Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing
Authors: 
Jianwen Sun, Shangheng Du, Jianpeng Zhou, Xin Yuan, Xiaoxuan Shen, Ruxia LiangAuthors Info & Claims
Article No.: 17, Pages 1 - 28
Abstract
Knowledge Tracing (KT) aims to predict students’ future performance on answering questions based on their historical exercise sequences. To alleviate the problem of data sparsity in KT, recent works have introduced auxiliary information to mine question similarity, resulting in the enhancement of question embeddings. Nonetheless, there remains a gap in developing an approach that effectively incorporates various forms of auxiliary information, including relational information (e.g., question–student, question–skill relation), relationship attributes (e.g., correctness indicating a student's performance on a question), and node attributes (e.g., student ability). To tackle this challenge, the Similarity-enhanced Question Embedding (SimQE) method for KT is proposed, with its central feature being the utilization of weighted and attributed meta-paths for extracting question similarity. To capture multi-dimensional question similarity semantics by integrating multiple relations, various meta-paths are constructed for learning question embeddings separately. These embeddings, each encoding different similarity semantics, are then fused to serve the task of KT. To capture finer-grained similarity by leveraging the relationship attributes and node attributes on the meta-paths, the biased random walk algorithm is designed. In addition, the auxiliary node generation method is proposed to capture high-order question similarity. Finally, extensive experiments conducted on six datasets demonstrate that SimQE performs the best among 10 representative question embedding methods. Furthermore, SimQE proves to be more effective in alleviating the problem of data sparsity.
References
[1]
Ghodai Abdelrahman and Qing Wang. 2019. Knowledge tracing with sequential key-value memory networks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19), 175–184.
[2]
Ghodai Abdelrahman and Qing Wang. 2023. Deep graph memory networks for forgetting-robust knowledge tracing. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2023), 7844–7855.
[3]
Ghodai Abdelrahman, Qing Wang, and Bernardo Nunes. 2023. Knowledge tracing: A survey. ACM Computing Surveys 55, 11 (2023), 1–37.
[4]
R. C. Atkinson and J. A. Paulson. 1972. An approach to the psychology of instruction. Psychological Bulletin 78, 1 (1972), 49–61.
[5]
Yanhong Bai, Jiabao Zhao, Tingjiang Wei, Qing Cai, and Liang He. 2024. A survey of explainable knowledge tracing. Applied Intelligence 54, 8 (2024), 1–32.
[6]
Hao Cen, Kenneth Koedinger, and Brian Junker. 2006. Learning factors analysis–A general method for cognitive model evaluation and improvement. In International Conference on Intelligent Tutoring Systems (ITS ’06). Springer, 164–175.
[7]
Mingzhi Chen, Quanlong Guan, Yizhou He, Zhenyu He, Liangda Fang, and Weiqi Luo. 2022. Knowledge tracing model with learning and forgetting behavior. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22), 3863–3867.
[8]
Albert T. Corbett and John R. Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4 (1994), 253–278.
[9]
Chaoran Cui, Yumo Yao, Chunyun Zhang, Hebo Ma, Yuling Ma, Zhaochun Ren, Chen Zhang, and James Ko. 2024. DGEKT: A dual graph ensemble learning method for knowledge tracing. ACM Transactions on Information Systems 42, 3 (2024), 1–24.
[10]
Jiajun Cui, Zeyuan Chen, Aimin Zhou, Jianyong Wang, and Wei Zhang. 2023. Fine-grained interaction modeling with multi-relational transformer for knowledge tracing. ACM Transactions on Information Systems 41, 4 (2023), 1–26.
[11]
Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD ’17), 135–144.
[12]
Lingyue Fu, Hao Guan, Kounianhua Du, Jianghao Lin, Wei Xia, Weinan Zhang, Ruiming Tang, Yasheng Wang, and Yong Yu. 2024. SINKT: A structure-aware inductive knowledge tracing model with large language model. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24), 632–642.
[13]
Wenbin Gan, Yuan Sun, and Yi Sun. 2022. Knowledge structure enhanced graph representation learning model for attentive knowledge tracing. International Journal of Intelligent Systems 37, 3 (2022), 2012–2045.
[14]
Theophile Gervet, Ken Koedinger, Jeff Schneider, and Tom Mitchell. 2020. When is deep learning the best approach to knowledge tracing. Journal of Educational Data Mining 12 (2020), 31–54.
