{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:43:33Z","timestamp":1760222613379,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030606350"},{"type":"electronic","value":"9783030606367"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-60636-7_11","type":"book-chapter","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T10:08:52Z","timestamp":1602497332000},"page":"125-136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Student Performance Prediction Based on Multi-view Network Embedding"],"prefix":"10.1007","author":[{"given":"Jianian","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanwei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Al-Shehri, H., et al.: Student performance prediction using support vector machine and k-nearest neighbor. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1\u20134. IEEE (2017)","DOI":"10.1109\/CCECE.2017.7946847"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., Xu, Q.: Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891\u2013900 (2015)","DOI":"10.1145\/2806416.2806512"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Carter, A.S., Hundhausen, C.D., Adesope, O.: The normalized programming state model: Predicting student performance in computing courses based on programming behavior. In: Proceedings of the eleventh annual International Conference on International Computing Education Research, pp. 141\u2013150 (2015)","DOI":"10.1145\/2787622.2787710"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Cetintas, S., Si, L., Xin, Y.P., Tzur, R.: Probabilistic latent class models for predicting student performance. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 1513\u20131516 (2013)","DOI":"10.1145\/2505515.2507832"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chen, H., Sultan, S.F., Tian, Y., Chen, M., Skiena, S.: Fast and accurate network embeddings via very sparse random projection. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 399\u2013408 (2019)","DOI":"10.1145\/3357384.3357879"},{"issue":"1","key":"11_CR7","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TLT.2016.2616312","volume":"10","author":"R Conijn","year":"2017","unstructured":"Conijn, R., Snijders, C., Kleingeld, A., Matzat, U.: 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."},{"issue":"5","key":"11_CR8","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833\u2013852 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"11_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/MC.2016.119","volume":"49","author":"A Elbadrawy","year":"2016","unstructured":"Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G., Rangwala, H.: Predicting student performance using personalized analytics. Computer 49(4), 61\u201369 (2016)","journal-title":"Computer"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Elbadrawy, A., Studham, R.S., Karypis, G.: Collaborative multi-regression models for predicting students\u2019 performance in course activities. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 103\u2013107 (2015)","DOI":"10.1145\/2723576.2723590"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864. ACM (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"11_CR12","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":"1","key":"11_CR13","first-page":"61","volume":"13","author":"D Kabakchieva","year":"2013","unstructured":"Kabakchieva, D.: Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13(1), 61\u201372 (2013)","journal-title":"Cybern. Inf. Technol."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Mayilvaganan, M., Kalpanadevi, D.: Comparison of classification techniques for predicting the performance of students academic environment. In: 2014 International Conference on Communication and Network Technologies, pp. 113\u2013118. IEEE (2014)","DOI":"10.1109\/CNT.2014.7062736"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: Netsmf: large-scale network embedding as sparse matrix factorization. In: The World Wide Web Conference, pp. 1509\u20131520 (2019)","DOI":"10.1145\/3308558.3313446"},{"key":"11_CR17","unstructured":"Quadri, M.M., Kalyankar, N.: Drop out feature of student data for academic performance using decision tree techniques. Glob. J. Comput. Sci. Technol. (2010)"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Thai-Nghe, N., Drumond, L., Horv\u00e1th, T., Schmidt-Thieme, L., et al.: Multi-relational factorization models for predicting student performance. In: KDD Workshop on Knowledge Discovery in Educational Data (KDDinED), pp. 27\u201340 (2011)","DOI":"10.4018\/978-1-61350-489-5.ch006"},{"issue":"2","key":"11_CR19","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1016\/j.procs.2010.08.006","volume":"1","author":"N Thai-Nghe","year":"2010","unstructured":"Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811\u20132819 (2010)","journal-title":"Procedia Comput. Sci."},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225\u20131234 (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Wang, T., Mitrovic, A.: Using neural networks to predict student\u2019s performance. In: Proceedings of International Conference on Computers in Education, pp. 969\u2013973. IEEE (2002)","DOI":"10.1109\/CIE.2002.1186127"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Yao, H., Lian, D., Cao, Y., Wu, Y., Zhou, T.: Predicting academic performance for college students: a campus behavior perspective. arXiv preprint arXiv:1903.06726 (2019)","DOI":"10.1145\/3299087"},{"key":"11_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-55699-4_37","volume-title":"Database Systems for Advanced Applications","author":"H Yao","year":"2017","unstructured":"Yao, H., Nie, M., Su, H., Xia, H., Lian, D.: Predicting academic performance via semi-supervised learning with constructed campus social network. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 597\u2013609. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-55699-4_37"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Yu, Y., Wang, H., Li, Z.: Inferring mobility relationship via graph embedding. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 3, pp. 1\u201321 (2018)","DOI":"10.1145\/3264957"},{"key":"11_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/978-3-319-91458-9_32","volume-title":"Database Systems for Advanced Applications","author":"Y Yu","year":"2018","unstructured":"Yu, Y., Yao, H., Wang, H., Tang, X., Li, Z.: Representation learning for large-scale dynamic networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10828, pp. 526\u2013541. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91458-9_32"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cui, P., Li, H., Wang, X., Zhu, W.: Billion-scale network embedding with iterative random projection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 787\u2013796. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00094"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60636-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:02:41Z","timestamp":1760220161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60636-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030606350","9783030606367"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60636-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"16 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/index_en.html","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":"Microsoft CMT system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"402","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":"158","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":"39% - 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":"4","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)"}}]}}