{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:27:13Z","timestamp":1742927233029,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981809"},{"type":"electronic","value":"9789819981816"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8181-6_41","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"535-551","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ASRCD: Adaptive Serial Relation-Based Model for\u00a0Cognitive Diagnosis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4459-0861","authenticated-orcid":false,"given":"Zhuonan","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8102-3949","authenticated-orcid":false,"given":"Dongnan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caiyun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3706-8896","authenticated-orcid":false,"given":"Weidong","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"41_CR1","unstructured":"Akour, M., AL-Omari, H.: Empirical investigation of the stability of IRT item-parameters estimation. Int. Online J. Educ. Sci. 5(2), 291\u2013301 (2013)"},{"key":"41_CR2","doi-asserted-by":"publisher","unstructured":"Anderson, A., Huttenlocher, D., et al.: Engaging with massive online courses (2014). https:\/\/doi.org\/10.1145\/2566486.2568042","DOI":"10.1145\/2566486.2568042"},{"key":"41_CR3","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-1-4614-3305-7_4","volume-title":"Learning Analytics","author":"RS Baker","year":"2014","unstructured":"Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Larusson, J.A., White, B. (eds.) Learning Analytics, pp. 61\u201375. Springer, New York (2014). https:\/\/doi.org\/10.1007\/978-1-4614-3305-7_4"},{"key":"41_CR4","unstructured":"Barnes, T.: The Q-matrix method: mining student response data for knowledge. In: American Association for Artificial Intelligence 2005 Educational Data Mining Workshop, Pittsburgh, PA, USA, pp. 1\u20138. AAAI Press (2005)"},{"issue":"1","key":"41_CR5","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":"41_CR6","doi-asserted-by":"publisher","unstructured":"DiBello, L.V., Roussos, L.A., Stout, W.: 31A review of cognitively diagnostic assessment and a summary of psychometric models. In: Rao, C., Sinharay, S. (eds.) Handbook of Statistics, vol. 26, pp. 979\u20131030. Elsevier (2006). https:\/\/doi.org\/10.1016\/S0169-7161(06)26031-0","DOI":"10.1016\/S0169-7161(06)26031-0"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press (2013)","DOI":"10.4324\/9781410605269"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Gao, W., Liu, Q., et al.: RCD: relation map driven cognitive diagnosis for intelligent education systems. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501\u2013510 (2021)","DOI":"10.1145\/3404835.3462932"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, Q., et al.: Constructing educational concept maps with multiple relationships from multi-source data. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1108\u20131113. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00132"},{"issue":"8","key":"41_CR10","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., et al.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"issue":"362","key":"41_CR11","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1080\/01621459.1978.10481567","volume":"73","author":"K Larntz","year":"1978","unstructured":"Larntz, K.: Small-sample comparisons of exact levels for chi-squared goodness-of-fit statistics. J. Am. Stat. Assoc. 73(362), 253\u2013263 (1978)","journal-title":"J. Am. Stat. Assoc."},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Leighton, J., Gierl, M.: Cognitive Diagnostic Assessment for Education: Theory and Applications. Cambridge University Press (2007)","DOI":"10.1017\/CBO9780511611186"},{"key":"41_CR13","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1007\/978-3-030-89698-0_88","volume-title":"Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Wang, S., Liang, Z., Fu, P.: Concept relative attention based deep knowledge tracing. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds.) ICNC-FSKD 2021. LNDECT, vol. 89, pp. 858\u2013865. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-89698-0_88"},{"issue":"2","key":"41_CR14","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1145\/6497.214326","volume":"12","author":"CR Mehta","year":"1986","unstructured":"Mehta, C.R., Patel, N.R.: Algorithm 643: FEXACT: a FORTRAN subroutine for fisher\u2019s exact test on unordered r $$\\times $$ c contingency tables. ACM Trans. Math. Softw. (TOMS) 12(2), 154\u2013161 (1986)","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Nakagawa, H., Iwasawa, Y., et al.: Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: IEEE\/WIC\/ACM International Conference on Web Intelligence, pp. 156\u2013163 (2019)","DOI":"10.1145\/3350546.3352513"},{"key":"41_CR16","unstructured":"Novak, J.D.: Learning, Creating, and Using Knowledge: Concept Maps as Facilitative Tools in Schools and Corporations. Routledge (2010)"},{"key":"41_CR17","unstructured":"Piaget, J., Brown, T., et al.: The Equilibration of Cognitive Structures: The Central Problem of Intellectual Development. University of Chicago Press (1985)"},{"issue":"1","key":"41_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40165-015-0015-5","volume":"2","author":"W Premchaiswadi","year":"2015","unstructured":"Premchaiswadi, W., Porouhan, P.: Process modeling and decision mining in a collaborative distance learning environment. Decis. Anal. 2(1), 1\u201334 (2015). https:\/\/doi.org\/10.1186\/s40165-015-0015-5","journal-title":"Decis. Anal."},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Reckase, M.D.: 18 multidimensional item response theory. In: Handbook of Statistics, vol. 26, pp. 607\u2013642 (2006)","DOI":"10.1016\/S0169-7161(06)26018-8"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Shi, H., Yang, Y., et al.: Dynamic multi-skill knowledge tracing for intelligent educational system. In: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence, pp. 1\u20136 (2022)","DOI":"10.1145\/3579654.3579740"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Tong, S., Liu, Q., 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":"41_CR22","unstructured":"Vaswani, A., Shazeer, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"41_CR23","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"41_CR24","doi-asserted-by":"publisher","unstructured":"Verhelst, N.D., Glas, C.A.W.: The one parameter logistic model. In: Fischer, G.H., Molenaar, I.W. (eds.) Rasch Models, pp. 215\u2013237. Springer, New York (1995). https:\/\/doi.org\/10.1007\/978-1-4612-4230-7_12","DOI":"10.1007\/978-1-4612-4230-7_12"},{"key":"41_CR25","unstructured":"Vukicevic, M., Jovanovic, M., et al.: Recommender system for selection of the right study program for higher education students. In: RapidMiner: Data Mining Use Cases and Business Analytics Applications, p. 145 (2013)"},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, Q., et al.: Neural cognitive diagnosis for intelligent education systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34-04, pp. 6153\u20136161 (2020)","DOI":"10.1609\/aaai.v34i04.6080"},{"key":"41_CR27","unstructured":"Wu, R., Liu, Q., et al.: Cognitive modelling for predicting examinee performance. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)"},{"issue":"3","key":"41_CR28","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.32604\/cmc.2020.011881","volume":"65","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Fu, P., et al.: MOOC learner\u2019s final grade prediction based on an improved random forests method. Comput. Mater. Continua 65(3), 2413\u20132423 (2020)","journal-title":"Comput. Mater. Continua"},{"issue":"3","key":"41_CR29","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1080\/24754269.2020.1796098","volume":"5","author":"X Yu","year":"2021","unstructured":"Yu, X., Li, S., et al.: A three-parameter logistic regression model. Stat. Theor. Relat. Fields 5(3), 265\u2013274 (2021)","journal-title":"Stat. Theor. Relat. Fields"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8181-6_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:32:32Z","timestamp":1710329552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8181-6_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981809","9789819981816"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8181-6_41","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 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":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}