{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:31:03Z","timestamp":1780471863792,"version":"3.54.1"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFF0901003"],"award-info":[{"award-number":["2021YFF0901003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62337001, U20A20229, and U23A20319"],"award-info":[{"award-number":["62337001, U20A20229, and U23A20319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["GXXT-2022-042"],"award-info":[{"award-number":["GXXT-2022-042"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n            The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a fundamental challenge is designing proper test for assessing the students\u2019 cognitive status on knowledge and skills accurately and efficiently. One promising approach, referred to as\n            <jats:italic>Computerized Adaptive Testing<\/jats:italic>\n            (CAT), is to administrate computer-automated tests that alternately select the next item for each examinee and estimate their cognitive states given their responses to the selected items. Nevertheless, existing CAT systems suffer from inflexibility in item selection and ineffectiveness in cognitive state estimation, respectively. In this article, we propose a Model-Agnostic adaptive testing framework via Meta-leaned Gradient Embeddings, MAMGE for short, improving both item selection and cognitive state estimation simultaneously. For item selection, we design a Gradient Embedding-based Item Selector (GEIS) which incorporates the concept of gradient embeddings to represent items and selects the best ones that are both informative and representative. For cognitive state estimation, we propose a Meta-learned Cognitive State Estimator (MCSE) to automatically control the estimation process by learning to learn a proper initialization and dynamically inferred updates. Both MCSE and GEIS are inherently model-agnostic, and the two modules have an ingenious connection via meta-learned gradient embeddings. Finally, extensive experiments evaluate the effectiveness and flexibility of MAMGE.\n          <\/jats:p>","DOI":"10.1145\/3660642","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T15:07:45Z","timestamp":1716476865000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Model-Agnostic Adaptive Testing for Intelligent Education Systems via Meta-learned Gradient Embeddings"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6824-1407","authenticated-orcid":false,"given":"Haoyang","family":"Bi","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China and Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5599-0625","authenticated-orcid":false,"given":"Han","family":"Wu","sequence":"additional","affiliation":[{"name":"Career Science Lab, Boss Zhipin, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1986-9710","authenticated-orcid":false,"given":"Weidong","family":"He","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-0420","authenticated-orcid":false,"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China and Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-5523","authenticated-orcid":false,"given":"Yu","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5099-1987","authenticated-orcid":false,"given":"Haiping","family":"Ma","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7950-4919","authenticated-orcid":false,"given":"Yu","family":"Su","sequence":"additional","affiliation":[{"name":"Hefei Normal University, Hefei, China and Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9202-7678","authenticated-orcid":false,"given":"Shijin","family":"Wang","sequence":"additional","affiliation":[{"name":"iFLYTEK AI Research (Central China), Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"10","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Abbas Momin","year":"2022","unstructured":"Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen. 2022. Sharp-maml: Sharpness-aware model-agnostic meta learning. In Proceedings of the International Conference on Machine Learning. PMLR, 10\u201332."},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Ash Jordan T.","year":"2019","unstructured":"Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019. Deep batch active learning by diverse, uncertain gradient lower bounds. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_4_2","first-page":"301","volume-title":"Proceedings of the 34th International Conference on Machine Learning","volume":"70","author":"Bachman Philip","year":"2017","unstructured":"Philip Bachman, Alessandro Sordoni, and Adam Trischler. 2017. Learning algorithms for active learning. In Proceedings of the 34th International Conference on Machine Learning, Vol. 70. JMLR.org, 301\u2013310."