{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:15:46Z","timestamp":1742966146751,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031096792"},{"type":"electronic","value":"9783031096808"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-09680-8_12","type":"book-chapter","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T23:02:58Z","timestamp":1656025378000},"page":"123-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Knowledge Tracing on\u00a0Skills with\u00a0Small Datasets"],"prefix":"10.1007","author":[{"given":"Ange","family":"Tato","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Nkambou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"12_CR1","unstructured":"Ange, T., Roger, N., Aude, D.: Hybrid deep neural networks to predict socio-moral reasoning skills. In: Proceedings of the 12th International Conference on Educational Data Mining, pp. 623\u2013626 (2019)"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Ange, T., Roger, N., Aude, D., Claude, F.: Semi-supervised multimodal deep learning model for polarity detection in arguments. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489342"},{"key":"12_CR3","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"12_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-540-73078-1_17","volume-title":"User Modeling 2007","author":"JE Beck","year":"2007","unstructured":"Beck, J.E., Chang, K.: Identifiability: a fundamental problem of student modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 137\u2013146. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-73078-1_17"},{"key":"12_CR5","unstructured":"Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems, pp. 577\u2013585 (2015)"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine learning, pp. 160\u2013167. ACM (2008)","DOI":"10.1145\/1390156.1390177"},{"issue":"4","key":"12_CR7","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":"12_CR8","unstructured":"Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Change Loy, C., Tang, X.: Learning deep representation for imbalanced classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5375\u20135384 (2016)","DOI":"10.1109\/CVPR.2016.580"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Kasurinen, J., Nikula, U.: Estimating programming knowledge with bayesian knowledge tracing. In: ACM SIGCSE Bulletin, vol. 41, pp. 313\u2013317. ACM (2009)","DOI":"10.1145\/1595496.1562972"},{"key":"12_CR12","unstructured":"Khajah, M., Lindsey, R.V., Mozer, M.C.: How deep is knowledge tracing? arXiv preprint arXiv:1604.02416 (2016)"},{"issue":"6","key":"12_CR13","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1109\/69.553163","volume":"8","author":"H Lu","year":"1996","unstructured":"Lu, H., Setiono, R., Liu, H.: Effective data mining using neural networks. IEEE Trans. Knowl. Data Eng. 8(6), 957\u2013961 (1996)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"issue":"6","key":"12_CR15","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1006\/ijhc.1995.1025","volume":"42","author":"J Martin","year":"1995","unstructured":"Martin, J., VanLehn, K.: Student assessment using bayesian nets. Int. J. Hum Comput Stud. 42(6), 575\u2013591 (1995)","journal-title":"Int. J. Hum Comput Stud."},{"key":"12_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-642-01973-9_2","volume-title":"Computational Science","author":"L Nguyen","year":"2009","unstructured":"Nguyen, L., Do, P.: Combination of bayesian network and overlay model in user modeling. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009. LNCS, vol. 5545, pp. 5\u201314. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-01973-9_2"},{"key":"12_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/978-3-319-24258-3_40","volume-title":"Design for Teaching and Learning in a Networked World","author":"R Nkambou","year":"2015","unstructured":"Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P., Tato, A.: Towards an intelligent tutoring system for logical reasoning in multiple contexts. In: Conole, G., Klobu\u010dar, T., Rensing, C., Konert, J., Lavou\u00e9, \u00c9. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 460\u2013466. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24258-3_40"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Nkambou, R., Mizoguchi, R., Bourdeau, J.: Advances in Intelligent Tutoring Systems, vol. 308. Springer, Berlin (2010). https:\/\/doi.org\/10.1007\/978-3-642-14363-2","DOI":"10.1007\/978-3-642-14363-2"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717\u20131724 (2014)","DOI":"10.1109\/CVPR.2014.222"},{"key":"12_CR20","unstructured":"Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, pp. 505\u2013513 (2015)"},{"key":"12_CR21","volume-title":"Artificial Intelligence: a Modern Approach","author":"SJ Russell","year":"2016","unstructured":"Russell, S.J., Norvig, P.: Artificial Intelligence: a Modern Approach. Pearson Education Limited, Malaysia (2016)"},{"key":"12_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-642-24600-5_32","volume-title":"Affective Computing and Intelligent Interaction","author":"J Sabourin","year":"2011","unstructured":"Sabourin, J., Mott, B., Lester, J.C.: Modeling learner affect with theoretically grounded dynamic bayesian networks. In: D\u2019Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6974, pp. 286\u2013295. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24600-5_32"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3982\u20133991 (2015)","DOI":"10.1109\/CVPR.2015.7299024"},{"key":"12_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/978-3-319-45153-4_78","volume-title":"Adaptive and Adaptable Learning","author":"A Tato","year":"2016","unstructured":"Tato, A., Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P.: A bayesian network for the cognitive diagnosis of deductive reasoning. In: Verbert, K., Sharples, M., Klobu\u010dar, T. (eds.) EC-TEL 2016. LNCS, vol. 9891, pp. 627\u2013631. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-45153-4_78"},{"key":"12_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-319-61425-0_32","volume-title":"Artificial Intelligence in Education","author":"A Tato","year":"2017","unstructured":"Tato, A., Nkambou, R., Brisson, J., Robert, S.: Predicting learner\u2019s deductive reasoning skills using a bayesian network. In: Andr\u00e9, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 381\u2013392. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-61425-0_32"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Wang, L., Sy, A., Liu, L., Piech, C.: Deep knowledge tracing on programming exercises. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pp. 201\u2013204. ACM (2017)","DOI":"10.1145\/3051457.3053985"},{"key":"12_CR27","unstructured":"Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048\u20132057 (2015)"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Yeung, C.K., Yeung, D.Y.: Addressing two problems in deep knowledge tracing via prediction-consistent regularization. arXiv preprint arXiv:1806.02180 (2018)","DOI":"10.1145\/3231644.3231647"},{"key":"12_CR29","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":"12_CR30","doi-asserted-by":"crossref","unstructured":"Zappone, A., Di Renzo, M., Debbah, M., Lam, T.T., Qian, X.: Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks towards wireless systems optimization. arXiv preprint arXiv:1808.01672 (2018)","DOI":"10.1109\/MVT.2019.2921627"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiong, X., Zhao, S., Botelho, A., Heffernan, N.T.: Incorporating rich features into deep knowledge tracing. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pp. 169\u2013172. ACM (2017)","DOI":"10.1145\/3051457.3053976"}],"container-title":["Lecture Notes in Computer Science","Intelligent Tutoring Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09680-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T17:39:45Z","timestamp":1727458785000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09680-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031096792","9783031096808"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09680-8_12","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":"24 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ITS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Tutoring Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bucharest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Romania","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":"29 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"its2022","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","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":"14","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":"13","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":"28% - 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":"3.8","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":"2.8","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":"Additionally, there are 11 poster 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)"}}]}}