{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:05:15Z","timestamp":1764785115992,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031301049"},{"type":"electronic","value":"9783031301056"}],"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\/978-3-031-30105-6_19","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T20:31:55Z","timestamp":1681331515000},"page":"224-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Heterogeneous Graph Representation for\u00a0Knowledge Tracing"],"prefix":"10.1007","author":[{"given":"Jisen","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jian","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Long","sequence":"additional","affiliation":[]},{"given":"Liping","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Weinan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Abdelrahman, G., Wang, Q.: Knowledge tracing with sequential key-value memory networks. In: Proceedings of SIGIR (2019)","DOI":"10.1145\/3331184.3331195"},{"key":"19_CR2","unstructured":"Chang, H.S., Hsu, H.J., Chen, K.T.: Modeling exercise relationships in e-learning: a unified approach. In: Proceedings of EDM (2015)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Chen, P., Lu, Y., Zheng, V.W., Pian, Y.: Prerequisite-driven deep knowledge tracing. In: Proceedings of ICDM (2018)","DOI":"10.1109\/ICDM.2018.00019"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of EMNLP (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Choi, Y., et al.: Towards an appropriate query, key, and value computation for knowledge tracing. In: Proceedings of L@S (2020)","DOI":"10.1145\/3386527.3405945"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Choi, Y., et al.: Ednet: a large-scale hierarchical dataset in education. In: Proceedings of AIED (2020)","DOI":"10.1007\/978-3-030-52240-7_13"},{"key":"19_CR7","unstructured":"Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. In: Proceedings of UMUAI (1994)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of KDD (2017)","DOI":"10.1145\/3097983.3098036"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Fan, S., et al.: Metapath-guided heterogeneous graph neural network for intent recommendation. In: Proceedings of KDD (2019)","DOI":"10.1145\/3292500.3330673"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Feng, M., Heffernan, N., Koedinger, K.: Addressing the assessment challenge with an online system that tutors as it assesses. In: Proceedings of UMUAI (2009)","DOI":"10.1007\/s11257-009-9063-7"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Gao, W., et al.: RCD: Relation map driven cognitive diagnosis for intelligent education systems. In: Proceedings of SIGIR (2021)","DOI":"10.1145\/3404835.3462932"},{"key":"19_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of ICLR (2015)"},{"key":"19_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of ICLR (2017)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature (2015)","DOI":"10.1038\/nature14539"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, Y., Chen, X., Shen, J., Zhang, H., Yu, Y.: Improving knowledge tracing via pre-training question embeddings. In: Proceedings of IJCAI (2020)","DOI":"10.24963\/ijcai.2020\/219"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Minn, S., Desmarais, M.C., Zhu, F., Xiao, J., Wang, J.: Dynamic student classification on memory networks for knowledge tracing. In: Proceedings of PAKDD (2019)","DOI":"10.1007\/978-3-030-16145-3_13"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Nakagawa, H., Iwasawa, Y., Matsuo, Y.: Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: WI (2019)","DOI":"10.1145\/3350546.3352513"},{"key":"19_CR18","unstructured":"Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. In: Proceedings of EDM (2019)"},{"key":"19_CR19","unstructured":"Piech, C., et al.: Deep knowledge tracing. In: Proceedings of NeurIPS (2015)"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Shen, S., et al.: Convolutional knowledge tracing: modeling individualization in student learning process. In: Proceedings of SIGIR (2020)","DOI":"10.1145\/3397271.3401288"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. In: Proceedings of TKDE (2017)","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Su, Y., et al.: Exercise-enhanced sequential modeling for student performance prediction. In: Proceedings of AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11864"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Explor. Newsl. (2013)","DOI":"10.1145\/2481244.2481248"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: Meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. (2011)","DOI":"10.14778\/3402707.3402736"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Tong, S., et al.: Structure-based knowledge tracing: an influence propagation view. In: Proceedings of ICDM (2020)","DOI":"10.1109\/ICDM50108.2020.00063"},{"key":"19_CR26","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: Proceedings of ICLR (2018)"},{"key":"19_CR27","unstructured":"Wang, T., Ma, F., Gao, J.: Deep hierarchical knowledge tracing. In: Proceedings of EDM (2019)"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Heterogeneous graph attention network. In: Proceedings of WWW (2019)","DOI":"10.1145\/3308558.3313562"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. (1989)","DOI":"10.1162\/neco.1989.1.2.270"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: GIKT: a graph-based interaction model for knowledge tracing. In: Proceedings of ECML-PKDD (2020)","DOI":"10.1007\/978-3-030-67658-2_18"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: Proceedings of AIED (2013)","DOI":"10.1007\/978-3-642-39112-5_18"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of KDD (2019)","DOI":"10.1145\/3292500.3330961"},{"key":"19_CR33","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 WWW (2017)","DOI":"10.1145\/3038912.3052580"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30105-6_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T20:34:56Z","timestamp":1681331696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30105-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301049","9783031301056"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30105-6_19","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":"13 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"44% - 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":"2.65","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":"3","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":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","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)"}}]}}