{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:18:06Z","timestamp":1760710686897,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030858988"},{"type":"electronic","value":"9783030858995"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-85899-5_17","type":"book-chapter","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T10:06:38Z","timestamp":1629281198000},"page":"221-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SQKT: A Student Attention-Based and\u00a0Question-Aware Model for\u00a0Knowledge Tracing"],"prefix":"10.1007","author":[{"given":"Qize","family":"Xie","sequence":"first","affiliation":[]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peidong","family":"Song","sequence":"additional","affiliation":[]},{"given":"Xuemin","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Abdelrahman, G., Wang, Q.: Knowledge tracing with sequential key-value memory networks. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 175\u2013184 (2019)","DOI":"10.1145\/3331184.3331195"},{"key":"17_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/978-3-540-69132-7_44","volume-title":"Intelligent Tutoring Systems","author":"RSJ Baker","year":"2008","unstructured":"Baker, R.S.J., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In: Woolf, B.P., A\u00efmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406\u2013415. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-69132-7_44"},{"key":"17_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/11774303_17","volume-title":"Intelligent Tutoring Systems","author":"H Cen","year":"2006","unstructured":"Cen, H., Koedinger, K., Junker, B.: Learning factors analysis \u2013 a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164\u2013175. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11774303_17"},{"key":"17_CR4","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"},{"issue":"4","key":"17_CR5","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/BF01099821","volume":"4","author":"AT Corbett","year":"1994","unstructured":"Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253\u2013278 (1994)","journal-title":"User Model. User-Adap. Inter."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Desmarais, M.C., d Baker, R.S.: A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling User-Adapted Interact. 22(1), 9\u201338 (2012)","DOI":"10.1007\/s11257-011-9106-8"},{"key":"17_CR7","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605269","volume-title":"Item Response Theory","author":"SE Embretson","year":"2013","unstructured":"Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press, London (2013)"},{"issue":"8","key":"17_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"1","key":"17_CR9","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TKDE.2019.2924374","volume":"33","author":"Q Liu","year":"2019","unstructured":"Liu, Q., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. 33(1), 100\u2013115 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"17_CR10","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"17_CR11","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546 (2013)"},{"key":"17_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-030-16145-3_13","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"S Minn","year":"2019","unstructured":"Minn, S., Desmarais, M.C., Zhu, F., Xiao, J., Wang, J.: Dynamic student classiffication on memory networks for knowledge tracing. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11440, pp. 163\u2013174. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-16145-3_13"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Minn, S., Yu, Y., Desmarais, M.C., Zhu, F., Vie, J.J.: Deep knowledge tracing and dynamic student classification for knowledge tracing. In: 2018 IEEE International conference on data mining (ICDM), pp. 1182\u20131187. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00156"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Nakagawa, H., Iwasawa, Y., Matsuo, Y.: Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: 2019 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI), pp. 156\u2013163. IEEE (2019)","DOI":"10.1145\/3350546.3352513"},{"key":"17_CR15","unstructured":"Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837 (2019)"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Pandey, S., Srivastava, J.: RKT: relation-aware self-attention for knowledge tracing. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1205\u20131214 (2020)","DOI":"10.1145\/3340531.3411994"},{"key":"17_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-642-13470-8_24","volume-title":"User Modeling, Adaptation, and Personalization","author":"ZA Pardos","year":"2010","unstructured":"Pardos, Z.A., Heffernan, N.T.: Modeling individualization in a Bayesian networks implementation of knowledge tracing. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 255\u2013266. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13470-8_24"},{"key":"17_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-642-22362-4_21","volume-title":"User Modeling, Adaption and Personalization","author":"ZA Pardos","year":"2011","unstructured":"Pardos, Z.A., Heffernan, N.T.: KT-IDEM: introducing item difficulty to the knowledge tracing model. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 243\u2013254. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-22362-4_21"},{"key":"17_CR19","unstructured":"Pavlik Jr, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis-a new alternative to knowledge tracing. Online Submission (2009)"},{"key":"17_CR20","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 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"17_CR21","unstructured":"Piech, C., et al.: Deep knowledge tracing. arXiv preprint arXiv:1506.05908 (2015)"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995\u20131000. IEEE (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"17_CR23","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842\u20131850. PMLR (2016)"},{"issue":"1","key":"17_CR24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Su, Y., et al.: Exercise-enhanced sequential modeling for student performance prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11864"},{"key":"17_CR26","unstructured":"Tong, H., Zhou, Y., Wang, Z.: HGKT: introducing problem schema with hierarchical exercise graph for knowledge tracing. arXiv preprint arXiv:2006.16915 (2020)"},{"issue":"2","key":"17_CR27","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","volume":"1","author":"RJ Williams","year":"1989","unstructured":"Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270\u2013280 (1989)","journal-title":"Neural Comput."},{"key":"17_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-642-39112-5_18","volume-title":"Artificial Intelligence in Education","author":"MV Yudelson","year":"2013","unstructured":"Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 171\u2013180. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39112-5_18"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shi, X., King, I., Yeung, D.Y.: Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web, pp. 765\u2013774 (2017)","DOI":"10.1145\/3038912.3052580"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85899-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T15:08:20Z","timestamp":1673104100000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85899-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030858988","9783030858995"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85899-5_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"19 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2021","order":10,"name":"conference_id","label":"Conference ID","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"184","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":"44","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":"24","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":"3.6","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":"6.38","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)"}}]}}