{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:29:24Z","timestamp":1742984964120,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031204999"},{"type":"electronic","value":"9783031205002"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20500-2_35","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T05:12:32Z","timestamp":1672549952000},"page":"425-437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Relational Cognitive Diagnosis for\u00a0Intelligent Education"],"prefix":"10.1007","author":[{"given":"Kaifang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Adams, R.J., Wilson, M., Wang, W.C.: The multidimensional random coefficients multinomial logit model. Appl. Psychol. Meas. 21(1), 1\u201323 (1997)","DOI":"10.1177\/0146621697211001"},{"key":"35_CR2","unstructured":"Allen, M.J., Yen, W.M.: Introduction to Measurement Theory. Waveland Press, Long Grove (2001)"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Engaging with massive online courses. In: WWW, pp. 687\u2013698 (2014)","DOI":"10.1145\/2566486.2568042"},{"issue":"4698","key":"35_CR4","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1126\/science.228.4698.456","volume":"228","author":"JR Anderson","year":"1985","unstructured":"Anderson, J.R., Boyle, C.F., Reiser, B.J.: Intelligent tutoring systems. Science 228(4698), 456\u2013462 (1985)","journal-title":"Science"},{"issue":"7","key":"35_CR5","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145\u20131159 (1997)","journal-title":"Pattern Recogn."},{"issue":"3","key":"35_CR6","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? - arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3), 1247\u20131250 (2014)","journal-title":"Geosci. Model Dev."},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI, vol. 34, pp. 27\u201334 (2020)","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"35_CR8","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597\u20131607. PMLR (2020)"},{"issue":"1","key":"35_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/0146621617697959","volume":"42","author":"Y Chen","year":"2018","unstructured":"Chen, Y., Li, X., Liu, J., Ying, Z.: Recommendation system for adaptive learning. Appl. Psychol. Meas. 42(1), 24\u201341 (2018)","journal-title":"Appl. Psychol. Meas."},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, S., et al.: Dirt: deep learning enhanced item response theory for cognitive diagnosis. In: CIKM, pp. 2397\u20132400 (2019)","DOI":"10.1145\/3357384.3358070"},{"issue":"1","key":"35_CR11","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3102\/1076998607309474","volume":"34","author":"J De La Torre","year":"2009","unstructured":"De La Torre, J.: Dina model and parameter estimation: a didactic. J. Educ. Behav. Stat. 34(1), 115\u2013130 (2009)","journal-title":"J. Educ. Behav. Stat."},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1016\/S0169-7161(06)26031-0","volume":"26","author":"LV DiBello","year":"2006","unstructured":"DiBello, L.V., Roussos, L.A., Stout, W.: 31A review of cognitively diagnostic assessment and a summary of psychometric models. Handb. Stat. 26, 979\u20131030 (2006)","journal-title":"Handb. Stat."},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press, Hove (2013)","DOI":"10.4324\/9781410605269"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Gao, W., et al.: RCD: relation map driven cognitive diagnosis for intelligent education systems. In: SIGIR, pp. 501\u2013510 (2021)","DOI":"10.1145\/3404835.3462932"},{"key":"35_CR15","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639\u2013648 (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"35_CR17","unstructured":"Khosla, P., et al.: Supervised contrastive learning. In: NeurIPS, vol. 33, pp. 18661\u201318673 (2020)"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: WWW, pp. 2320\u20132329 (2022)","DOI":"10.1145\/3485447.3512104"},{"key":"35_CR19","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-030-93046-2_3","volume-title":"CICAI 2021","author":"M Liu","year":"2021","unstructured":"Liu, M., Shao, P., Zhang, K.: Graph-based exercise-and knowledge-aware learning network for student performance prediction. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds.) CICAI 2021. LNCS, vol. 13069, pp. 27\u201338. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93046-2_3"},{"issue":"1","key":"35_CR20","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":"35_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: Exploiting cognitive structure for adaptive learning. In: KDD, pp. 627\u2013635 (2019)","DOI":"10.1145\/3292500.3330922"},{"key":"35_CR22","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":"35_CR23","unstructured":"Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints arXiv-1807 (2018)"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: GCC: graph contrastive coding for graph neural network pre-training. In: KDD, pp. 1150\u20131160 (2020)","DOI":"10.1145\/3394486.3403168"},{"key":"35_CR25","unstructured":"Rasch, G.: On general laws and the meaning of measurement in psychology. In: Berkeley Symposium on Mathematical Statistics, vol. 4, pp. 321\u2013333 (1961)"},{"key":"35_CR26","series-title":"Statistics for Social and Behavioral Sciences","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-0-387-89976-3_4","volume-title":"Multidimensional Item Response Theory","author":"MD Reckase","year":"2009","unstructured":"Reckase, M.D.: Multidimensional item response theory models. In: Reckase, M.D. (ed.) Multidimensional Item Response Theory. SSBS, pp. 79\u2013112. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-89976-3_4"},{"key":"35_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., van\u00a0den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38"},{"key":"35_CR28","doi-asserted-by":"crossref","unstructured":"Thai-Nghe, N., Schmidt-Thieme, L.: Multi-relational factorization models for student modeling in intelligent tutoring systems. In: 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), pp. 61\u201366. IEEE (2015)","DOI":"10.1109\/KSE.2015.9"},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Tong, S., et al.: Structure-based knowledge tracing: an influence propagation view. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 541\u2013550. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00063"},{"key":"35_CR30","doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Neural cognitive diagnosis for intelligent education systems. In: AAAI, vol. 34, pp. 6153\u20136161 (2020)","DOI":"10.1609\/aaai.v34i04.6080"},{"key":"35_CR31","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: ICML, pp. 9929\u20139939. PMLR (2020)"},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: SIGIR, pp. 165\u2013174 (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"35_CR33","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Self-supervised graph learning for recommendation. In: SIGIR, pp. 726\u2013735 (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"Wu, L., He, X., Wang, X., Zhang, K., Wang, M.: A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation. IEEE Trans. Knowl. Data Eng. (2022)","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"35_CR35","unstructured":"Wu, R., et al.: Cognitive modelling for predicting examinee performance. In: IJCAI (2015)"},{"key":"35_CR36","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-030-67658-2_18","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Y Yang","year":"2021","unstructured":"Yang, Y., et al.: GIKT: a graph-based interaction model for knowledge tracing. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12457, pp. 299\u2013315. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67658-2_18"},{"key":"35_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Modeling context-aware features for cognitive diagnosis in student learning. In: KDD, pp. 2420\u20132428 (2021)","DOI":"10.1145\/3447548.3467264"},{"key":"35_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW, pp. 2069\u20132080 (2021)","DOI":"10.1145\/3442381.3449802"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20500-2_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T05:40:32Z","timestamp":1672551632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20500-2_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031204999","9783031205002"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20500-2_35","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":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"472","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":"164","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":"35% - 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":"3.1","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":"3.7","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)"}}]}}