[15]
Aritra Ghosh, Neil Heffernan, and Andrew S. Lan. 2020. Context-aware attentive knowledge tracing. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD ’20), 2330–2339.
[16]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data mining (SIGKDD ’16), 855–864.
[17]
William J. Hawkins and Neil T. Heffernan. 2014. Using similarity to the previous problem to improve bayesian knowledge tracing. In Proceedings of the 7th International Conference on Educational Data Mining Workshops (EDMW ’14), 136–140.
[18]
Liangliang He. 2021. Integrating performance and side factors into embeddings for deep learning-based knowledge tracing. In Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME ’21). IEEE, 1–6.
[19]
Chenji Huang, Yixiang Fang, Xuemin Lin, Xin Cao, and Wenjie Zhang. 2022. ABLE: Meta-path prediction in heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data 16, 4 (2022), 1–21.
[20]
Junchen Jin, Mark Heimann, Di Jin, and Danai Koutra. 2021. Toward understanding and evaluating structural node embeddings. ACM Transactions on Knowledge Discovery from Data 16, 3 (2021), 1–32.
[21]
Heeseok Jung, Jaesang Yoo, Yohaan Yoon, and Yeonju Jang. 2024. CLST: Cold-start mitigation in knowledge tracing by aligning a generative language model as a students’ knowledge tracer. arXiv:2406.10296. Retrieved from https://doi.org/10.48550/arXiv.2406.10296
[22]
Tanja Käser, Severin Klingler, Alexander G. Schwing, and Markus Gross. 2017. Dynamic bayesian networks for student modeling. IEEE Transactions on Learning Technologies 10, 4 (2017), 450–462.
[23]
Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. Retrieved from https://doi.org/10.48550/arXiv.1609.02907
[24]
Hang Lee. 2021. The rise and challenges of postpandemic online education. IEEE Engineering Management Review 49, 4 (2021), 54–58.
[25]
Fei Liu, Chenyang Bu, Haotian Zhang, Le Wu, Kui Yu, and Xuegang Hu. 2024. FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning. ACM Transactions on Information Systems 42, 5 (2024), 1–26.
[26]
Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, and Guoping Hu. 2019. EKT: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering 33, 1 (2019), 100–115.
[27]
Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, and Yonghe Zheng. 2024. A survey of knowledge tracing. IEEE Transactions on Learning Technologies, 17 (2024), 1898–1919.
[28]
Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, and Shijin Wang. 2019. Exploiting cognitive structure for adaptive learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD ’19), 627–635.
[29]
Renming Liu, Matthew Hirn, and Arjun Krishnan. 2023. Accurately modeling biased random walks on weighted networks using node2vec+. Bioinformatics 39, 1 (2023), btad047.
[30]
Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, and Zongkai Yang. 2024. Automated discovery of symbolic laws governing skill acquisition from naturally occurring data. Nature Computational Science 4 (2024), 334–345.
[31]
Sannyuya Liu, Jianwei Yu, Qing Li, Ruxia Liang, Yunhan Zhang, Xiaoxuan Shen, and Jianwen Sun. 2022. Ability boosted knowledge tracing. Information Sciences 596 (2022), 567–587.
[32]
Yunfei Liu, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, and Yong Yu. 2020. Improving knowledge tracing via pre-training question embeddings. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI ’20), 1577–1583.
[33]
Shun Mao, Jieyu Zhan, Yizhao Wang, and Yuncheng Jiang. 2023. Improving knowledge tracing via considering two types of actual differences from exercises and prior knowledge. IEEE Transactions on Learning Technologies 16, 3 (2023), 324–338.
[34]
Sein Minn, Yi Yu, Michel C Desmarais, Feida Zhu, and Jill-Jenn Vie. 2018. Deep knowledge tracing and dynamic student classification for knowledge tracing. In Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM ’18). IEEE, 1182–1187.
[35]
Hiromi Nakagawa, Yusuke Iwasawa, and Yutaka Matsuo. 2018. End-to-end deep knowledge tracing by learning binary question-embedding. In Proceedings of the 2018 IEEE International Conference on Data Mining Workshops (ICDMW ’18), 332–342.
