{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:26:45Z","timestamp":1743060405550,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030342227"},{"type":"electronic","value":"9783030342234"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-34223-4_9","type":"book-chapter","created":{"date-parts":[[2019,11,14]],"date-time":"2019-11-14T00:14:40Z","timestamp":1573690480000},"page":"133-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Dual Path Convolutional Neural Network for Student Performance Prediction"],"prefix":"10.1007","author":[{"given":"Yuling","family":"Ma","sequence":"first","affiliation":[]},{"given":"Jian","family":"Zong","sequence":"additional","affiliation":[]},{"given":"Chaoran","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Chunyun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qizheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"issue":"1","key":"9_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TLT.2016.2616312","volume":"10","author":"C Rianne","year":"2017","unstructured":"Rianne, C., Chris, S., Ad, K., Uwe, M.: Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS. IEEE Trans. Learn. Technol. 10(1), 17\u201329 (2017)","journal-title":"IEEE Trans. Learn. Technol."},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.compedu.2012.08.015","volume":"61","author":"S Huang","year":"2013","unstructured":"Huang, S., Fang, N.: Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Comput. Educ. 61, 133\u2013145 (2013)","journal-title":"Comput. Educ."},{"issue":"4","key":"9_CR3","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 Trans. Signal Proces. 64(4), 959\u2013972 (2016)","journal-title":"IEEE Trans. Signal Proces."},{"issue":"6","key":"9_CR4","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 Trans. Syst. Man. Cybern. C 40(6), 601\u2013618 (2010)","journal-title":"IEEE Trans. Syst. Man. Cybern. C"},{"key":"9_CR5","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. Comput. Educ. 127, 41\u201365 (2018)","journal-title":"Comput. Educ."},{"key":"9_CR6","unstructured":"Ren Z., Rangwala H., Johri A.: Predicting performance on MOOC assessments using multi-regression models. arXiv preprint arXiv:1605.02269 (2016)"},{"issue":"2","key":"9_CR7","first-page":"200","volume":"62","author":"YL Ma","year":"2019","unstructured":"Ma, Y.L., Cui, C.R., Nie, X.S., et al.: Pre-course student performance prediction with multi-instance multi-label learning. Sci. China Inf. Sci. 62(2), 200\u2013205 (2019)","journal-title":"Sci. China Inf. Sci."},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Cao, Y., Gao, J., Lian, D., et al.: Orderliness predicts academic performance: behavioural analysis on campus lifestyle. J. Roy. Soc. Interface 15(146) (2018)","DOI":"10.1098\/rsif.2018.0210"},{"issue":"3","key":"9_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3299087","volume":"10","author":"H Yao","year":"2019","unstructured":"Yao, H., Lian, D., Cao, Y., et al.: Predicting academic performance for college students: a campus behavior perspective. ACM Trans. Intel. Syst. Tec. 10(3), 1\u201321 (2019)","journal-title":"ACM Trans. Intel. Syst. Tec."},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Razavian, A.S., Azizpour, H., Sullivan, J., et al.: CNN features off-the-shelf: an astounding baseline for recognition. arXiv preprint arXiv:1403.6382 (2014)","DOI":"10.1109\/CVPRW.2014.131"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Zhang J., Zheng Y., Qi D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: 31st AAAI Proceedings on Artificial Intelligence, pp. 1655\u20131661. AAAI, San Francisco (2017)","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"9_CR12","unstructured":"Zhang Y., Yang Q.: A survey on multi-task learning. arXiv preprint arXiv:1707.08114 (2017)"},{"key":"9_CR13","unstructured":"Wang, F., Chen, L.: A nonlinear state space model for identifying at-risk students in open online courses. In: 9th International Proceedings on Educational Data Mining, Raleigh, NC, USA, pp. 527\u2013532 (2016)"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Li, W., Gao, M., Li, H., Xiong, Q.Y., et al.: Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning. In: International Proceedings on Neural Networks, pp. 3130\u20133137. IEEE, Vancouver (2016)","DOI":"10.1109\/IJCNN.2016.7727598"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"He, J.Z., Bailey, J., Rubinstein, B., Zhang, R.: Identifying at-risk students in massive open online courses. In: 29th AAAI Proceedings on Artificial Intelligence, pp. 1749\u20131755. AAAI, Austin (2015)","DOI":"10.1609\/aaai.v29i1.9471"},{"key":"9_CR16","unstructured":"Mi, F., Dit-Yan, Y.: Temporal models for predicting student dropout in massive open online courses. In: 2015 IEEE International Proceedings on Data Mining Workshop, pp. 256\u2013263. IEEE, Atlantic City (2015)"},{"key":"9_CR17","unstructured":"Kim, B.H., Vizitei, E., Ganapathi, V.: GritNet: student performance prediction with deep learning. In: 11st International Proceedings on Educational Data Mining, Buffalo, NY, USA, pp. 625\u2013629 (2018)"},{"key":"9_CR18","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: International Conference on Artificial Intelligence in Education, Christchurch, New Zealand, pp. 377\u2013384 (2011)","DOI":"10.1007\/978-3-642-21869-9_49"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Thai-Nghe, N., Schmidt-Thieme, L.: Multi-relational factorization models for student modeling in intelligent tutoring systems. In: 7th International Conference on Knowledge and Systems Engineering. IEEE, Chongqing (2015)","DOI":"10.1109\/KSE.2015.9"},{"key":"9_CR20","unstructured":"Suleyman, C., Luo, S., Yan, P.X., Ron, T.: Probabilistic latent class models for predicting student performance. In: International Conference on Information and Knowledge Management, pp. 1513\u20131516. ACM, San Francisco (2013)"},{"issue":"4","key":"9_CR21","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. Int. J. Mach. Learn. Comput. 