{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T23:17:30Z","timestamp":1769728650519,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s11704-019-9062-8","type":"journal-article","created":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T05:16:18Z","timestamp":1579497378000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Multi-task MIML learning for pre-course student performance prediction"],"prefix":"10.1007","volume":"14","author":[{"given":"Yuling","family":"Ma","sequence":"first","affiliation":[]},{"given":"Chaoran","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Gongping","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"issue":"1","key":"9062_CR1","first-page":"22","volume":"8","author":"M Sweeney","year":"2016","unstructured":"Sweeney M, Rangwala H, Lester J, Johri A. Next-term student performance prediction: a recommender systems approach. Journal of Educational Data Mining, 2016, 8(1): 22\u201351","journal-title":"Journal of Educational Data Mining"},{"issue":"2","key":"9062_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1080\/03069889808259704","volume":"26","author":"A Grayson","year":"1998","unstructured":"Grayson A, Miller H, Clarke D D. Identifying barriers to help-seeking: a qualitative analysis of students\u2019 preparedness to seek help from tutors. British Journal of Guidance & Counselling, 1998, 26(2): 237\u2013253","journal-title":"British Journal of Guidance & Counselling"},{"issue":"6","key":"9062_CR3","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TSMCC.2010.2053532","volume":"40","author":"C Romero","year":"2010","unstructured":"Romero C, Ventura S. Educational data mining: a review of the state of the art. IEEE Transactions on Systems Man and Cybernetics, Part C (Application and Reviews), 2010, 40(6): 601\u2013618","journal-title":"IEEE Transactions on Systems Man and Cybernetics, Part C (Application and Reviews)"},{"key":"9062_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.compedu.2018.08.005","volume":"127","author":"L Qiujie","year":"2018","unstructured":"Qiujie L, Rachel B. The different relationships between engagement and outcomes across participant subgroups in massive open online courses. Computers & Education, 2018, 127: 41\u201365","journal-title":"Computers & Education"},{"key":"9062_CR5","unstructured":"Ren Z, Rangwala H, Johri A. Predicting performance on MOOC assessments using multi-regression models. In: Proceedings of the 9th International Conference on Education Data Mining. 2016, 484\u2013489"},{"key":"9062_CR6","doi-asserted-by":"crossref","unstructured":"Trivedi S, Pardos Z A, Heffernan N T. Clustering students to generate an ensemble to improve standard test score predictions. In: Proceedings of International Conference on Artificial Intelligence in Education. 2011, 377\u2013384","DOI":"10.1007\/978-3-642-21869-9_49"},{"issue":"4","key":"9062_CR7","doi-asserted-by":"publisher","first-page":"476","DOI":"10.7763\/IJMLC.2012.V2.171","volume":"2","author":"E Er","year":"2012","unstructured":"Er E. Identifying at-risk students using machine learning techniques: a case study with is 100. International Journal of Machine Learning and Computing, 2012, 2(4): 476\u2013480","journal-title":"International Journal of Machine Learning and Computing"},{"key":"9062_CR8","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.chb.2014.04.002","volume":"36","author":"Y H Hu","year":"2014","unstructured":"Hu Y H, Lo C L, Shih S P. Developing early warning systems to predict students online learning performance. Computers in Human Behavior, 2014, 36: 469\u2013478","journal-title":"Computers in Human Behavior"},{"issue":"2","key":"9062_CR9","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.compedu.2009.09.008","volume":"54","author":"L P Macfadyen","year":"2010","unstructured":"Macfadyen L P, Dawson S. Mining LMS data to develop an early warning system for educators: a proof of concept. Computers & Education, 2010, 54(2): 588\u2013599","journal-title":"Computers & Education"},{"issue":"12","key":"9062_CR10","doi-asserted-by":"publisher","first-page":"15020","DOI":"10.1016\/j.eswa.2011.05.044","volume":"38","author":"A Zafra","year":"2011","unstructured":"Zafra A, Romero C, Ventura S. Multiple instance learning for