[36]
Hiromi Nakagawa, Yusuke Iwasawa, and Yutaka Matsuo. 2019. Graph-based knowledge tracing: Modeling student proficiency using graph neural network. In Proceedings of the 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI ’19). IEEE, 156–163.
[37]
Shalini Pandey and George Karypis. 2019. A self-attentive model for knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining (EDM ’19). IEDMS, 384–389.
[38]
Shalini Pandey and Jaideep Srivastava. 2020. RKT: Relation-aware self-attention for knowledge tracing. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM ’20), 1205–1214.
[39]
Radek Pelánek. 2019. Measuring similarity of educational items: An overview. IEEE Transactions on Learning Technologies 13, 2 (2019), 354–366.
[40]
Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in Neural Information Processing Systems (NIPS), Vol. 28, 505–513.
[41]
Liqing Qiu, Menglin Zhu, and Jingcheng Zhou. 2023. OPKT: Enhancing knowledge tracing with optimized pre-training mechanisms in intelligent tutoring. IEEE Transactions on Learning Technologies, 17 (2023), 841–855.
[42]
Shuanghong Shen, Qi Liu, Zhenya Huang, Yonghe Zheng, Minghao Yin, Minjuan Wang, and Enhong Chen. 2024. A survey of knowledge tracing: Models, variants, and applications. IEEE Transactions on Learning Technologies 17 (2024), 1898–1919.
[43]
Chuan Shi, Binbin Hu, Wayne Xin Zhao, and S. Yu Philip. 2018. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering 31, 2 (2018), 357–370.
[44]
Chuan Shi, Zhiqiang Zhang, Ping Luo, Philip S. Yu, Yading Yue, and Bin Wu. 2015. Semantic path based personalized recommendation on weighted heterogeneous information networks. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM ’15), 453–462.
[45]
Laura M. Smith, Linhong Zhu, Kristina Lerman, and Allon G. Percus. 2016. Partitioning networks with node attributes by compressing information flow. ACM Transactions on Knowledge Discovery from Data 11, 2 (2016), 1–26.
[46]
Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, and Richard G. Baraniuk. 2020. qDKT: Question-centric deep knowledge tracing. In Proceedings of the 13th International Conference on Educational Data Mining (EDM ’20). IEDMS, 677–681.
[47]
Yu Su, Qingwen Liu, Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Chris Ding, Si Wei, and Guoping Hu. 2018. Exercise-enhanced sequential modeling for student performance prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI ’18), 2435–2443.
[48]
Jianwen Sun, Shangheng Du, Zhi Liu, Fenghua Yu, Sannyuya Liu, Qing Li, and Xiaoxuan Shen. 2024. Weighted heterogeneous graph-based three-view contrastive learning for knowledge tracing in personalized e-learning systems. IEEE Transactions on Consumer Electronics 70, 1 (2024), 2838–2847.
[49]
Jianwen Sun, Fenghua Yu, Qian Wan, Qing Li, Sannyuya Liu, and Xiaoxuan Shen. 2024. Interpretable knowledge tracing with multiscale state representation. In Proceedings of the ACM on Web Conference 2024, 3265–3276.
[50]
Jianwen Sun, Jianpeng Zhou, Sannyuya Liu, Feijuan He, and Yun Tang. 2021. Hierarchical attention network based interpretable knowledge tracing. Journal of Computer Research and Development 58, 12 (2021), 2630–2644.
[51]
Jianwen Sun, Jianpeng Zhou, Kai Zhang, Qing Li, and Zijian Lu. 2021. Collaborative embedding for knowledge tracing. In International Conference on Knowledge Science, Engineering and Management (KSEM ’21). Springer, 333–342.
[52]
Yizhou Sun and Jiawei Han. 2013. Mining heterogeneous information networks: A structural analysis approach. ACM SIGKDD Explorations Newsletter 14, 2 (2013), 20–28.
[53]
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment 4, 11 (2011), 992–1003.
[54]
Hanshuang Tong, Yun Zhou, and Zhen Wang. 2020. Exercise hierarchical feature enhanced knowledge tracing. In Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED ’20). Springer, 324–328.
[55]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. arXiv:1710.10903. Retrieved from https://doi.org/10.48550/arXiv.1710.10903
[56]
Jill-Jênn Vie and Hisashi Kashima. 2019. Knowledge tracing machines: Factorization machines for knowledge tracing. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’19), Vol. 33, 750–757.