},{"key":"e_1_3_2_5_2","first-page":"20755","article-title":"Meta-learning with adaptive hyperparameters","volume":"33","author":"Baik Sungyong","year":"2020","unstructured":"Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, and Kyoung Mu Lee. 2020a. Meta-learning with adaptive hyperparameters. Advances in Neural Information Processing Systems 33 (2020), 20755\u201320765.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_6_2","first-page":"2379","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Baik Sungyong","year":"2020","unstructured":"Sungyong Baik, Seokil Hong, and Kyoung Mu Lee. 2020b. Learning to forget for meta-learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2379\u20132387."},{"key":"e_1_3_2_7_2","first-page":"35","volume-title":"Proceedings of the International Conference on Computational Learning Theory","author":"Balcan Maria-Florina","year":"2007","unstructured":"Maria-Florina Balcan, Andrei Broder, and Tong Zhang. 2007. Margin based active learning. In Proceedings of the International Conference on Computational Learning Theory. Springer, 35\u201350."},{"issue":"2","key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446343","article-title":"Active learning for effectively fine-tuning transfer learning to downstream task","volume":"12","author":"Bashar Md Abul","year":"2021","unstructured":"Md Abul Bashar and Richi Nayak. 2021. Active learning for effectively fine-tuning transfer learning to downstream task. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 2 (2021), 1\u201324.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_9_2","first-page":"42","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM)","author":"Bi Haoyang","year":"2020","unstructured":"Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, and Shijin Wang. 2020. Quality meets diversity: A model-agnostic framework for computerized adaptive testing. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE, 42\u201351."},{"issue":"1","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11336-014-9401-5","article-title":"Psychometrics behind computerized adaptive testing","volume":"80","author":"Chang Hua-Hua","year":"2015","unstructured":"Hua-Hua Chang. 2015. Psychometrics behind computerized adaptive testing. Psychometrika 80, 1 (2015), 1\u201320.","journal-title":"Psychometrika"},{"issue":"3","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1177\/014662169602000303","article-title":"A global information approach to computerized adaptive testing","volume":"20","author":"Chang Hua-Hua","year":"1996","unstructured":"Hua-Hua Chang and Zhiliang Ying. 1996. A global information approach to computerized adaptive testing. Applied Psychological Measurement 20, 3 (1996), 213\u2013229.","journal-title":"Applied Psychological Measurement"},{"issue":"1","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/0013164410387338","article-title":"A new stopping rule for computerized adaptive testing","volume":"71","author":"Choi Seung W.","year":"2011","unstructured":"Seung W. Choi, Matthew W. Grady, and Barbara G. Dodd. 2011. A new stopping rule for computerized adaptive testing. Educational and Psychological Measurement 71, 1 (2011), 37\u201353.","journal-title":"Educational and Psychological Measurement"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","DOI":"10.4324\/9781410605269","volume-title":"Item Response Theory","author":"Embretson Susan E.","year":"2013","unstructured":"Susan E. Embretson and Steven P. Reise. 2013. Item Response Theory. Psychology Press."},{"key":"e_1_3_2_14_2","first-page":"1126","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1126\u20131135."},{"key":"e_1_3_2_15_2","first-page":"1183","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gal Yarin","year":"2017","unstructured":"Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017. Deep bayesian active learning with image data. In Proceedings of the International Conference on Machine Learning. PMLR, 1183\u20131192."},{"key":"e_1_3_2_16_2","first-page":"501","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Gao Weibo","year":"2021","unstructured":"Weibo Gao, Qi Liu, Zhenya Huang, Yu Yin, Haoyang Bi, Mu-Chun Wang, Jianhui Ma, Shijin Wang, and Yu Su. 2021. RCD: Relation map driven cognitive diagnosis for intelligent education systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 501\u2013510."},{"key":"e_1_3_2_17_2","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Ghosh Aritra","year":"2021","unstructured":"Aritra Ghosh and Andrew Lan. 2021. BOBCAT: Bilevel optimization-based computerized adaptive testing. In Proceedings of the International Joint Conference on Artificial Intelligence."