Fei Liu
ABSTRACT
Exercise recommendation is a fundamental and important task in the E-learning system, facilitating students' personalized learning. Most existing exercise recommendation algorithms design a scoring criterion (e.g., weakest mastery, lowest historical correctness) in conjunction with experience, and then recommend the recommended knowledge concepts (KCs). These algorithms rely entirely on the scoring criteria by treating exercise recommendations as a centralized system. However, it is a complex problem for the centralized system to choose a limited number of exercises in a period of time to consolidate and learn the KCs efficiently. Moreover, different groups of students (e.g., different countries, schools, or classes) have different solutions for the same group of KCs according to their own situations, in the spirit of competency-based instructing. Therefore, we propose Meta Multi-Agent Exercise Recommendation (MMER). Specifically, we design the multi-agent exercise recommendation module, in which the KCs involved in exercises are considered agents with competition and cooperation among them. And the meta-training stage is designed to learn a robust recommendation module for new student groups. Extensive experiments on real-world datasets validate the satisfactory performance of the proposed model. Furthermore, the effectiveness of the multi-agent and meta-training part is demonstrated for the model in recommendation applications.
Supplemental Material
- Ghodai Abdelrahman, Qing Wang, and Bernardo Pereira Nunes. 2022. Knowledge tracing: A survey. arXiv preprint arXiv:2201.06953 (2022). doi: 10.48550/arXiv.2201.06953.Google Scholar
- M Mehdi Afsar, Trafford Crump, and Behrouz Far. 2022. Reinforcement learning based recommender systems: A survey. Comput. Surveys, Vol. 55, 7 (2022), 1--38.Google ScholarDigital Library
- Lawrence E Blume. 1993. The statistical mechanics of strategic interaction. Games and Economic Behavior, Vol. 5, 3 (1993), 387--424.Google ScholarCross Ref
- Chenyang Bu, Fei Liu, Zhiyong Cao, Lei Li, Yuhong Zhang, Xuegang Hu, and Wenjian Luo. 2022. Cognitive diagnostic model made more practical by genetic algorithm. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), Vol. 7, 2 (2022), 447--461. doi: 10.1109/TETCI.2022.3182692.Google ScholarCross Ref
- Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Daniele Giardino, Marco Re, and Sergio Spanò. 2021. Multi-agent reinforcement learning: A review of challenges and applications. Applied Sciences, Vol. 11, 11 (2021), 4948.Google ScholarCross Ref
- Yunxiao Chen, Xiaoou Li, Jingchen Liu, and Zhiliang Ying. 2018. Recommendation system for adaptive learning. Applied Psychological Measurement, Vol. 42, 1 (2018), 24--41.Google ScholarCross Ref
- Yuying Chen, Qi Liu, Zhenya Huang, Le Wu, Enhong Chen, Run-ze Wu, Yu Su, and Guoping Hu. 2017. Tracking knowledge proficiency of students with educational priors. In Proceedings of the ACM on Conference on Information and Knowledge Management (CIKM). ACM, 989--998.Google ScholarDigital Library
- Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction (UMUAI), Vol. 4, 4 (1994), 253--278.Google ScholarCross Ref
- Miao Dai, Jui-Long Hung, Xu Du, Hengtao Tang, and Hao Li. 2021. Knowledge tracing: A review of available technologies. Journal of Educational Technology Development and Exchange (JETDE), Vol. 14, 2 (2021), 1--19.Google ScholarCross Ref
- Janez Demvsar. 2006. Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research, Vol. 7 (2006), 1--30.Google ScholarDigital Library
- LV DiBello, LA Roussos, and W Stout. 2007. Review of cognitively diagnostic assessment and a summary of psychometric models., Vol. 26 (2007), 970--1030.Google Scholar
- Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of International Conference on Machine Learning (ICML). PMLR, 1126--1135.Google Scholar
- 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). 2330--2339.Google ScholarDigital Library
- Xuegang Hu, Fei Liu, and Chenyang Bu. 2020. Research advances on knowledge tracing models in educational big data. Journal of Computer Research and Development, Vol. 57, 12 (2020), 2523--2546.Google Scholar
- Zhenya Huang, Qi Liu, Yuying Chen, Le Wu, Keli Xiao, Enhong Chen, Haiping Ma, and Guoping Hu. 2020. Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students. ACM Transactions on Information Systems (TOIS), Vol. 38, 2, Article 19 (2020), 33 pages. https://doi.org/10.1145/3379507Google ScholarDigital Library