classifying students in learning management systems. Expert Systems with Applications, 2011, 38(12): 15020\u201315031","journal-title":"Expert Systems with Applications"},{"issue":"5","key":"9062_CR11","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1080\/08839510490442058","volume":"18","author":"S B Kotsiantis","year":"2004","unstructured":"Kotsiantis S B, Pierrakeas C J, Pintelas P E. Preventing student dropout in distance learning using machine learning techniques. Applied Artificial Intelligence, 2004, 18(5): 411\u2013426","journal-title":"Applied Artificial Intelligence"},{"issue":"4","key":"9062_CR12","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.compedu.2003.09.005","volume":"43","author":"M Xenos","year":"2004","unstructured":"Xenos M. Prediction and assessment of student behaviour in open and distance education in computers using bayesian networks. Computers & Education, 2004, 43(4): 345\u2013359","journal-title":"Computers & Education"},{"key":"9062_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compedu.2016.09.005","volume":"103","author":"F Marbouti","year":"2016","unstructured":"Marbouti F, Diefes-Dux H A, Madhavan K. Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 2016, 103: 1\u201315","journal-title":"Computers & Education"},{"issue":"4","key":"9062_CR14","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1109\/TSP.2015.2496278","volume":"64","author":"Y Meier","year":"2016","unstructured":"Meier Y, Xu J, Atan O, Schaar M V D. Predicting grades. IEEE Transactions on Signal Processing, 2016, 64(4): 959\u2013972","journal-title":"IEEE Transactions on Signal Processing"},{"key":"9062_CR15","unstructured":"Gedeon T D, Turner S. Explaining student grades predicted by a neural network. In: Proceedings of International Joint Conference on Neural Networks. 2002, 609\u2013612"},{"issue":"1","key":"9062_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.5120\/18717-9939","volume":"107","author":"A Acharya","year":"2014","unstructured":"Acharya A, Sinha D. Early prediction of students performance using machine learning techniques. International Journal of Computer Applications, 2014, 107(1): 37\u201343","journal-title":"International Journal of Computer Applications"},{"issue":"2","key":"9062_CR17","first-page":"200","volume":"62","author":"Y L Ma","year":"2019","unstructured":"Ma Y L, Cui C R, Nie X S, Yang G P, Shaheed K, Yin Y L. Pre-course student performance prediction with multi-instance multi-label learning. Science China Information Sciences, 2019, 62(2): 200\u2013205","journal-title":"Science China Information Sciences"},{"key":"9062_CR18","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding Machine Learning","author":"S Shalevshwartz","year":"2014","unstructured":"Shalevshwartz S, Bendavid S. Understanding Machine Learning. 1st ed. New York: Cambridge University Press, 2014","edition":"1st ed."},{"key":"9062_CR19","doi-asserted-by":"crossref","unstructured":"Zhou Z H, Zhang M L. Multi-instance multi-label learning with application to scene classification. In: Proceedings of the 19th International Conference on Neural Information Processing Systems. 2006, 1609\u20131616","DOI":"10.7551\/mitpress\/7503.003.0206"},{"key":"9062_CR20","unstructured":"Zhang Y, Yang Q. A survey on multi-task learning. 2017, arXiv preprint arXiv:1707.08114"},{"issue":"3","key":"9062_CR21","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1207\/S15328023TOP2803_09","volume":"28","author":"A Y Wang","year":"2001","unstructured":"Wang A Y, Newlin M H, Tucker T L. A discourse analysis of online classroom chats: predictors of cyber-student performance. Teaching of Psychology, 2001, 28(3): 222\u2013226","journal-title":"Teaching of Psychology"},{"issue":"10","key":"9062_CR22","first-page":"21","volume":"29","author":"A Y Wang","year":"2002","unstructured":"Wang A Y, Newlin M H. Predictors of performance in the virtual classroom: identifying and helping at-risk cyber-students. Journal of Higher Education Academic Matters, 2002, 29(10): 21\u201325","journal-title":"Journal of Higher Education Academic Matters"},{"key":"9062_CR23","doi-asserted-by":"crossref","unstructured":"Essa A, Ayad H. Student success system: risk analytics and data visualization using ensembles of predictive models. In: Proceedings of International Conference on Learning Analytics and Knowledge. 