterogeneous Information Network for Knowledge TracingACM Transactions on Knowledge Discovery from Data10.1145/370315819:1(1-28)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3703158
Huang TOu XYang HHu SGeng JHu JXu ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Remembering is Not Applying: Interpretable Knowledge Tracing for Problem-solving ProcessesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681049(3151-3159)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681049
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Heterogeneous graph-based knowledge tracing with spatiotemporal evolution
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 238, Issue PF

Mar 2024

1588 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 27 February 2024

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Cited By

Hu PLi GLv YWan MZhang P(2024)DHKFNInternational Journal of Knowledge Management10.4018/IJKM.35801120:1(1-16)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.4018/IJKM.358011
Sun JDu SZhou JYuan XShen XLiang R(2024)Question Embedding on Weighted Heterogeneous Information Network for Knowledge TracingACM Transactions on Knowledge Discovery from Data10.1145/370315819:1(1-28)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3703158
Huang TOu XYang HHu SGeng JHu JXu ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Remembering is Not Applying: Interpretable Knowledge Tracing for Problem-solving ProcessesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681049(3151-3159)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681049
Mao HMao MMao F(2024)Ranking on user–item heterogeneous graph for Ecommerce next basket recommendationsKnowledge-Based Systems10.1016/j.knosys.2024.111863296:COnline publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111863

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