Knowledge-Tracing-Paper-Lists

Paper Collection for Knowledge Tracing with brief introductions. This collection refers to Awesome-Knowledge-Tracing and also contains self-collected papers.
To be precise, the "knowledge tracing" is education-based task which estimate students' mastery of knowledge by the probability of they correctly answering questions. Thus, the input of knwoledge tracing is usually defined as the question-asnwering history of a student and an arbitrary question, and the output is the probability of the student correctly answering the question.

Overview

• Probabilistic methods
• Logistic methods
• Single-state methods
  • Graph-free methods
  • Graph-based methods
• Multi-state methods
  • Graph-free methods
  • Graph-based methods
• Attention-based methods

Survey

• Liu Q, Shen S, Huang Z, et al. "A survey of knowledge tracing". arXiv preprint arXiv:2105.15106, 2021.
• Abdelrahman G, Wang Q, Nunes B P. "Knowledge Tracing: A Survey". arXiv preprint arXiv:2201.06953, 2022.

Probabilistic methods

• BKT: Corbett, Albert T., and John R. Anderson. "Knowledge tracing: Modeling the acquisition of procedural knowledge." User modeling and user-adapted interaction 4.4 (1994): 253-278.

Single-state methods

Graph-free methods

• DKT: Piech, Chris, et al. "Deep knowledge tracing." arXiv preprint arXiv:1506.05908 (2015).
• DFKT: Nagatani, Koki, et al. "Augmenting knowledge tracing by considering forgetting behavior." The world wide web conference. 2019.
• DHKT: Wang, Tianqi, Fenglong Ma, and Jing Gao. "Deep hierarchical knowledge tracing." Proceedings of the 12th International Conference on Educational Data Mining. 2019.
• EERNNA: Su, Yu, et al. "Exercise-enhanced sequential modeling for student performance prediction." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 32. No. 1. 2018.
• PDKT-C: Chen, Penghe, et al. "Prerequisite-driven deep knowledge tracing." 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018.
• Long, Ting, et al. "Tracing Knowledge State with Individual Cognition and Acquisition Estimation." Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021.
• Long, Ting, et al. " Improving Knowledge Tracing with Collaborative Information." Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 2022.

Graph-based methods

• GIKT:Yang, Yang, et al. "GIKT: a graph-based interaction model for knowledge tracing." arXiv preprint arXiv:2009.05991 (2020).
• Liu, Yunfei, et al. "Improving knowledge tracing via pre-training question embeddings." arXiv preprint arXiv:2012.05031 (2020).
• Sun, Jianwen, et al. "Collaborative Embedding for Knowledge Tracing." International Conference on Knowledge Science, Engineering and Management. Springer, Cham, 2021.

Multi-states methods

Graph-free methods

• Zhang, Jiani, et al. "Dynamic key-value memory networks for knowledge tracing." Proceedings of the 26th international conference on World Wide Web. 2017.
• Abdelrahman, Ghodai, and Qing Wang. "Knowledge tracing with sequential key-value memory networks." Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2019
• Shen, Shuanghong, et al. "Learning Process-consistent Knowledge Tracing." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021.

Graph-based methods

• Nakagawa, Hiromi, Yusuke Iwasawa, and Yutaka Matsuo. "Graph-based knowledge tracing: modeling student proficiency using graph neural network(https://rlgm.github.io/papers/70.pdf)." 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2019.
• Tong, Shiwei, et al. "Structure-based Knowledge Tracing: An Influence Propagation View." 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020.