2012, 158\u2013161","DOI":"10.1145\/2330601.2330641"},{"key":"9062_CR24","unstructured":"Lopez M I, Luna J M, Romero C, Ventura S. Classification via clustering for predicting final marks based on student participation in forums. In: Proceedings of International Conference on Educational Data Mining. 2012, 148\u2013151"},{"key":"9062_CR25","doi-asserted-by":"crossref","unstructured":"Zhang M L, Zhou Z H. M3MIML: a maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th International Conference on Data Mining. 2008, 688\u2013697","DOI":"10.1109\/ICDM.2008.27"},{"key":"9062_CR26","doi-asserted-by":"crossref","unstructured":"Zhang M L. A k-nearest neighbor based multi-instance multi-label learning algorithm. In: Proceedings of the 22nd International Conference on Tools with Artificial Intelligence. 2010, 207\u2013212","DOI":"10.1109\/ICTAI.2010.102"},{"key":"9062_CR27","doi-asserted-by":"crossref","unstructured":"Xu X S, Xue X, Zhou Z H. Ensemble multi-instance multi-label learning approach for video annotation task. In: Proceedings of the 19th International Conference on Multimedea. 2011, 1153\u20131156","DOI":"10.1145\/2072298.2071962"},{"key":"9062_CR28","doi-asserted-by":"crossref","unstructured":"Li Y F, Hu J H, Jiang Y, Zhou Z H. Towards discovering what patterns trigger what labels. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 2012, 1012\u20131018","DOI":"10.1609\/aaai.v26i1.8285"},{"key":"9062_CR29","doi-asserted-by":"crossref","unstructured":"Huang S J, Zhou Z H. Fast multi-instance multi-label learning. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 1868\u20131874","DOI":"10.1609\/aaai.v28i1.8970"},{"key":"9062_CR30","doi-asserted-by":"crossref","unstructured":"Feng J, Zhou Z H. Deep MIML network. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017, 158\u2013161","DOI":"10.1609\/aaai.v31i1.10890"},{"key":"9062_CR31","doi-asserted-by":"crossref","unstructured":"Yang Y, Wu Y F, Zhan D C, Liu Z B, Jiang Y. Complex object classification: a multi-modal multi-instance multi-label deep network with optimal transport. In: Proceedings of the 24th ACM International Conference on Knowledge Discovery and Data Mining. 2018, 2594\u20132603","DOI":"10.1145\/3219819.3220012"},{"issue":"2","key":"9062_CR32","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10115-006-0029-3","volume":"11","author":"Z H Zhou","year":"2007","unstructured":"Zhou Z H, Zhang M L. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge & Information Systems, 2007, 11(2): 155\u2013170","journal-title":"Knowledge & Information Systems"},{"issue":"9","key":"9062_CR33","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"M R Boutell","year":"2004","unstructured":"Boutell M R, Luo J, Shen X, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757\u20131771","journal-title":"Pattern Recognition"},{"key":"9062_CR34","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"Z H Zhou","year":"2012","unstructured":"Zhou Z H. Ensemble Methods: Foundations and Algorithms. 1st ed. Florida: CRC Press, 2012","edition":"1st ed."},{"key":"9062_CR35","first-page":"2811","volume":"26","author":"S B Wang","year":"2015","unstructured":"Wang S B, Li Y F. Classifier circle method for multi-label learning. Journal of Software, 2015, 26: 2811\u20132819","journal-title":"Journal of Software"},{"key":"9062_CR36","volume-title":"Machine Learning","author":"Z H Zhou","year":"2016","unstructured":"Zhou Z H. Machine Learning. 1st ed. Beijing: Tsinghua University Press, 2016","edition":"1st ed."}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-019-9062-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-019-9062-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-019-9062-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T09:55:30Z","timestamp":1695635730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-019-9062-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,20]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["9062"],"URL":"https:\/\/doi.org\/10.1007\/s11704-019-9062-8","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,20]]},"assertion":[{"value":"19 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"145313"}}