Scaling Generated Feedback for Novice Teachers by Sustaining Teacher Educators' Expertise: A Design to Train LLMs with Teacher Educator Endorsement of Generated Feedback
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3657604.3664677.pdf
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754.06 KB
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Adobe PDF
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Author(s)
Barno, Erin
Albaladejo-Gonz?lez, Mariano
Reich, Justin
Date Issued
July 15, 2024
Publisher
Association for Computing Machinery
Citation
Erin Barno, Mariano Albaladejo-González, and Justin Reich. 2024. Scaling Generated Feedback for Novice Teachers by Sustaining Teacher Educators' Expertise: A Design to Train LLMs with Teacher Educator Endorsement of Generated Feedback. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24). Association for Computing Machinery, New York, NY, USA, 412–416.
Version
Final published version
Abstract
When using simulations to design and implement novice teacher practice, a teacher educator may be concerned about if what is technically possible in terms of generating feedback to novice teachers' responses is educationally purposeful to support their learning. This paper details the design of infrastructure to incorporate user feedback within the Teacher Moments platform that is generated by an AI agent, and how we designed to sustain and scale the expertise of mathematics teacher educators when training a large language model. To best support the learning of novice mathematics teacher users to enact ambitious and equitable mathematics teaching, this paper explains the research design of training a large language model by collaborating with mathematics teacher educators to edit or endorse generated feedback across multiple training cycles. This paper also describes the UI design to explore potential of hosting such processes all within the Teacher Moments platform.
Description
L@S '24, July 18–20, 2024, Atlanta, GA, USA
MIT Department
Massachusetts Institute of Technology. Program in Comparative Media Studies/Writing
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3657604.3664677
