{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/survey-of-computerized-adaptive-testing-a","title":"Survey of Computerized Adaptive Testing: A Machine Learning Perspective","arxiv_id":"2404.00712","date":"2024-03-31","proceeding":null,"authors":["Qi Liu","Yan Zhuang","Haoyang Bi","Zhenya Huang","Weizhe Huang","Jiatong Li","Junhao Yu","Zirui Liu","Zirui Hu","Yuting Hong","Zachary A. Pardos","Haiping Ma","Mengxiao Zhu","Shijin Wang","Enhong Chen"],"abstract":"Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthcare, sports, and sociology, CAT has revolutionized testing practices. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing method. By examining the test question selection algorithm at the heart of CAT's adaptivity, we shed light on its functionality. Furthermore, we delve into cognitive diagnosis models, question bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing.","url_abs":"https://arxiv.org/abs/2404.00712v2","url_pdf":"https://arxiv.org/pdf/2404.00712v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"survey-of-computerized-adaptive-testing-a","repo_url":"https://github.com/bigdata-ustc/educat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"question-selection","task_name":"Question Selection"},{"task_slug":"sociology","task_name":"Sociology"},{"task_slug":"survey","task_name":"Survey"},{"task_slug":"cognitive-diagnosis","task_name":"cognitive diagnosis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2404.00712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00712"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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