Aritra Ghosh, Andrew S. Lan:BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. IJCAI 2021: 2410-2417:https://arxiv.org/pdf/2108.07386.pdf
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing:https://arxiv.org/pdf/2101.05986.pdf
[Nurakhmetov, 2019] Darkhan Nurakhmetov. Reinforcement learning applied to adaptive classification testing. In Theoretical and Practical Advances in Computer-based Educational Measurement, pages 325–336. Springer, Cham, 2019.
[Li et al., 2020] Xiao Li, Hanchen Xu, Jinming Zhang, and Huahua Chang. Deep reinforcement learning for adaptive learning systems. arXiv preprint arXiv:2004.08410, 2020.:https://arxiv.org/pdf/2004.08410.pdf
Comprehensive Empirical Analysis of Stop Criteria in Computerized Adaptive Testing. CSEDU (1) 2021: 48-59
Multi-objective optimization of item selection in computerized adaptive testing. GECCO 2021
Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items. J. Classif. 37(3): 709-729 (2020)
ALICAT: a customized approach to item selection process in computerized adaptive testing. J. Braz. Comput. Soc. 26(1): 4 (2020)
Towards a Tailored Hybrid Recommendation-based System for Computerized Adaptive Testing through Clustering and IRT. CSEDU (1) 2020
Feasibility of computerized adaptive testing evaluated by Monte-Carlo and post-hoc simulations. FedCSIS 2020