{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:19:44Z","timestamp":1757618384556,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819681792"},{"type":"electronic","value":"9789819681808"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-8180-8_18","type":"book-chapter","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T09:16:28Z","timestamp":1750324588000},"page":"224-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameter Estimation in\u00a0Cognitive Diagnosis: A Light-Weight Neural Network Solution"],"prefix":"10.1007","author":[{"given":"Qiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binbin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Chan, W., Jaitly, N., Le, Q., Vinyals, O.: Listen, attend and spell: a neural network for large vocabulary conversational speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4960\u20134964. IEEE (2016)","key":"18_CR1","DOI":"10.1109\/ICASSP.2016.7472621"},{"doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Tracking knowledge proficiency of students with educational priors. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 989\u2013998 (2017)","key":"18_CR2","DOI":"10.1145\/3132847.3132929"},{"issue":"6","key":"18_CR3","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1177\/0146621612449069","volume":"36","author":"LT DeCarlo","year":"2012","unstructured":"DeCarlo, L.T.: Recognizing uncertainty in the q-matrix via a bayesian extension of the dina model. Appl. Psychol. Meas. 36(6), 447\u2013468 (2012)","journal-title":"Appl. Psychol. Meas."},{"issue":"1","key":"18_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc.: Ser. B (Methodol.) 39(1), 1\u201322 (1977)","journal-title":"J. R. Stat. Soc.: Ser. B (Methodol.)"},{"doi-asserted-by":"crossref","unstructured":"Hastings, W.K.: Monte Carlo sampling methods using Markov chains and their applications (1970)","key":"18_CR5","DOI":"10.2307\/2334940"},{"issue":"2","key":"18_CR6","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11336-008-9089-5","volume":"74","author":"RA Henson","year":"2009","unstructured":"Henson, R.A., Templin, J.L., Willse, J.T.: Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika 74(2), 191 (2009)","journal-title":"Psychometrika"},{"issue":"1","key":"18_CR7","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."},{"issue":"2","key":"18_CR8","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/BF02294535","volume":"64","author":"E Maris","year":"1999","unstructured":"Maris, E.: Estimating multiple classification latent class models. Psychometrika 64(2), 187\u2013212 (1999)","journal-title":"Psychometrika"},{"unstructured":"Mordvintsev, A., Olah, C., Tyka, M.: Inceptionism: going deeper into neural networks (2015)","key":"18_CR9"},{"unstructured":"Piech, C., et al.: Deep knowledge tracing. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 1, pp. 505\u2013513 (2015)","key":"18_CR10"},{"doi-asserted-by":"crossref","unstructured":"Roussos, L.A., DiBello, L.V., Stout, W., Hartz, S.M., Henson, R.A., Templin, J.L.: The fusion model skills diagnosis system. In: Cognitive Diagnostic Assessment for Education: Theory and Applications, pp. 275\u2013318 (2007)","key":"18_CR11","DOI":"10.1017\/CBO9780511611186.010"},{"issue":"2","key":"18_CR12","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1002\/bimj.201600225","volume":"60","author":"M Silva","year":"2017","unstructured":"Silva, M., Oliveira, E., Davier, A., Baz\u00e1n, J.: Estimating the dina model parameters using the no-u-turn sampler. Biom. J. 60(2), 352 (2017)","journal-title":"Biom. J."},{"issue":"1","key":"18_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3102\/1076998607309474","volume":"34","author":"D Torre","year":"2009","unstructured":"Torre, D.: Dina model and parameter estimation: a didactic. J. Educ. Behav. Stat. 34(1), 115\u2013130 (2009)","journal-title":"J. Educ. Behav. Stat."},{"doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Neural cognitive diagnosis for intelligent education systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 6153\u20136161 (2020)","key":"18_CR14","DOI":"10.1609\/aaai.v34i04.6080"},{"issue":"5","key":"18_CR15","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1080\/00273171.2021.1896352","volume":"57","author":"J Wang","year":"2022","unstructured":"Wang, J., Shi, N., Zhang, X., Xu, G.: Sequential gibbs sampling algorithm for cognitive diagnosis models with many attributes. Multivar. Behav. Res. 57(5), 840\u2013858 (2022)","journal-title":"Multivar. Behav. Res."},{"doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, Z., Hu, H.: Hierarchical attention network for image captioning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 8957\u20138964 (2019)","key":"18_CR16","DOI":"10.1609\/aaai.v33i01.33018957"},{"unstructured":"Wu, R., et al.: Cognitive modelling for predicting examinee performance. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)","key":"18_CR17"},{"doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 7370\u20137377 (2019)","key":"18_CR18","DOI":"10.1609\/aaai.v33i01.33017370"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8180-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T20:23:35Z","timestamp":1757190215000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8180-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819681792","9789819681808"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8180-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"20 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","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":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}