Scaling Generated Feedback for Novice Teachers by Sustaining Teacher Educators' Expertise: A Design to Train LLMs with Teacher Educator Endorsement of Generated Feedback
Pages 412 - 416
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.
References
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Index Terms
- 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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Published In
July 2024
582 pages
ISBN:9798400706332
DOI:10.1145/3657604
- General Chair:
- David Joyner,
- Program Chairs:
- Min Kyu Kim,
- Xu Wang,
- Meng Xia
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 15 July 2024
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