- Zhenya Huang, Qi Liu, Chengxiang Zhai, Yu Yin, Enhong Chen, Weibo Gao, and Guoping Hu. 2019. Exploring multi-objective exercise recommendations in online education systems. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM). 1261--1270.Google ScholarDigital Library
- Shristi Shakya Khanal, PWC Prasad, Abeer Alsadoon, and Angelika Maag. 2020. A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies, Vol. 25, 4 (2020), 2635--2664.Google ScholarCross Ref
- Fei Liu, Xuegang Hu, Chenyang Bu, and Kui Yu. 2022. Fuzzy Bayesian knowledge tracing. IEEE Transactions on Fuzzy Systems (TFS), Vol. 30, 7 (2022), 2412--2425. https://doi.org/10.1109/TFUZZ.2021.3083177Google ScholarDigital Library
- 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 (TKDE), Vol. 33, 1 (2019), 100--115.Google ScholarDigital Library
- Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, and Yonghe Zheng. 2021. A survey of knowledge tracing. arXiv preprint arXiv:2105.15106 (2021). doi: 10.48550/arXiv.2105.15106.Google Scholar
- Ting Long, Yunfei Liu, Jian Shen, Weinan Zhang, and Yong Yu. 2021. Tracing knowledge state with individual cognition and acquisition estimation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 173--182.Google ScholarDigital Library
- Yu Lu, Deliang Wang, Qinggang Meng, and Penghe Chen. 2020. Towards interpretable deep learning models for knowledge tracing. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED). Springer, 185--190.Google ScholarDigital Library
- Haiping Ma, Jingyuan Wang, Hengshu Zhu, Xin Xia, Haifeng Zhang, Xingyi Zhang, and Lei Zhang. 2022a. Reconciling cognitive modeling with knowledge forgetting: A continuous time-aware neural network approach. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 2174--2181.Google ScholarCross Ref
- Haiping Ma, Jinwei Zhu, Shangshang Yang, Qi Liu, Haifeng Zhang, Xingyi Zhang, Yunbo Cao, and Xuemin Zhao. 2022b. A prerequisite attention model for knowledge proficiency diagnosis of students. In Proceedings of the ACM International Conference on Information & Knowledge Management (CIKM). ACM, 4304--4308.Google ScholarDigital Library
- Sein Minn, Yi Yu, Michel C Desmarais, Feida Zhu, and Jill-Jênn Vie. 2018. Deep knowledge tracing and dynamic student classification for knowledge tracing. In Proceddings of IEEE International Conference on Data Mining (ICDM). IEEE, 1182--1187.Google ScholarCross Ref
- Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Proceedings of International Conference on Neural Information Processing Systems (NeurIPS). 505--513.Google Scholar
- Saman Shishehchi, Seyed Yashar Banihashem, Nor Azan Mat Zin, and Shahrul Azman Mohd Noah. 2011. Review of personalized recommendation techniques for learners in e-learning systems. In Proceedings of International Conference on Semantic Technology and Information Retrieval. IEEE, 277--281.Google ScholarCross Ref
- 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 AAAI Conference on Artificial Intelligence (AAAI), Vol. 32.Google ScholarCross Ref
- Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press.Google ScholarDigital Library
- María Cora Urdaneta-Ponte, Amaia Mendez-Zorrilla, and Ibon Oleagordia-Ruiz. 2021. Recommendation systems for education: systematic review. Electronics, Vol. 10, 14 (2021), 1611.Google ScholarCross Ref
- Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang. 2018. Mean field multi-agent reinforcement learning. In Proceedings of International Conference on Machine Learning (ICML). PMLR, 5571--5580.Google Scholar
- Jiani Zhang, Xingjian Shi, Irwin King, and Dit Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. In Proceedings of International Conference on World Wide Web (WWW). ACM, 765--774.Google ScholarDigital Library
- Yuqiang Zhou, Qi Liu, Jinze Wu, Fei Wang, Zhenya Huang, Wei Tong, Hui Xiong, Enhong Chen, and Jianhui Ma. 2021. Modeling context-aware features for cognitive diagnosis in student learning. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery & Data Mining (SIGKDD). 2420--2428.Google ScholarDigital Library
Index Terms
- Meta Multi-agent Exercise Recommendation: A Game Application Perspective
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