research-article

Adversarial Bootstrapped Question Representation Learning for Knowledge Tracing

Authors:

Xiaoxuan ShenAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 8016 - 8025

https://doi.org/10.1145/3581783.3612044

Published: 27 October 2023 Publication History

Abstract

Knowledge tracing (KT), which estimates and traces the degree of learners' mastery of concepts based on students' responses to learning resources, has become an increasingly relevant problem in intelligent education. The accuracy of predictions greatly depends on the quality of question representations. While contrastive learning has been commonly used to generate high-quality representations, the selection of positive and negative samples for knowledge tracing remains a challenge. To address this issue, we propose an adversarial bootstrapped question representation (ABQR) model, which can generate robust and high-quality question representations without requiring negative samples. Specifically, ABQR introduces the bootstrap self-supervised learning framework, which learns question representations from different views of the skill-informed question interaction graph and facilitates question representations between each view to predict one another, thereby circumventing the need for negative sample selection. Moreover, we propose a multi-objective multi-round feature adversarial graph augmentation method to obtain a higher-quality target view, while preserving the structural information of the original graph. ABQR is versatile and can be easily integrated with any base KT model as a plug-in to enhance the quality of question representation. Extensive experiments demonstrate that ABQR significantly improves the performance of the base KT model and outperforms state-of-the-art models. Ablation experiments confirm the effectiveness of each module of ABQR. The code is available at https://github.com/lilstrawberry/ABQR.

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Cited By

Bai YLi XLiu ZHuang YGuo THou MXia FLuo W(2025)csKT: Addressing cold-start problem in knowledge tracing via kernel bias and cone attentionExpert Systems with Applications10.1016/j.eswa.2024.125988266(125988)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125988
Shen XYu FLiu YLiang RWan QYang KSun JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Revisiting Knowledge Tracing: A Simple and Powerful ModelProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681205(263-272)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681205
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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Index Terms

Adversarial Bootstrapped Question Representation Learning for Knowledge Tracing
1. Applied computing
  1. Education
    1. Distance learning

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
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M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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Hubei Provincial Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
National Key R&D Program of China
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Knowledge Innovation Program of Wuhan-Shuguang Project

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MM '23

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Bai YLi XLiu ZHuang YGuo THou MXia FLuo W(2025)csKT: Addressing cold-start problem in knowledge tracing via kernel bias and cone attentionExpert Systems with Applications10.1016/j.eswa.2024.125988266(125988)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125988
Shen XYu FLiu YLiang RWan QYang KSun JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Revisiting Knowledge Tracing: A Simple and Powerful ModelProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681205(263-272)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681205
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
Shen JQian HLiu SZhang WJiang BZhou ABaeza-Yates RBonchi F(2024)Capturing Homogeneous Influence among Students: Hypergraph Cognitive Diagnosis for Intelligent Education SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672002(2628-2639)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672002
Qian HLiu SLi MLi BLiu ZZhou ABaeza-Yates RBonchi F(2024)ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671988(2455-2466)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671988
Sun JYu FWan QLi QLiu SShen XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Interpretable Knowledge Tracing with Multiscale State RepresentationProceedings of the ACM Web Conference 202410.1145/3589334.3645373(3265-3276)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645373
Zhang HZhang XShang YLuo XZhang Y(2024)Customized adversarial training enhances the performance of knowledge tracing tasks2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00108(808-815)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00108
Wang ZLiu YZhang MLi NXu LWang XLi C(2024)Improving Question Embeddings for Knowledge Tracing with Residual Graph Convolutional Networks2024 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)10.1109/ICWAPR63074.2024.10870506(1-6)Online publication date: 20-Sep-2024
https://doi.org/10.1109/ICWAPR63074.2024.10870506

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