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Advancing the Science of Teaching with Tutoring Data: A Collaborative Workshop with the National Tutoring Observatory

Author(s)
Thomas, Danielle R.; Demszky, Dorottya; Koedinger, Kenneth R.; Marland, Joshua; Pietrzak, Doug; Reich, Justin; Slama, Rachel; Toutziaridi, Amalia; Kizilcec, Ren?; ... Show more Show less
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Abstract
Effective 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.
Description
L@S ’25, Palermo, Italy
Date issued
2025-07-17
URI
https://hdl.handle.net/1721.1/162578
Department
Massachusetts Institute of Technology. Program in Comparative Media Studies/Writing; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
ACM|Proceedings of the Twelfth ACM Conference on Learning @ Scale
Citation
anielle 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.
Version: Final published version
ISBN
979-8-4007-1291-3

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