[57]
Mengdan Wang, Chao Peng, Rui Yang, Chenchao Wang, Yao Chen, and Xiaohua Yu. 2021. GASKT: A graph-based attentive knowledge-search model for knowledge tracing. In International Conference on Knowledge Science, Engineering and Management (KSEM ’21). Springer, 268–279.
[58]
Tianqi Wang, Fenglong Ma, and Jing Gao. 2019. Deep hierarchical knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining (EDM ’19), 671–674.
[59]
Xiao Wang, Yuanfu Lu, Chuan Shi, Ruijia Wang, Peng Cui, and Shuai Mou. 2020. Dynamic heterogeneous information network embedding with meta-path based proximity. IEEE Transactions on Knowledge and Data Engineering 34, 3 (2020), 1117–1132.
[60]
Zhiwei Wang, Xiaoqin Feng, Jiliang Tang, Gale Yan Huang, and Zitao Liu. 2019. Deep knowledge tracing with side information. In International Conference on Artificial Intelligence in Education (ICAIE ’19). Springer, 303–308.
[61]
Qize Xie, Liping Wang, Peidong Song, and Xuemin Lin. 2021. SQKT: A student attention-based and question-aware model for knowledge tracing. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM ’2.1). Springer, 221–236.
[62]
Xiaolu Xiong, Siyuan Zhao, Eric G. Van Inwegen, and Joseph E. Beck. 2016. Going deeper with deep knowledge tracing. In Proceedings of the 9th International Conference on Educational Data Mining (EDM ’16), 545–550.
[63]
Jia Xu, Jiajia Wei, Ge Yu, Xinyue Huang, and Pin Lyu. 2022. Review of question difficulty evaluation approaches. Journal of Frontiers of Computer Science and Technology 16, 4 (2022), 734–759.
[64]
Liangbei Xu and Mark A. Davenport. 2020. Dynamic knowledge embedding and tracing. In Proceedings of the 13th International Conference on Educational Data Mining (EDM ’20), 524–530.
[65]
Huali Yang, Shengze Hu, Jing Geng, Tao Huang, Junjie Hu, Hao Zhang, and Qiang Zhu. 2024. Heterogeneous graph-based knowledge tracing with spatiotemporal evolution. Expert Systems with Applications 238 (2024), 122249.
[66]
Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu, Weinan Zhang, and Yong Yu. 2020. GIKT: A graph-based interaction model for knowledge tracing. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD ’20). Springer, 299–315.
[67]
Chun-Kit Yeung and Dit-Yan Yeung. 2018. Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the 5th Annual ACM Conference on Learning at Scale (L@S ’18), 1–10.
[68]
Jianxing Yu, Qinliang Su, Xiaojun Quan, and Jian Yin. 2023. Multi-hop reasoning question generation and Its application. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2023), 725–740.
[69]
Jiani Zhang and Irwin King. 2016. Topological order discovery via deep knowledge tracing. In International Conference on Neural Information Processing (ICONIP ’16), Vol. 9950, 112–119.
[70]
Jiani Zhang, Xingjian Shi, Irwin King, and Dit-Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17), 765–774.
[71]
Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, and Geng Sun. 2020. Attention-based knowledge tracing with heterogeneous information network embedding. In Proceedings of the 13th International Conference on Knowledge Science, Engineering and Management (KSEM ’20). Springer, 95–103.
[72]
Yiding Zhang, Xiao Wang, Nian Liu, and Chuan Shi. 2021. Embedding heterogeneous information network in hyperbolic spaces. ACM Transactions on Knowledge Discovery from Data 16, 2 (2021), 1–16.
[73]
Susu Zheng, Donghai Guan, and Weiwei Yuan. 2022. Semantic-aware heterogeneous information network embedding with incompatible meta-paths. World Wide Web 25 (2022), 1–21.
Index Terms
- Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing
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Publication History
Published: 10 December 2024
Online AM: 04 November 2024
Accepted: 22 October 2024
Revised: 14 September 2024
Received: 08 October 2023
Published in TKDD Volume 19, Issue 1
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- National Science and Technology
- National Natural Science Foundation of China
- Higher Education Science Research Program of China Association of Higher Education
- China Postdoctoral Science Foundation
- Hubei Provincial Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
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