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dc.contributor.authorThomas, Danielle R.
dc.contributor.authorDemszky, Dorottya
dc.contributor.authorKoedinger, Kenneth R.
dc.contributor.authorMarland, Joshua
dc.contributor.authorPietrzak, Doug
dc.contributor.authorReich, Justin
dc.contributor.authorSlama, Rachel
dc.contributor.authorToutziaridi, Amalia
dc.contributor.authorKizilcec, Ren?
dc.date.accessioned2025-08-28T21:10:41Z
dc.date.available2025-08-28T21:10:41Z
dc.date.issued2025-07-17
dc.identifier.isbn979-8-4007-1291-3
dc.identifier.urihttps://hdl.handle.net/1721.1/162578
dc.descriptionL@S ’25, Palermo, Italyen_US
dc.description.abstractEffective teaching is among the most powerful influences on student learning, but scientific progress in understanding effective teaching moves has been held back by insufficient data on teaching. Despite extensive research efforts, progress is hindered by persistent challenges related to data de-identification and preprocessing, annotation and segmentation, multimodal analysis, predictive and causal modeling of student outcomes. Addressing these barriers requires a concerted, interdisciplinary approach. The National Tutoring Observatory (NTO) is a first-of-its-kind research infrastructure designed to unite researchers, developers, tutoring providers, and educational organizations in tackling common barriers to uncovering the dynamics of effective tutoring moves. The NTO is spearheading the creation of the Million Tutor Moves dataset, the largest open-access collection of tutoring interactions, leveraging artificial intelligence to unlock insights that accelerate the science of teaching at scale. This workshop aims to bring together the Learning at Scale community to share progress, identify common challenges, and explore collaborative solutions. The agenda will feature presentations of accepted papers, interactive demos, and a moderated panel bringing together researchers, developers, and tutoring providers. This workshop aims to advance a shared vision for uncovering the fundamental principles of impactful tutoring and teaching through the power of collaborative research and data-driven discovery.en_US
dc.publisherACM|Proceedings of the Twelfth ACM Conference on Learning @ Scaleen_US
dc.relation.isversionofhttps://doi.org/10.1145/3698205.3733961en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleAdvancing the Science of Teaching with Tutoring Data: A Collaborative Workshop with the National Tutoring Observatoryen_US
dc.typeArticleen_US
dc.identifier.citationanielle R. Thomas, Dorottya Demszky, Kenneth R. Koedinger, Joshua Marland, Doug Pietrzak, Justin Reich, Rachel Slama, Amalia Toutziaridi, and René F. Kizilcec. 2025. Advancing the Science of Teaching with Tutoring Data: A Collaborative Workshop with the National Tutoring Observatory. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S '25). Association for Computing Machinery, New York, NY, USA, 404–406.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Program in Comparative Media Studies/Writingen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2025-08-01T08:01:27Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2025-08-01T08:01:27Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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