{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:34:18Z","timestamp":1742992458249,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031109850"},{"type":"electronic","value":"9783031109867"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10986-7_24","type":"book-chapter","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T22:30:36Z","timestamp":1658183436000},"page":"300-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structural and\u00a0Temporal Learning for\u00a0Dropout Prediction in\u00a0MOOCs"],"prefix":"10.1007","author":[{"given":"Tianxing","family":"Han","sequence":"first","affiliation":[]},{"given":"Pengyi","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"104042","DOI":"10.1016\/j.compedu.2020.104042","volume":"160","author":"S Blum-Smith","year":"2021","unstructured":"Blum-Smith, S., Yurkofsky, M.M., et al.: Stepping back and stepping in: facilitating learner-centered experiences in MOOCs. Comput. Educ. 160, 104042 (2021)","journal-title":"Comput. Educ."},{"key":"24_CR2","first-page":"1","volume":"2019","author":"J Chen","year":"2019","unstructured":"Chen, J., Feng, J., Sun, X., Wu, N., Yang, Z., Chen, S.: MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Math. Prob. Eng. 2019, 1\u201311 (2019)","journal-title":"Math. Prob. Eng."},{"key":"24_CR3","doi-asserted-by":"publisher","unstructured":"Fan, H., Zhang, F., et al.: Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3059313","DOI":"10.1109\/TPAMI.2021.3059313"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Fan, S., Zhu, J., et al.: Metapath-guided heterogeneous graph neural network for intent recommendation. In: KDD, pp. 2478\u20132486 (2019)","DOI":"10.1145\/3292500.3330673"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Feng, W., Tang, J., et al.: Understanding dropouts in MOOCs. In: Proceedings of the AAAI, vol. 33, pp. 517\u2013524 (2019)","DOI":"10.1609\/aaai.v33i01.3301517"},{"key":"24_CR6","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, pp. 249\u2013256 (2010)"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Gong, J., Wang, S., et al.: Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view. In: ACM SIGIR, pp. 79\u201388 (2020)","DOI":"10.1145\/3397271.3401057"},{"issue":"5\u20136","key":"24_CR8","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5\u20136), 602\u2013610 (2005)","journal-title":"Neural Netw."},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"He, J., Bailey, J., et al.: Identifying at-risk students in massive open online courses. In: Proceedings of the AAAI, vol. 29 (2015)","DOI":"10.1609\/aaai.v29i1.9471"},{"issue":"14","key":"24_CR10","doi-asserted-by":"publisher","first-page":"8971","DOI":"10.1007\/s00500-021-05795-1","volume":"25","author":"C Jin","year":"2021","unstructured":"Jin, C.: Dropout prediction model in MOOC based on clickstream data and student sample weight. Soft. Comput. 25(14), 8971\u20138988 (2021)","journal-title":"Soft. Comput."},{"key":"24_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"103728","DOI":"10.1016\/j.compedu.2019.103728","volume":"145","author":"PM Moreno-Marcos","year":"2020","unstructured":"Moreno-Marcos, P.M., Munoz-Merino, P.J., et al.: Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Comput. Educ. 145, 103728 (2020)","journal-title":"Comput. Educ."},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Nitta, I., Ishizaki, R., et al.: Graph-based massive open online course (MOOC) dropout prediction using clickstream data in virtual learning environment. In: ICCSE, pp. 48\u201352 (2021)","DOI":"10.1109\/ICCSE51940.2021.9569582"},{"issue":"10","key":"24_CR14","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.1109\/TKDE.2013.2297920","volume":"26","author":"C Shi","year":"2014","unstructured":"Shi, C., Kong, X., et al.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479\u20132492 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR15","unstructured":"Vaswani, A., Shazeer, N., et al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, H., et al.: Heterogeneous graph attention network. In: World Wide Web, pp. 2022\u20132032 (2019)","DOI":"10.1145\/3308558.3313562"},{"key":"24_CR17","unstructured":"Xu, K., Ba, J., et al.: Show, attend and tell: neural image caption generation with visual attention. In: ICML, pp. 2048\u20132057 (2015)"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Yu, J., Luo, G., et al.: MOOCCube: a large-scale data repository for NLP applications in MOOCs. In: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.285"},{"key":"24_CR19","doi-asserted-by":"publisher","first-page":"104189","DOI":"10.1016\/j.compedu.2021.104189","volume":"167","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Gao, M., Zhang, J.: The learning behaviours of dropouts in MOOCs: a collective attention network perspective. Comput. Educ. 167, 104189 (2021)","journal-title":"Comput. Educ."},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, X., et al.: Heterogeneous graph structure learning for graph neural networks. In: Proceedings of the AAAI (2021)","DOI":"10.1609\/aaai.v35i5.16600"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10986-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T22:36:45Z","timestamp":1676155005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10986-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031109850","9783031109867"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10986-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem22.smart-conf.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"498","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":"169","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":"34% - 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":"10","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)"}}]}}