{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:47:21Z","timestamp":1773118041724,"version":"3.50.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"University of Macau Start-up Research","award":["SRG2021-00023-FED"],"award-info":[{"award-number":["SRG2021-00023-FED"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Learning Technol."],"published-print":{"date-parts":[[2023,4,1]]},"DOI":"10.1109\/tlt.2022.3224075","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T16:32:55Z","timestamp":1669307575000},"page":"243-255","source":"Crossref","is-referenced-by-count":7,"title":["Learning Outcome Modeling in Computer-Based Assessments for Learning: A Sequential Deep Collaborative Filtering Approach"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9073-5267","authenticated-orcid":false,"given":"Fu","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Education, University of Macau, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-0378","authenticated-orcid":false,"given":"Chang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Education, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Educational Psychology, University of Alberta, Edmonton, AB, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhu","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Educational Psychology, University of Alberta, Edmonton, AB, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2021.100270"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/bjet.12595"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1111\/jcal.12172"},{"issue":"1","key":"ref4","first-page":"3","article-title":"The state of educational data mining in 2009: A review and future visions","volume":"1","author":"Baker","year":"2009","journal-title":"J. Educ. Data Mining"},{"key":"ref5","first-page":"95","article-title":"Model-based collaborative filtering analysis of student response data: Machine-learning item response theory","author":"Bergner","year":"2012","journal-title":"Int. Educ. Data Mining Soc"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358070"},{"issue":"57","key":"ref7","first-page":"1959","article-title":"Sparse factor analysis for learning and content analytics","volume":"15","author":"Lan","year":"2014","journal-title":"J. Mach. Learn. Res"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9654-y"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2877208"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/00131857.2017.1421941"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"ref13","article-title":"Deep content-based music recommendation","volume-title":"Proc. Neural Inf. Process. Syst. Conf.","volume":"26","author":"Oord","year":"2013"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783273"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2012.02.007"},{"issue":"2","key":"ref17","first-page":"180","article-title":"Learning theories: Constructivism","volume":"90","author":"Clark","year":"2018","journal-title":"Radiol. Technol."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1177\/0146621613488436"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1177\/0146621612456591"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93843-1_4"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2207243.2207248"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-39112-5_45"},{"key":"ref23","first-page":"1386","article-title":"Automatic discovery of cognitive skills to improve the prediction of student learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lindsey","year":"2014"},{"key":"ref24","article-title":"Alternating recursive method for Q-matrix learning","volume-title":"Proc. Educ. Data Mining","author":"Sun","year":"2014"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2014.12.020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/BF01099821"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-011-9106-8"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2016.05.047"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9589.001.0001"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1080\/10627197.2013.814517"},{"issue":"1","key":"ref31","first-page":"1","article-title":"Evidence-centered design for diagnostic assessment within digital learning environments: Integrating modern psychometrics and educational data mining","volume":"4","author":"Rupp","year":"2012","journal-title":"J. Educ. Data Mining"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-6091-824-7_12"},{"issue":"1","key":"ref33","first-page":"80","article-title":"Analyzing student process data in game-based assessments with Bayesian knowledge tracing and dynamic Bayesian networks","volume":"11","author":"Cui","year":"2019","journal-title":"J. Educ. Data Mining"},{"key":"ref34","article-title":"Dynamic Bayesian network modeling of game based diagnostic assessments. CRESST report 837","volume-title":"Nat. Center Res. Eval., Standards, Student Testing","author":"Levy","year":"2014"},{"key":"ref35","article-title":"Automated assessment of complex task performance in games and simulations","volume-title":"Proc. Interservice\/Ind. Training, Simul. Educ. Conf.","author":"Iseli","year":"2010"},{"key":"ref36","article-title":"A conceptual framework for assessing performance in games and simulations. CRESST report 771","author":"Koenig","year":"2010","journal-title":"Nat. Center Res. Eval., Standards, Student Testing"},{"key":"ref37","article-title":"Deep knowledge tracing","author":"Piech","year":"2015"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11864"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462164"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806527"},{"key":"ref41","article-title":"Going deeper with deep knowledge tracing","author":"Xiong","year":"2016","journal-title":"Proc. Int. Educ. Data Mining Soc"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3051457.3053985"},{"key":"ref43","article-title":"Deep-IRT: Make deep learning based knowledge tracing explainable using item response theory","author":"Yeung","year":"2019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3231644.3231647"},{"issue":"2","key":"ref45","first-page":"1","article-title":"dAFM: Fusing psychometric and connectionist modeling for Q-matrix refinement","volume":"10","author":"Pardos","year":"2018","journal-title":"J. Educ. Data Mining"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-61425-0_4"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2705581"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24258-3_5"},{"key":"ref49","article-title":"Machine beats experts: Automatic discovery of skill models for data-driven online course refinement","volume-title":"Proc. Int. Educ. Data Mining Soc","author":"Matsuda","year":"2015"},{"key":"ref50","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Proc. Adv. Neural Inf. Process. Syst"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref52","article-title":"Layer normalization","author":"Ba","year":"2016"},{"key":"ref53","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref54","volume-title":"Probabilistic Models for Some Intell. and Attainment Tests 1960","author":"Rasch","year":"1980"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1201\/b10274-6"},{"issue":"1","key":"ref56","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"ref57","first-page":"66","article-title":"LOGCF: Deep collaborative filtering with process data for enhanced learning outcome modeling","volume":"12","author":"Chen","year":"2020","journal-title":"J. Educ. Data Mining"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3390\/app10144926"},{"key":"ref59","article-title":"Empirical evaluation of deep learning models for knowledge tracing: Of hyperparameters and metrics on performance and replicability","author":"Sarsa","year":"2021"},{"key":"ref60","volume-title":"Python 3 Reference Manual","author":"Rossum","year":"2009"},{"issue":"8","key":"ref61","first-page":"T1","article-title":"Keras: Deep learning library for Theano and TensorFlow","volume":"7","author":"Chollet","year":"2015"},{"issue":"3","key":"ref62","first-page":"31","article-title":"When is deep learning the best approach to knowledge tracing","volume":"12","author":"Gervet","year":"2020","journal-title":"J. Educ. Data Mining"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44886-1_25"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.3354\/cr030079"},{"key":"ref65","article-title":"A self-attentive model for knowledge tracing","author":"Pandey","year":"2019"}],"container-title":["IEEE Transactions on Learning Technologies"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4620076\/10104192\/09961941.pdf?arnumber=9961941","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:58:56Z","timestamp":1706756336000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9961941\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,1]]},"references-count":65,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tlt.2022.3224075","relation":{},"ISSN":["1939-1382","2372-0050"],"issn-type":[{"value":"1939-1382","type":"electronic"},{"value":"2372-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,1]]}}}