},{"issue":"11","key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1016\/j.jclinepi.2006.02.010","article-title":"Computer adaptive testing improved accuracy and precision of scores over random item selection in a physical functioning item bank","volume":"59","author":"Haley Stephen M.","year":"2006","unstructured":"Stephen M. Haley, Pengsheng Ni, Ronald K. Hambleton, Mary D. Slavin, and Alan M. Jette. 2006. Computer adaptive testing improved accuracy and precision of scores over random item selection in a physical functioning item bank. Journal of Clinical Epidemiology 59, 11 (2006), 1174\u20131182.","journal-title":"Journal of Clinical Epidemiology"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1145\/3583780.3615049","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","author":"Hong Yuting","year":"2023","unstructured":"Yuting Hong, Shiwei Tong, Wei Huang, Yan Zhuang, Qi Liu, Enhong Chen, Xin Li, and Yuanjing He. 2023. Search-efficient computerized adaptive testing. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 773\u2013782."},{"issue":"3","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s11336-009-9111-6","article-title":"Paradoxical results in multidimensional item response theory","volume":"74","author":"Hooker Giles","year":"2009","unstructured":"Giles Hooker, Matthew Finkelman, and Armin Schwartzman. 2009. Paradoxical results in multidimensional item response theory. Psychometrika 74, 3 (2009), 419\u2013442.","journal-title":"Psychometrika"},{"issue":"9","key":"e_1_3_2_21_2","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales Timothy","year":"2021","unstructured":"Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2021. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2021), 5149\u20135169.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_22_2","first-page":"1","volume-title":"Proceedings of the 30th Conference on Neural Information Processing Systems","author":"Jiaji H.","year":"2016","unstructured":"H. Jiaji, C. Rewon, R. Vinay, L. Hairong, S. Sanjeev, and C. Adam. 2016. Active learning for speech recognition: The power of gradients. In Proceedings of the 30th Conference on Neural Information Processing Systems. NIPS, Barcelona, Spain, 1\u20135."},{"key":"e_1_3_2_23_2","first-page":"10177","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Jiang Weisen","year":"2022","unstructured":"Weisen Jiang, James Kwok, and Yu Zhang. 2022. Subspace learning for effective meta-learning. In Proceedings of the International Conference on Machine Learning. PMLR, 10177\u201310194."},{"key":"e_1_3_2_24_2","volume-title":"ICML Deep Learning Workshop","author":"Koch Gregory","year":"2015","unstructured":"Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015. Siamese neural networks for one-shot image recognition. In ICML Deep Learning Workshop, Vol. 2. Lille."},{"key":"e_1_3_2_25_2","first-page":"4228","article-title":"Learning active learning from data","volume":"30","author":"Konyushkova Ksenia","year":"2017","unstructured":"Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua. 2017. Learning active learning from data. Advances in Neural Information Processing Systems 30 (2017), 4228\u20134238.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_26_2","first-page":"714","volume-title":"Proceedings of the 16th ACM Conference on Recommender Systems","author":"Lex Elisabeth","year":"2022","unstructured":"Elisabeth Lex and Markus Schedl. 2022. Psychology-informed recommender systems tutorial. In Proceedings of the 16th ACM Conference on Recommender Systems. 714\u2013717."},{"key":"e_1_3_2_27_2","unstructured":"Zhenguo Li Fengwei Zhou Fei Chen and Hang Li. 2017. Meta-sgd: Learning to learn quickly for few-shot learning. arXiv:1707.09835."},{"issue":"1","key":"e_1_3_2_28_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TKDE.2019.2924374","article-title":"Ekt: Exercise-aware knowledge tracing for student performance prediction","volume":"33","author":"Liu Qi","year":"2019","unstructured":"Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, and Guoping Hu. 2019. Ekt: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering 33, 1 (2019), 100\u2013115.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"4","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3168361","article-title":"Fuzzy cognitive diagnosis for modelling examinee performance","volume":"9","author":"Liu Qi","year":"2018","unstructured":"Qi Liu, Runze Wu, Enhong Chen, Guandong Xu, Yu Su, Zhigang Chen, and Guoping Hu. 2018. Fuzzy cognitive diagnosis for modelling examinee performance. ACM Transactions on Intelligent Systems and Technology (TIST) 9, 4 (2018), 1\u201326.