{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:16:24Z","timestamp":1781518584272,"version":"3.54.1"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031630309","type":"print"},{"value":"9783031630316","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-63031-6_10","type":"book-chapter","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T16:05:16Z","timestamp":1717171516000},"page":"107-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["MonaCoBERT: Monotonic Attention Based ConvBERT for\u00a0Knowledge Tracing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0883-4128","authenticated-orcid":false,"given":"Unggi","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6927-7409","authenticated-orcid":false,"given":"Yonghyun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4174-6568","authenticated-orcid":false,"given":"Yujin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7968-3726","authenticated-orcid":false,"given":"Seongyune","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0555-8591","authenticated-orcid":false,"given":"Hyeoncheol","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"issue":"5","key":"10_CR1","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s11423-021-10008-5","volume":"69","author":"Y An","year":"2021","unstructured":"An, Y., Kaplan-Rakowski, R., Yang, J., Conan, J., Kinard, W., Daughrity, L.: Examining k-12 teachers\u2019 feelings, experiences, and perspectives regarding online teaching during the early stage of the COVID-19 pandemic. Educ. Tech. Res. Dev. 69(5), 2589\u20132613 (2021)","journal-title":"Educ. Tech. Res. Dev."},{"key":"10_CR2","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150 (2020)"},{"issue":"5","key":"10_CR3","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s11159-020-09860-z","volume":"66","author":"X Bonal","year":"2020","unstructured":"Bonal, X., Gonz\u00e1lez, S.: The impact of lockdown on the learning gap: family and school divisions in times of crisis. Int. Rev. Educ. 66(5), 635\u2013655 (2020)","journal-title":"Int. Rev. Educ."},{"key":"10_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-030-52240-7_13","volume-title":"Artificial Intelligence in Education","author":"Y Choi","year":"2020","unstructured":"Choi, Y., et al.: EdNet: a large-scale hierarchical dataset in education. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Mill\u00e1n, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 69\u201373. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52240-7_13"},{"key":"10_CR5","unstructured":"Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)"},{"issue":"2","key":"10_CR6","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s40593-019-00188-w","volume":"30","author":"M Cukurova","year":"2020","unstructured":"Cukurova, M., Luckin, R., Kent, C.: Impact of an artificial intelligence research frame on the perceived credibility of educational research evidence. Int. J. Artif. Intell. Educ. 30(2), 205\u2013235 (2020)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"10_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Feng, W., Tang, J., Liu, T.X.: Understanding dropouts in MOOCs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 517\u2013524 (2019)","DOI":"10.1609\/aaai.v33i01.3301517"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Heffernan, N., Lan, A.S.: Context-aware attentive knowledge tracing. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2330\u20132339 (2020)","DOI":"10.1145\/3394486.3403282"},{"key":"10_CR10","unstructured":"Gildenblat, J., contributors: Pytorch library for cam methods. https:\/\/github.com\/jacobgil\/pytorch-grad-cam (2021)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"He, J., Bailey, J., Rubinstein, B., Zhang, R.: Identifying at-risk students in massive open online courses. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)","DOI":"10.1609\/aaai.v29i1.9471"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/s40593-014-0024-x","volume":"24","author":"NT Heffernan","year":"2014","unstructured":"Heffernan, N.T., Heffernan, C.L.: The assistments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. Int. J. Artif. Intell. Educ. 24, 470\u2013497 (2014)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Holmes, W., Bialik, M., Fadel, C.: Artificial Intelligence in Education. (2020)","DOI":"10.1007\/978-3-030-10576-1_107"},{"issue":"1","key":"10_CR14","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s11423-020-09920-z","volume":"69","author":"Y Hu","year":"2021","unstructured":"Hu, Y., Spiro, R.J.: Design for now, but with the future in mind: a \u201ccognitive flexibility theory\u2019\u2019 perspective on online learning through the lens of MOOCs. Educ. Tech. Res. Dev. 69(1), 373\u2013378 (2021)","journal-title":"Educ. Tech. Res. Dev."},{"key":"10_CR15","first-page":"12837","volume":"33","author":"ZH Jiang","year":"2020","unstructured":"Jiang, Z.H., Yu, W., Zhou, D., Chen, Y., Feng, J., Yan, S.: ConvBERT: improving BERT with span-based dynamic convolution. Adv. Neural. Inf. Process. Syst. 33, 12837\u201312848 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"10_CR16","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s11423-020-09884-0","volume":"69","author":"K Karakaya","year":"2021","unstructured":"Karakaya, K.: Design considerations in emergency remote teaching during the COVID-19 pandemic: a human-centered approach. Educ. Tech. Res. Dev. 69(1), 295\u2013299 (2021)","journal-title":"Educ. Tech. Res. Dev."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, W., Jang, Y., Choi, S., Jung, H., Kim, H.: Student knowledge prediction for teacher-student interaction. In: EAAI, pp. 15560\u201315568 (2021)","DOI":"10.1609\/aaai.v35i17.17832"},{"key":"10_CR18","doi-asserted-by":"publisher","unstructured":"Kim, S., Kim, W., Jung, H., Kim, H.: DiKT: dichotomous knowledge tracing. In: Cristea, A.I., Troussas, C. (eds.) International Conference on Intelligent Tutoring Systems, pp. 41\u201351. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80421-3_5","DOI":"10.1007\/978-3-030-80421-3_5"},{"key":"10_CR19","unstructured":"Kitaev, N., Kaiser, \u0141., Levskaya, A.: Reformer: the efficient transformer. arXiv preprint arXiv:2001.04451 (2020)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Kizilcec, R.F., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 170\u2013179 (2013)","DOI":"10.1145\/2460296.2460330"},{"key":"10_CR21","unstructured":"Lee, S.H., Lee, S., Song, B.C.: Vision transformer for small-size datasets. arXiv preprint arXiv:2112.13492 (2021)"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Lee, W., Chun, J., Lee, Y., Park, K., Park, S.: Contrastive learning for knowledge tracing. In: Proceedings of the ACM Web Conference 2022, pp. 2330\u20132338 (2022)","DOI":"10.1145\/3485447.3512105"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Li, K., Yu, R., Wang, Z., Yuan, L., Song, G., Chen, J.: Locality guidance for improving vision transformers on tiny datasets. arXiv preprint arXiv:2207.10026 (2022)","DOI":"10.1007\/978-3-031-20053-3_7"},{"key":"10_CR24","unstructured":"Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"10_CR25","unstructured":"Li, Y., Zhang, K., Cao, J., Timofte, R., Van\u00a0Gool, L.: LocalViT: bringing locality to vision transformers. arXiv preprint arXiv:2104.05707 (2021)"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Lin, Y., et al.: BertGCN: transductive text classification by combining GCN and BERT. arXiv preprint arXiv:2105.05727 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.126"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Liu, L., Liu, X., Gao, J., Chen, W., Han, J.: Understanding the difficulty of training transformers. arXiv preprint arXiv:2004.08249 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.463"},{"key":"10_CR28","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. arXiv preprint arXiv:2012.05031 (2020)","DOI":"10.24963\/ijcai.2020\/219"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10_CR30","unstructured":"Luckin, R., Holmes, W., Griffiths, M., Forcier, L.B.: Intelligence Unleashed: An Argument for AI in Education (2016)"},{"issue":"11","key":"10_CR31","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR32","unstructured":"Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837 (2019)"},{"key":"10_CR33","unstructured":"Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"10_CR34","doi-asserted-by":"publisher","first-page":"249","DOI":"10.3758\/BF03194060","volume":"14","author":"S Ritter","year":"2007","unstructured":"Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.: Cognitive tutor: applied research in mathematics education. Psychon. Bull. Rev. 14, 249\u2013255 (2007)","journal-title":"Psychon. Bull. Rev."},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., Choi, Y.: Saint+: integrating temporal features for EdNet correctness prediction. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 490\u2013496 (2021)","DOI":"10.1145\/3448139.3448188"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441\u20131450 (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"10_CR38","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-030-98005-4_19","volume-title":"Ad Hoc Networks and Tools for IT","author":"W Tan","year":"2022","unstructured":"Tan, W., Jin, Y., Liu, M., Zhang, H.: BiDKT: deep knowledge tracing with BERT. In: Bao, W., Yuan, X., Gao, L., Luan, T.H., Choi, D.B.J. (eds.) ADHOCNETS\/TridentCom -2021. LNICST, vol. 428, pp. 260\u2013278. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-98005-4_19"},{"key":"10_CR39","unstructured":"Tiana, Z., Zhengc, G., Flanaganb, B., Mic, J., Ogatab, H.: BEKT: deep knowledge tracing with bidirectional encoder representations from transformers. In: Proceedings of the 29th International Conference on Computers in Education (2021)"},{"key":"10_CR40","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"10_CR41","unstructured":"Xiong, R., et al.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524\u201310533. PMLR (2020)"},{"key":"10_CR42","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"10_CR43","unstructured":"Yeung, C.K.: Deep-IRT: make deep learning based knowledge tracing explainable using item response theory. arXiv preprint arXiv:1904.11738 (2019)"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Yeung, C.K., Yeung, D.Y.: Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1\u201310 (2018)","DOI":"10.1145\/3231644.3231647"},{"key":"10_CR45","first-page":"17283","volume":"33","author":"M Zaheer","year":"2020","unstructured":"Zaheer, M., et al.: Big bird: transformers for longer sequences. Adv. Neural. Inf. Process. Syst. 33, 17283\u201317297 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR46","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 the 26th International Conference on World Wide Web, pp. 765\u2013774 (2017)","DOI":"10.1145\/3038912.3052580"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhu, X., Zhang, C., Ji, Y., Pan, F., Yin, C.: Multi-factors aware dual-attentional knowledge tracing. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2588\u20132597 (2021)","DOI":"10.1145\/3459637.3482372"}],"container-title":["Lecture Notes in Computer Science","Generative Intelligence and Intelligent Tutoring Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63031-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T16:11:29Z","timestamp":1717171889000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63031-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031630309","9783031630316"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63031-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","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":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"its2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iis-international.org\/its2024-generative-intelligence-and-its\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}