2(4), 476\u2013480 (2012)","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.chb.2014.04.002","volume":"36","author":"YH Hu","year":"2014","unstructured":"Hu, Y.H., Lo, C.L., Shih, S.P.: Developing early warning systems to predict students online learning performance. Comput. Hum. Behav. 36, 469\u2013478 (2014)","journal-title":"Comput. Hum. Behav."},{"issue":"2","key":"9_CR23","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.compedu.2009.09.008","volume":"54","author":"LP Macfadyen","year":"2010","unstructured":"Macfadyen, L.P., Dawson, S.: Mining lms data to develop an early warning system for educators: a proof of concept. Comput. Educ. 54(2), 588\u2013599 (2010)","journal-title":"Comput. Educ."},{"issue":"12","key":"9_CR24","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 Syst. Appl. 38(12), 15020\u201315031 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"9_CR25","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1080\/08839510490442058","volume":"18","author":"SB Kotsiantis","year":"2004","unstructured":"Kotsiantis, S.B., Pierrakeas, C.J., Pintelas, P.E.: Preventing student dropout in distance learning using machine learning techniques. Appl. Artif. Intell. 18(5), 411\u2013426 (2004)","journal-title":"Appl. Artif. Intell."},{"issue":"4","key":"9_CR26","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. Comput. Educ. 43(4), 345\u2013359 (2004)","journal-title":"Comput. Educ."},{"issue":"3","key":"9_CR27","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1207\/S15328023TOP2803_09","volume":"28","author":"AY 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. Teach. Psychol. 28(3), 222\u2013226 (2001)","journal-title":"Teach. Psychol."},{"issue":"10","key":"9_CR28","first-page":"21","volume":"29","author":"AY Wang","year":"2002","unstructured":"Wang, A.Y., Newlin, M.H.: Predictors of performance in the virtual classroom: identifying and helping at-risk cyber-students. J. High Educ. 29(10), 21\u201325 (2002)","journal-title":"J. High Educ."},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Essa, A., Ayad, H.: Student success system: risk analytics and data visualization using ensembles of predictive models. In: 2nd Proceedings on Learning Analytics and Knowledge, Vancouver BC, Canada, pp. 158\u2013161 (2012)","DOI":"10.1145\/2330601.2330641"},{"key":"9_CR30","unstructured":"Lopez, M.I., Luna, J.M., Romero, C., Ventura, S.: Classification via clustering for predicting final marks based on student participation in forums. JEDM 4 (2012)"},{"key":"9_CR31","unstructured":"Wu, R.Z., Liu, Q., Liu, Y.P., et al.: Cognitive modelling for predicting examinee performance. In: 24th Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1017\u20131024. AAAI Press, Buenos Aires (2015)"},{"key":"9_CR32","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. Comput. Educ. 103, 1\u201315 (2016)","journal-title":"Comput. Educ."},{"key":"9_CR33","unstructured":"Kimberly, E.A., Matthew, D.P.: Course signals at purdue: using learning analytics to increase student success. In: 2nd Proceedings on Learning Analytics and Knowledge, pp. 267\u2013270. ACM, Vancouver (2012)"},{"key":"9_CR34","unstructured":"Ashay, T., Shajith, I., Bikram, S., et al.: Predicting student risks through longitudinal analysis. In: 20th Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 1544\u20131552. ACM, New York (2014)"},{"key":"9_CR35","unstructured":"Gedeon, T.D., Turner, S.: Explaining student grades predicted by a neural network. In: Proceedings of International Joint Conference on Neural Networks, Nagoya, pp. 609\u2013612 (2002)"},{"issue":"1","key":"9_CR36","first-page":"37","volume":"107","author":"A Acharya","year":"2014","unstructured":"Acharya, A., Sinha, D.: Early prediction of students performance using machine learning techniques. Int. J. Comput. Appl. 107(1), 37\u201343 (2014)","journal-title":"Int. J. Comput. Appl."},{"issue":"8","key":"9_CR37","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal."},{"key":"9_CR38","unstructured":"Ruder S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098v1 (2017)"},{"issue":"3\/4","key":"9_CR39","doi-asserted-by":"publisher","first-page":"441","DOI":"10.2307\/1422689","volume":"100","author":"C Spearman","year":"1987","unstructured":"Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 100(3\/4), 441\u2013471 (1987)","journal-title":"Am. J. Psychol."}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34223-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:01:36Z","timestamp":1710349296000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-34223-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030342227","9783030342234"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34223-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"29 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 January 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 January 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2019.comp.polyu.edu.hk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"WISE 2019 has been postponed until January 2020 because of the problems in Hong Kong. For CCIS volume, Submissions: 30. Full papers accepted: 10. Short papers accepted: 5.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}