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_30_2","volume-title":"Proceedings of the ACM Web Conference 2024","author":"Liu Shuo","year":"2024","unstructured":"Shuo Liu, Junhao Shen, Hong Qian, and Aimin Zhou. 2024. Inductive cognitive diagnosis for fast student learning in web-based online intelligent education systems. In Proceedings of the ACM Web Conference 2024."},{"issue":"2","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3372121","article-title":"Pair-based uncertainty and diversity promoting early active learning for person re-identification","volume":"11","author":"Liu Wenhe","year":"2020","unstructured":"Wenhe Liu, Xiaojun Chang, Ling Chen, Dinh Phung, Xiaoqin Zhang, Yi Yang, and Alexander G. Hauptmann. 2020. Pair-based uncertainty and diversity promoting early active learning for person re-identification. ACM Transactions on Intelligent Systems and Technology 11, 2 (2020), 1\u201315.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_32_2","volume-title":"Applications of Item Response Theory to Practical Testing Problems","author":"Lord Frederic M.","year":"1980","unstructured":"Frederic M. Lord. 1980. Applications of Item Response Theory to Practical Testing Problems. Routledge."},{"key":"e_1_3_2_33_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-69218-0","volume-title":"Computerized Adaptive and Multistage Testing with R: Using Packages CATR and MSTR","author":"Magis David","year":"2017","unstructured":"David Magis, Duanli Yan, and Alina A. Von Davier. 2017. Computerized Adaptive and Multistage Testing with R: Using Packages CATR and MSTR. Springer."},{"issue":"1","key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1037\/edu0000205","article-title":"Computer-adaptive testing: Implications for students\u2019 achievement, motivation, engagement, and subjective test experience","volume":"110","author":"Martin Andrew J.","year":"2018","unstructured":"Andrew J. Martin and Goran Lazendic. 2018. Computer-adaptive testing: Implications for students\u2019 achievement, motivation, engagement, and subjective test experience. Journal of Educational Psychology 110, 1 (2018), 27.","journal-title":"Journal of Educational Psychology"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/0-306-47531-6_4","volume-title":"Computerized Adaptive Testing: Theory and Practice","author":"Mills Craig N.","year":"2000","unstructured":"Craig N. Mills and Manfred Steffen. 2000. The GRE computer adaptive test: Operational issues. In: Van der Linden Wim J., Glas, Gees A. W. (Eds.), Computerized Adaptive Testing: Theory and Practice. Springer, 75\u201399."},{"key":"e_1_3_2_36_2","first-page":"2554","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Munkhdalai Tsendsuren","year":"2017","unstructured":"Tsendsuren Munkhdalai and Hong Yu. 2017. Meta networks. In Proceedings of the International Conference on Machine Learning. PMLR, 2554\u20132563."},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/978-3-030-18480-3_17","article-title":"Reinforcement learning applied to adaptive classification testing","author":"Nurakhmetov Darkhan","year":"2019","unstructured":"Darkhan Nurakhmetov. 2019. Reinforcement learning applied to adaptive classification testing. Theoretical and Practical Advances in Computer-Based Educational Measurement (2019), 325\u2013336.","journal-title":"Theoretical and Practical Advances in Computer-Based Educational Measurement"},{"key":"e_1_3_2_38_2","unstructured":"Martin Plajner and Jir\u00ed Vomlel. 2016. Student skill models in adaptive testing. In Probabilistic Graphical Models. JMLR: Workshop and Conference Proceedings vol 52. 403\u2013414."},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/978-0-387-85461-8_8","volume-title":"Elements of Adaptive Testing","author":"Rudner Lawrence M.","year":"2009","unstructured":"Lawrence M. Rudner. 2009. Implementing the graduate management admission test computerized adaptive test. In: Van der Linden Wim J., Glas Cees A. W. (Eds.), Elements of Adaptive Testing. Springer, 151\u2013165."},{"key":"e_1_3_2_40_2","first-page":"1842","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Santoro Adam","year":"2016","unstructured":"Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1842\u20131850."},{"key":"e_1_3_2_41_2","unstructured":"Ozan Sener and Silvio Savarese. 2017. Active learning for convolutional neural networks: A core-set approach. arXiv:1708.00489."},{"key":"e_1_3_2_42_2","series-title":"JMLR Workshop and Conference Proceedings","first-page":"1","volume-title":"Active Learning and Experimental Design Workshop in Conjunction with AISTATS 2010","author":"Settles Burr","year":"2011","unstructured":"Burr Settles. 2011. From theories to queries: Active learning in practice. In Active Learning and Experimental Design Workshop in Conjunction with AISTATS 2010. JMLR Workshop and Conference Proceedings, 1\u201318."