{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:29:10Z","timestamp":1772159350913,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031362712","type":"print"},{"value":"9783031362729","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-36272-9_53","type":"book-chapter","created":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T23:03:19Z","timestamp":1687734199000},"page":"651-664","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Teacher Talk Moves in K12 Mathematics Lessons: Automatic Identification, Prediction Explanation, and Characteristic Exploration"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6488-0234","authenticated-orcid":false,"given":"Deliang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dapeng","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqian","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaowei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"53_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"53_CR2","unstructured":"Alexander, R.J.: Towards Dialogic Teaching: Rethinking Classroom Talk, 5th edn. Dialogos (2017)"},{"key":"53_CR3","unstructured":"Alvarez Melis, D., Jaakkola, T.: Towards robust interpretability with self-explaining neural networks. In: Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)"},{"issue":"4\u20135","key":"53_CR4","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1080\/10508406.2020.1783269","volume":"29","author":"G Chen","year":"2020","unstructured":"Chen, G., Chan, C.K.K., Chan, K.K.H., Clarke, S.N., Resnick, L.B.: Efficacy of video-based teacher professional development for increasing classroom discourse and student learning. J. Learn. Sci. 29(4\u20135), 642\u2013680 (2020)","journal-title":"J. Learn. Sci."},{"key":"53_CR5","unstructured":"Cook, C.: An open vocabulary approach for detecting authentic questions in classroom discourse. In: International Conference on Educational Data Mining (2018)"},{"key":"53_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: International Conference of the North American Chapter of the Association for Computational Linguistics), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"53_CR7","doi-asserted-by":"crossref","unstructured":"Donnelly, P.J., Blanchard, N., Olney, A.M., Kelly, S., Nystrand, M., D\u2019Mello, S.K.: Words matter: automatic detection of teacher questions in live classroom discourse using linguistics, acoustics, and context. In: International Learning Analytics and Knowledge Conference, pp. 218\u2013227. ACM (2017)","DOI":"10.1145\/3027385.3027417"},{"issue":"4\u20135","key":"53_CR8","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1080\/10508406.2019.1573730","volume":"28","author":"C Howe","year":"2019","unstructured":"Howe, C., Hennessy, S., Mercer, N., Vrikki, M., Wheatley, L.: Teacher-student dialogue during classroom teaching: does it really impact on student outcomes? J. Learn. Sci. 28(4\u20135), 462\u2013512 (2019)","journal-title":"J. Learn. Sci."},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Hunkins, N.C., Kelly, S., D\u2019Mello, S.: \"Beautiful work, you\u2019re rock stars!\": teacher analytics to uncover discourse that supports or undermines student motivation, identity, and belonging in classrooms. In: 12th International Learning Analytics and Knowledge Conference (LAK 2022), pp. 230\u2013238. ACM (2022)","DOI":"10.1145\/3506860.3506896"},{"key":"53_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.tate.2022.103631","volume":"112","author":"J Jacobs","year":"2022","unstructured":"Jacobs, J., Scornavacco, K., Harty, C., Suresh, A., Lai, V., Sumner, T.: Promoting rich discussions in mathematics classrooms: using personalized, automated feedback to support reflection and instructional change. Teach. Teach. Educ. 112, 103631 (2022)","journal-title":"Teach. Teach. Educ."},{"key":"53_CR11","doi-asserted-by":"crossref","unstructured":"Jensen, E., Pugh, S.L., D\u2019Mello, S.K.: A deep transfer learning approach to modeling teacher discourse in the classroom. In: 11th International Learning Analytics and Knowledge Conference (LAK 2021), pp. 302\u2013312. ACM (2021)","DOI":"10.1145\/3448139.3448168"},{"issue":"7","key":"53_CR12","doi-asserted-by":"publisher","first-page":"451","DOI":"10.3102\/0013189X18785613","volume":"47","author":"S Kelly","year":"2018","unstructured":"Kelly, S., Olney, A.M., Donnelly, P., Nystrand, M., D\u2019Mello, S.K.: Automatically measuring question authenticity in real-world classrooms. Educ. Res. 47(7), 451\u2013464 (2018)","journal-title":"Educ. Res."