[2] Jiahao Chen, Zitao Liu, Shuyan Huang, Qiongqiong Liu, and Weiqi Luo. Improving interpretability of deep sequential knowledge tracing models with question-centric cognitive representations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14196-14204, 2023.
[3] Penghe Chen, Yu Lu, Vincent W Zheng, and Yang Pian. Prerequisite-driven deep knowledge tracing. In 2018 IEEE International Conference on Data Mining, pages 39-48. IEEE, 2018.
[4] Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, and Jaewe Heo. Towards an appropriate query, key, and value computation for knowledge tracing. In Proceedings of the Seventh ACM Conference on Learning@Scale, pages 341-344, 2020.
[5] Youngduck Choi, Youngnam Lee, Dongmin Shin, Junghyun Cho, Seoyon Park, Seewoo Lee, Jineon Baek, Chan Bae, Byungsoo Kim, and Jaewe Heo. Ednet: A large-scale hierarchical dataset in education. In International Conference on Artificial Intelligence in Education, pages 69-73. Springer, 2020.
[6] Mingyu Feng, Neil Heffernan, and Kenneth Koedinger. Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-adapted Interaction, 19(3):243-266, 2009.
[7] Aritra Ghosh, Neil T. Heffernan, and Andrew S. Lan. Context-aware attentive knowledge tracing. In Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash, editors, The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020, pages 2330-2339. ACM, 2020.
[8] Xiaopeng Guo, Zhijie Huang, Jie Gao, Mingyu Shang, Maojing Shu, and Jun Sun. Enhancing knowledge tracing via adversarial training. In Proceedings of the 29th ACM International Conference on Multimedia, pages 367-375, 2021.
[9] Shuyan Huang, Zitao Liu, Xiangyu Zhao, Weiqi Luo, and Jian Weng. Towards robust knowledge tracing models via k-sparse attention. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2441-2445, 2023.
[10] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[11] Jinseok Lee and Dit-Yan Yeung. Knowledge query network for knowledge tracing: How knowledge interacts with skills. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pages 491-500, 2019.
[12] Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, and Guoping Hu. EKT: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering, 33(1):100-115, 2019.
[13] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
[14] Yunfei Liu, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, and Yong Yu. Improving knowledge tracing via pre-training question embeddings. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pages 1577-1583. ijcai.org, 2020.
[15] Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Boyu Gao, Weiqi Luo, and Jian Weng. Enhancing deep knowledge tracing with auxiliary tasks. In Proceedings of the ACM Web Conference 2023, pages 4178-4187, 2023.
[16] Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, and Weiqi Luo. SIMPLEKT: A simple but tough-to-beat baseline for knowledge tracing. In The Eleventh International Conference on Learning Representations, 2022.
[17] Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Jiliang Tang, and Weiqi Luo. pyKT: A python library to benchmark deep learning based knowledge tracing models. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
[18] Ting Long, Yunfei Liu, Jian Shen, Weinan Zhang, and Yong Yu. Tracing knowledge state with individual cognition and acquisition estimation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 173-182, 2021.