},{"key":"e_1_3_2_43_2","first-page":"14928","volume-title":"Proceedings of the 38th AAAI Conference on Artificial Intelligence","author":"Shen Junhao","year":"2024","unstructured":"Junhao Shen, Hong Qian, Wei Zhang, and Aimin Zhou. 2024b. Symbolic cognitive diagnosis via hybrid optimization for intelligent education systems. In Proceedings of the 38th AAAI Conference on Artificial Intelligence. 14928\u201314936."},{"key":"e_1_3_2_44_2","first-page":"1","article-title":"A survey of knowledge tracing: Models, variants, and applications.","author":"Shen Shuanghong","year":"2024","unstructured":"Shuanghong Shen, Qi Liu, Zhenya Huang, Yonghe Zheng, Minghao Yin, Minjuan Wang, and Enhong Chen. 2024a. A survey of knowledge tracing: Models, variants, and applications. IEEE Transactions on Learning Technologies (2024), 1\u201322.","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"e_1_3_2_45_2","unstructured":"Jake Snell Kevin Swersky and Richard S. Zemel. 2017. Prototypical networks for few-shot learning. arXiv:1703.05175."},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"109547","DOI":"10.1016\/j.knosys.2022.109547","article-title":"Graph-based cognitive diagnosis for intelligent tutoring systems","volume":"253","author":"Su Yu","year":"2022","unstructured":"Yu Su, Zeyu Cheng, Jinze Wu, Yanmin Dong, Zhenya Huang, Le Wu, Enhong Chen, Shijin Wang, and Fei Xie. 2022. Graph-based cognitive diagnosis for intelligent tutoring systems. Knowledge-Based Systems 253 (2022), 109547.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_2_47_2","first-page":"1199","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Sung Flood","year":"2018","unstructured":"Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1199\u20131208."},{"key":"e_1_3_2_48_2","first-page":"3650","article-title":"Optimal subsampling with influence functions","volume":"31","author":"Ting Daniel","year":"2018","unstructured":"Daniel Ting and Eric Brochu. 2018. Optimal subsampling with influence functions. Advances in Neural Information Processing Systems 31, 3650\u20133659.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-85461-8","volume-title":"Elements of Adaptive Testing","author":"Linden Wim J. van der","year":"2010","unstructured":"Wim J. van der Linden and Cees A. W. Glas. 2010. Elements of Adaptive Testing. Springer."},{"key":"e_1_3_2_50_2","first-page":"113","volume-title":"Learning Analytics: Fundaments, Applications, and Trends","author":"Vie Jill-J\u00eann","year":"2017","unstructured":"Jill-J\u00eann Vie, Fabrice Popineau, \u00c9ric Bruillard, and Yolaine Bourda. 2017. A review of recent advances in adaptive assessment. In: Pe\u00f1a-Ayala Alejandro (Ed.), Learning Analytics: Fundaments, Applications, and Trends. Springer, 113\u2013142."},{"key":"e_1_3_2_51_2","first-page":"3630","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals Oriol","year":"2016","unstructured":"Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. 2016. Matching networks for one shot learning. Advances in Neural Information Processing Systems 29 (2016), 3630\u20133638.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","DOI":"10.4324\/9781410605931","volume-title":"Computerized Adaptive Testing: A Primer","author":"Wainer Howard","year":"2000","unstructured":"Howard Wainer, Neil J. Dorans, Ronald Flaugher, Bert F. Green, and Robert J. Mislevy. 2000. Computerized Adaptive Testing: A Primer. Routledge."},{"issue":"3","key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s11336-011-9215-7","article-title":"Item selection in multidimensional computerized adaptive testing\u2014Gaining information from different angles","volume":"76","author":"Wang Chun","year":"2011","unstructured":"Chun Wang and Hua-Hua Chang. 2011. Item selection in multidimensional computerized adaptive testing\u2014Gaining information from different angles. Psychometrika 76, 3 (2011), 363\u2013384.","journal-title":"Psychometrika"},{"issue":"8","key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"8312","DOI":"10.1109\/TKDE.2022.3201037","article-title":"NeuralCD: A general framework for cognitive diagnosis","volume":"35","author":"Wang Fei","year":"2023","unstructured":"Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yu Yin, Shijin Wang, and Yu Su. 2023. NeuralCD: A general framework for cognitive diagnosis. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2023), 8312\u20138327.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3463913","article-title":"Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation","volume":"40","author":"Wang Hao","year":"2021","unstructured":"Hao Wang, Defu Lian, Hanghang Tong, Qi Liu, Zhenya Huang, and Enhong Chen. 2021. Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation. ACM Transactions on Information Systems (TOIS) 40, 2 (2021), 1\u201328.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"3","key":"e_1_3_2_56_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2700496","article-title":"Relevance meets coverage: A unified framework to generate diversified recommendations","volume":"7","author":"Wu Le","year":"2016","unstructured":"Le Wu, Qi Liu, Enhong Chen, Nicholas Jing Yuan, Guangming Guo, and Xing Xie. 2016. Relevance meets coverage: A unified framework to generate diversified recommendations. ACM Transactions on Intelligent Systems and Technology (TIST) 7, 3 (2016), 1\u201330.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_57_2","first-page":"399","volume-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Yang Lin","year":"2017","unstructured":"Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, and Danny Z. Chen. 2017. Suggestive annotation: A deep active learning framework for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 399\u2013407."},{"issue":"1","key":"e_1_3_2_58_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3617510","article-title":"SHGCN: Socially enhanced heterogeneous graph convolutional network for multi-behavior prediction","volume":"18","author":"Zhang Lei","year":"2023","unstructured":"Lei Zhang, Wuji Zhang, Likang Wu, Ming He, and Hongke Zhao. 2023b. SHGCN: Socially enhanced heterogeneous graph convolutional network for multi-behavior prediction. ACM Transactions on the Web 18, 1 (2023), 1\u201327.","journal-title":"ACM Transactions on the Web"},{"key":"e_1_3_2_59_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"31","author":"Zhang Ye","year":"2017","unstructured":"Ye Zhang, Matthew Lease, and Byron Wallace. 2017. Active discriminative text representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31."},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"152106","DOI":"10.1007\/s11432-022-3852-0","article-title":"Understanding and improving fairness in cognitive diagnosis","volume":"67","author":"Zhang Zheng","year":"2023","unstructured":"Zheng Zhang, Le Wu, Qi Liu, Jiayu Liu, Zhenya Huang, Yu Yin, Yan Zhuang, Weibo Gao, and Enhong Chen. 2023a. Understanding and improving fairness in cognitive diagnosis. SCIENCE CHINA Information Sciences Volume 67, 152106 (2024).","journal-title":"SCIENCE CHINA Information Sciences"},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1109\/TKDE.2023.3324912","article-title":"Cross-domain recommendation via progressive structural alignment","volume":"36","author":"Zhao Chuang","year":"2023","unstructured":"Chuang Zhao, Hongke Zhao, Xiaomeng Li, Ming He, Jiahui Wang, and Jianping Fan. 2023. Cross-domain recommendation via progressive structural alignment. IEEE Transactions on Knowledge and Data Engineering 36, 2401\u20132415.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_62_2","volume-title":"Some practical item selection algorithms in cognitive diagnostic computerized adaptive testing\u2013smart diagnosis for smart learning","author":"Zheng Chanjin","year":"2015","unstructured":"Chanjin Zheng. 2015. Some practical item selection algorithms in cognitive diagnostic computerized adaptive testing\u2013smart diagnosis for smart learning. Ph. D. Dissertation. University of Illinois at Urbana-Champaign."},{"key":"e_1_3_2_63_2","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1145\/3477495.3531928","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhuang Yan","year":"2022","unstructured":"Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Binbin Jin, Haoyang Bi, Enhong Chen, and Shijin Wang. 2022. A robust computerized adaptive testing approach in educational question retrieval. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 416\u2013426."},{"key":"e_1_3_2_64_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zou Yingtian","year":"2021","unstructured":"Yingtian Zou, Fusheng Liu, and Qianxiao Li. 2021. Unraveling model-agnostic meta-learning via the adaptation learning rate. In Proceedings of the International Conference on Learning Representations."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3660642","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3660642","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:21Z","timestamp":1750287021000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3660642"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"references-count":63,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3660642"],"URL":"https:\/\/doi.org\/10.1145\/3660642","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"2023-08-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}