},{"key":"53_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2022.100074","volume":"3","author":"H Khosravi","year":"2022","unstructured":"Khosravi, H., et al.: Explainable artificial intelligence in education. Comput. Educ. Artif. Intell. 3, 100074 (2022)","journal-title":"Comput. Educ. Artif. Intell."},{"key":"53_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, X., Hovy, E., Jurafsky, D.: Visualizing and understanding neural models in NLP. In: International Conference of the North American Chapter of the Association for Computational Linguistics, pp. 681\u2013691 (2016)","DOI":"10.18653\/v1\/N16-1082"},{"key":"53_CR15","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.ijer.2017.08.007","volume":"97","author":"N Mercer","year":"2019","unstructured":"Mercer, N., Hennessy, S., Warwick, P.: Dialogue, thinking together and digital technology in the classroom: some educational implications of a continuing line of inquiry. Int. J. Educ. Res. 97, 187\u2013199 (2019)","journal-title":"Int. J. Educ. Res."},{"key":"53_CR16","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11217-007-9071-1","volume":"27","author":"S Michaels","year":"2008","unstructured":"Michaels, S., O\u2019Connor, C., Resnick, L.: Deliberative discourse idealized and realized: accountable talk in the classroom and in civic life. Stud. Philos. Educ. 27, 283\u2013297 (2008)","journal-title":"Stud. Philos. Educ."},{"key":"53_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019)","journal-title":"Artif. Intell."},{"key":"53_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon, G., Samek, W., M\u00fcller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1\u201315 (2018)","journal-title":"Digit. Signal Process."},{"key":"53_CR19","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.ijer.2017.11.003","volume":"97","author":"C O\u2019Connor","year":"2019","unstructured":"O\u2019Connor, C., Michaels, S.: Supporting teachers in taking up productive talk moves: the long road to professional learning at scale. Int. J. Educ. Res. 97, 166\u2013175 (2019)","journal-title":"Int. J. Educ. Res."},{"key":"53_CR20","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"issue":"3","key":"53_CR21","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1177\/0735633120968554","volume":"59","author":"Y Song","year":"2021","unstructured":"Song, Y., Lei, S., Hao, T., Lan, Z., Ding, Y.: Automatic classification of semantic content of classroom dialogue. J. Educ. Comput. Res. 59(3), 496\u2013521 (2021)","journal-title":"J. Educ. Comput. Res."},{"key":"53_CR22","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 3319\u20133328. PMLR (2017)"},{"key":"53_CR23","unstructured":"Suresh, A., Jacobs, J., Harty, C., Perkoff, M., Martin, J.H., Sumner, T.: The TalkMoves dataset: K-12 mathematics lesson transcripts annotated for teacher and student discursive moves (2022)"},{"key":"53_CR24","doi-asserted-by":"crossref","unstructured":"Suresh, A., Sumner, T., Jacobs, J., Foland, B., Ward, W.H.: Automating analysis and feedback to improve mathematics teachers\u2019 classroom discourse. In: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), pp. 9721\u20139728. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33019721"},{"key":"53_CR25","doi-asserted-by":"publisher","unstructured":"Wang, D., Lu, Y., Zhang, Z., Chen, P.: A generic interpreting method for knowledge tracing models. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education (AIED 2022). LNCS, vol. 13355, pp. 573\u2013580. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-11644-5_51","DOI":"10.1007\/978-3-031-11644-5_51"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36272-9_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:12:20Z","timestamp":1710259940000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36272-9_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031362712","9783031362729"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36272-9_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aied2023.org\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"311","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":"53","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":"26","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":"17% - 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","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}