Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach

Author:

Kwon Soonwoo1ORCID,Kim Sojung2ORCID,Lee Seunghyun1ORCID,Kim Jin-Young1ORCID,An Suyeong1ORCID,Kim Kyuseok1ORCID

Affiliation:

1. Riiid AI Research, Seoul, Republic of Korea

2. Riiid AI Research, Seoul, South Korea

Publisher

ACM

Reference25 articles.

1. H. Bi , H. Ma , Z. Huang , Y. Yin , Q. Liu , E. Chen , Y. Su , and S. Wang . 2020 . Quality Meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society , Los Alamitos, CA, USA, 42--51. H. Bi, H. Ma, Z. Huang, Y. Yin, Q. Liu, E. Chen, Y. Su, and S. Wang. 2020. Quality Meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, Los Alamitos, CA, USA, 42--51.

2. A Global Information Approach to Computerized Adaptive Testing

3. Bias and Debias in Recommender System: A Survey and Future Directions

4. Younyoung Choi and Cayce McClenen . 2020. Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks. Applied Sciences 10, 22 ( 2020 ). Younyoung Choi and Cayce McClenen. 2020. Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks. Applied Sciences 10, 22 (2020).

5. BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

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