{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:00:57Z","timestamp":1771884057974,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62107001"],"award-info":[{"award-number":["62107001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20512"],"award-info":[{"award-number":["U21A20512"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CCF-Tencent Open Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Computerized adaptive testing (CAT) targets to accurately assess the student\u2019s proficiency in the required subject\/area. The key issue is how to design a question selector that adaptively selects the best-suited questions for each student based on previous performance step by step. Most existing question selectors execute via greedy metric functions\u00a0(e.g., question information and uncertainty), which can not effectively capture data characteristics. There also exist learning-based question selectors that redefine the CAT problem as a bilevel optimization problem, where the parameter learning of the question selector and the student proficiency estimation model are coupled, which is not flexible enough. To this end, in this paper, we propose a novel CAT framework with Decoupled Learning selector\u00a0(DL-CAT). Specifically, we first use the currently estimated student ability and question characteristics as input and design a deep learning-based question selector to predict question selection scores. Then, to address the issue that there is no ground truth to measure the quality of the selected question, an approximate ground-truth and a pairwise rank loss function are specially designed to update the parameters of the question selector independently. Extensive experiments on two real datasets demonstrate that our proposed DL-CAT has certain advantages in effectiveness and significant advantages in efficiency.<\/jats:p>","DOI":"10.1007\/s40747-023-01019-1","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T03:02:41Z","timestamp":1679626961000},"page":"5555-5566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A novel computerized adaptive testing framework with decoupled learning selector"],"prefix":"10.1007","volume":"9","author":[{"given":"Haiping","family":"Ma","sequence":"first","affiliation":[]},{"given":"Yi","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Shangshang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Chuan","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0072-4047","authenticated-orcid":false,"given":"Limiao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"issue":"3","key":"1019_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1177\/01466219922031347","volume":"23","author":"RG Almond","year":"1999","unstructured":"Almond RG, Mislevy RJ (1999) Graphical models and computerized adaptive testing. Appl Psychol Meas 23(3):223\u2013237","journal-title":"Appl Psychol Meas"},{"key":"1019_CR2","doi-asserted-by":"crossref","unstructured":"Bi H, Ma H, Huang Z, Yin Y, Liu Q, Chen E, Su Y, Wang S (2020) Quality meets diversity: a model-agnostic framework for computerized adaptive testing. In: 2020 IEEE International Conference on Data Mining (ICDM), pp 42\u201351","DOI":"10.1109\/ICDM50108.2020.00013"},{"issue":"2","key":"1019_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3086820","volume":"36","author":"W Cai","year":"2017","unstructured":"Cai W, Zhang Y, Zhang Y, Zhou S, Wang W, Chen Z, Ding C (2017) Active learning for classification with maximum model change. ACM Trans Inf Syst (TOIS) 36(2):1\u201328","journal-title":"ACM Trans Inf Syst (TOIS)"},{"issue":"3","key":"1019_CR4","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1177\/014662169602000303","volume":"20","author":"H-H Chang","year":"1996","unstructured":"Chang H-H, Ying Z (1996) A global information approach to computerized adaptive testing. Appl Psychol Meas 20(3):213\u2013229","journal-title":"Appl Psychol Meas"},{"key":"1019_CR5","doi-asserted-by":"crossref","unstructured":"Cheng S, Liu Q, Chen E, Huang Z, Huang Z, Chen Y, Ma H, Hu G (2019) Dirt: deep learning enhanced item response theory for cognitive diagnosis. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 2397\u20132400","DOI":"10.1145\/3357384.3358070"},{"issue":"1","key":"1019_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/014662169501900103","volume":"19","author":"BG Dodd","year":"1995","unstructured":"Dodd BG, De Ayala R, Koch WR (1995) Computerized adaptive testing with polytomous items. Appl Psychol Meas 19(1):5\u201322","journal-title":"Appl Psychol Meas"},{"key":"1019_CR7","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605269","volume-title":"Item response theory","author":"SE Embretson","year":"2013","unstructured":"Embretson SE, Reise SP (2013) Item response theory. Psychology Press"},{"issue":"3","key":"1019_CR8","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s11257-009-9063-7","volume":"19","author":"M Feng","year":"2009","unstructured":"Feng M, Heffernan N, Koedinger K (2009) Addressing the assessment challenge with an online system that tutors as it assesses. User Model User-Adapt Interact 19(3):243\u2013266","journal-title":"User Model User-Adapt Interact"},{"key":"1019_CR9","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, pp 1126\u20131135"},{"key":"1019_CR10","doi-asserted-by":"crossref","unstructured":"Ghosh A, Lan A (2021) Bobcat: bilevel optimization-based computerized adaptive testing. arXiv preprint arXiv:2108.07386","DOI":"10.24963\/ijcai.2021\/332"},{"issue":"3","key":"1019_CR11","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/s11336-009-9111-6","volume":"74","author":"G Hooker","year":"2009","unstructured":"Hooker G, Finkelman M, Schwartzman A (2009) Paradoxical results in multidimensional item response theory. Psychometrika 74(3):419\u2013442","journal-title":"Psychometrika"},{"key":"1019_CR12","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"1019_CR13","unstructured":"Lan Y, Zhu Y, Guo J, Niu S, Cheng X (2014) Position-aware ListMLE: a sequential learning process for ranking. In: UAI, pp 449\u2013458"},{"key":"1019_CR14","unstructured":"Li X, Xu H, Zhang J, Chang H (2020) Deep reinforcement learning for adaptive learning systems. arXiv preprint arXiv:2004.08410"},{"key":"1019_CR15","doi-asserted-by":"crossref","unstructured":"Linden WJ, Pashley PJ (2009) Item selection and ability estimation in adaptive testing. In: Elements of adaptive testing. Springer, pp 3\u201330","DOI":"10.1007\/978-0-387-85461-8_1"},{"key":"1019_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/0-306-47531-6","volume-title":"Computerized adaptive testing: theory and practice","author":"WJ Linden","year":"2000","unstructured":"Linden WJ, van der Linden WJ, Glas CA (2000) Computerized adaptive testing: theory and practice. Springer"},{"key":"1019_CR17","doi-asserted-by":"crossref","unstructured":"Liu T-Y et al (2009) Learning to rank for information retrieval. Found Trends\u00ae Inf Retr 3(3):225\u2013331","DOI":"10.1561\/1500000016"},{"key":"1019_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.neucom.2017.06.008","volume":"267","author":"Y Liu","year":"2017","unstructured":"Liu Y, Zhang X, Zhu X, Guan Q, Zhao X (2017) Listnet-based object proposals ranking. Neurocomputing 267:182\u2013194","journal-title":"Neurocomputing"},{"key":"1019_CR19","doi-asserted-by":"publisher","DOI":"10.4324\/9780203056615","volume-title":"Applications of item response theory to practical testing problems","author":"FM Lord","year":"2012","unstructured":"Lord FM (2012) Applications of item response theory to practical testing problems. Routledge"},{"issue":"3","key":"1019_CR20","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1177\/01466219922031310","volume":"23","author":"RR Meijer","year":"1999","unstructured":"Meijer RR, Nering ML (1999) Computerized adaptive testing: overview and introduction. Appl Psychol Meas 23(3):187\u2013194","journal-title":"Appl Psychol Meas"},{"key":"1019_CR21","doi-asserted-by":"crossref","unstructured":"Mills CN, Steffen M (2000) The GRE computer adaptive test: operational issues. In: Computerized adaptive testing: theory and practice. Springer, pp 75\u201399","DOI":"10.1007\/0-306-47531-6_4"},{"key":"1019_CR22","doi-asserted-by":"crossref","unstructured":"Mulder J, Linden WJ (2009) Multidimensional adaptive testing with Kullback\u2013Leibler information item selection. In: Elements of adaptive testing. Springer, pp 77\u2013101","DOI":"10.1007\/978-0-387-85461-8_4"},{"key":"1019_CR23","doi-asserted-by":"crossref","unstructured":"Pang L, Xu J, Ai Q, Lan Y, Cheng X, Wen J (2020) Setrank: learning a permutation-invariant ranking model for information retrieval. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 499\u2013508","DOI":"10.1145\/3397271.3401104"},{"key":"1019_CR24","unstructured":"Plajner M, Vomlel J (2015) Bayesian network models for adaptive testing. arXiv preprint arXiv:1511.08488"},{"key":"1019_CR25","unstructured":"Riedmiller M, Lernen A (2014) Multi layer perceptron. Machine Learning Lab Special Lecture, University of Freiburg, pp 7\u201324"},{"key":"1019_CR26","unstructured":"Rudner LM (2002) An examination of decision-theory adaptive testing procedures. In: Annual meeting of the American Educational Research Association"},{"key":"1019_CR27","doi-asserted-by":"crossref","unstructured":"Rudner LM (2009) Implementing the graduate management admission test computerized adaptive test. In: Elements of adaptive testing. Springer, pp 151\u2013165","DOI":"10.1007\/978-0-387-85461-8_8"},{"key":"1019_CR28","doi-asserted-by":"crossref","unstructured":"Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, pp 253\u2013260","DOI":"10.1145\/564376.564421"},{"issue":"2","key":"1019_CR29","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/BF02294343","volume":"61","author":"DO Segall","year":"1996","unstructured":"Segall DO (1996) Multidimensional adaptive testing. Psychometrika 61(2):331\u2013354","journal-title":"Psychometrika"},{"issue":"1","key":"1019_CR30","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1137\/15M1043303","volume":"5","author":"P Tsilifis","year":"2017","unstructured":"Tsilifis P, Ghanem RG, Hajali P (2017) Efficient Bayesian experimentation using an expected information gain lower bound. SIAM\/ASA J Uncertain Quantif 5(1):30\u201362","journal-title":"SIAM\/ASA J Uncertain Quantif"},{"issue":"4","key":"1019_CR31","doi-asserted-by":"publisher","first-page":"398","DOI":"10.3102\/10769986024004398","volume":"24","author":"WJ Van Der Linden","year":"1999","unstructured":"Van Der Linden WJ (1999) Multidimensional adaptive testing with a minimum error-variance criterion. J Educ Behav Stat 24(4):398\u2013412","journal-title":"J Educ Behav Stat"},{"issue":"4","key":"1019_CR32","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/BF02295132","volume":"67","author":"BP Veldkamp","year":"2002","unstructured":"Veldkamp BP, van der Linden WJ (2002) Multidimensional adaptive testing with constraints on test content. Psychometrika 67(4):575\u2013588","journal-title":"Psychometrika"},{"issue":"2","key":"1019_CR33","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1023\/A:1019956318069","volume":"18","author":"R Vilalta","year":"2002","unstructured":"Vilalta R, Drissi Y (2002) A perspective view and survey of meta-learning. Artif Intell Rev 18(2):77\u201395","journal-title":"Artif Intell Rev"},{"issue":"3","key":"1019_CR34","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s11336-011-9215-7","volume":"76","author":"C Wang","year":"2011","unstructured":"Wang C, Chang H-H (2011) Item selection in multidimensional computerized adaptive testing\u2014gaining information from different angles. Psychometrika 76(3):363\u2013384","journal-title":"Psychometrika"},{"issue":"1","key":"1019_CR35","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s11336-010-9186-0","volume":"76","author":"C Wang","year":"2011","unstructured":"Wang C, Chang H-H, Boughton KA (2011) Kullback\u2013Leibler information and its applications in multi-dimensional adaptive testing. Psychometrika 76(1):13\u201339","journal-title":"Psychometrika"},{"key":"1019_CR36","volume-title":"Going deeper with deep knowledge tracing","author":"X Xiong","year":"2016","unstructured":"Xiong X, Zhao S, Van Inwegen EG, Beck JE (2016) Going deeper with deep knowledge tracing. International Educational Data Mining Society"},{"key":"1019_CR37","doi-asserted-by":"crossref","unstructured":"Yao L (2013) Comparing the performance of five multidimensional CAT selection procedures with different stopping rules. Appl Psychol Meas 37(1):3\u201323","DOI":"10.1177\/0146621612455687"},{"key":"1019_CR38","doi-asserted-by":"crossref","unstructured":"Yoo D, Kweon IS (2019) Learning loss for active learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 93\u2013102","DOI":"10.1109\/CVPR.2019.00018"},{"key":"1019_CR39","unstructured":"Yoon J, Kim T, Dia O, Kim S, Bengio Y, Ahn S (2018) Bayesian model-agnostic meta-learning. In: Advances in neural information processing systems, vol 31"},{"key":"1019_CR40","doi-asserted-by":"crossref","unstructured":"Zhuang Y, Liu Q, Huang Z, Li Z, Shen S, Ma H (2022) Fully adaptive framework: neural computerized adaptive testing for online education","DOI":"10.1609\/aaai.v36i4.20399"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01019-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01019-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01019-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T17:21:07Z","timestamp":1695403267000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01019-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,24]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1019"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01019-1","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,24]]},"assertion":[{"value":"17 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}