GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing-Reference-Cited by-同舟云学术

GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Hangyu¹^ORCID,Long Ting²^ORCID,Yin Liang¹^ORCID,Zhang Weinan¹^ORCID,Xia Wei³^ORCID,Hong Qichen⁴^ORCID,Xia Dingyin⁴^ORCID,Tang Ruiming³^ORCID,Yu Yong¹^ORCID

Affiliation:

1. Shanghai Jiaotong University, Shanghai, China

2. Jilin University, Changchun, China

3. Huawei Noah's Ark Lab, Shenzhen, China

4. Huawei CBG Edu AI Lab, Shenzhen, China

Funder

National Key R&D Program of China

Shanghai Municipal Science and Technology Major Project

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599367

Reference44 articles.

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3. Quality Meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

4. Andrew P Bradley . 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition , Vol. 30 , 7 ( 1997 ), 1145--1159. Andrew P Bradley. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, Vol. 30, 7 (1997), 1145--1159.

5. Psychometrics Behind Computerized Adaptive Testing

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