{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:32:38Z","timestamp":1743100358611,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031362712"},{"type":"electronic","value":"9783031362729"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36272-9_35","type":"book-chapter","created":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T23:03:19Z","timestamp":1687734199000},"page":"426-437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contrastive Learning for\u00a0Reading Behavior Embedding in\u00a0E-book System"],"prefix":"10.1007","author":[{"given":"Tsubasa","family":"Minematsu","sequence":"first","affiliation":[]},{"given":"Yuta","family":"Taniguchi","sequence":"additional","affiliation":[]},{"given":"Atsushi","family":"Shimada","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"35_CR1","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 1597\u20131607 (2020)"},{"key":"35_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-030-52240-7_13","volume-title":"Artificial Intelligence in Education","author":"Y Choi","year":"2020","unstructured":"Choi, Y., et al.: EdNet: a large-scale hierarchical dataset in education. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Mill\u00e1n, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 69\u201373. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52240-7_13"},{"key":"35_CR3","unstructured":"Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 59\u201374. Prague (2004)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Ding, M., Yang, K., Yeung, D.Y., Pong, T.C.: Effective feature learning with unsupervised learning for improving the predictive models in massive open online courses. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 135\u2013144 (2019)","DOI":"10.1145\/3303772.3303795"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: Simcse: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894\u20136910 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"35_CR6","unstructured":"Ke, G., He, D., Liu, T.Y.: Rethinking positional encoding in language pre-training. In: International Conference on Learning Representations (2020)"},{"key":"35_CR7","unstructured":"Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186 (2019)"},{"key":"35_CR8","unstructured":"Lopez Zapata, E., Minematsu, T., Taniguchi, Y., Okubo, F., Shimada, A.: Encoding students reading characteristics to improve low academic performance predictive models. In: Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22), pp. 36\u201338, March 2022"},{"key":"35_CR9","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)"},{"issue":"86","key":"35_CR10","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(86), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"35_CR11","unstructured":"Marras, M., Vignoud, J.T.T., Kaser, T.: Can feature predictive power generalize? Benchmarking early predictors of student success across flipped and online courses. In: 14th International Conference on Educational Data Mining, pp. 150\u2013160 (2021)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Northcutt, C.G., Ho, A.D., Chuang, I.L.: Detecting and preventing \u201cmultiple-account\u201d cheating in massive open online courses. Comput. Educ. 100, 71\u201380 (2016)","DOI":"10.1016\/j.compedu.2016.04.008"},{"key":"35_CR13","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-319-55345-0_13","volume-title":"Smart Sensors at the IoT Frontier","author":"H Ogata","year":"2017","unstructured":"Ogata, H., et al.: Learning analytics for E-Book-based educational big data in higher education. In: Yasuura, H., Kyung, C.-M., Liu, Y., Lin, Y.-L. (eds.) Smart Sensors at the IoT Frontier, pp. 327\u2013350. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-55345-0_13"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Okubo, F., Yamashita, T., Shimada, A., Ogata, H.: A neural network approach for students\u2019 performance prediction. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 598\u2013599 (2017)","DOI":"10.1145\/3027385.3029479"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Park, J., Denaro, K., Rodriguez, F., Smyth, P., Warschauer, M.: Detecting changes in student behavior from clickstream data. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 21\u201330 (2017)","DOI":"10.1145\/3027385.3027430"},{"issue":"140","key":"35_CR16","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"35_CR17","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 6000\u20136010 (2017)"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2021)","DOI":"10.1109\/CVPR46437.2021.00252"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36272-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:09:25Z","timestamp":1710259765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36272-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031362712","9783031362729"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36272-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aied2023.org\/","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":"311","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":"53","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":"26","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":"17% - 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":"4","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}