Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application
Abhijit Suresh, Jennifer Jacobs, Vivian Lai, Chenhao Tan, Wayne Ward,, James H. Martin, Tamara Sumner

TL;DR
This paper introduces TalkMoves, a transformer-based application that analyzes classroom recordings to give teachers personalized feedback on discourse strategies, aiming to improve mathematics instruction and student engagement.
Contribution
The paper presents a novel transformer-based model and a cloud infrastructure for analyzing classroom speech and providing targeted feedback to teachers.
Findings
Achieved a model F1 score of 79.3% in identifying talk moves.
Developed a cloud-based system for processing noisy classroom audio.
Addressed technical challenges in real-world speech data analysis.
Abstract
TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as "talk moves" within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application's cloud-based infrastructure for managing and processing classroom recordings, and its…
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Taxonomy
TopicsInnovative Teaching and Learning Methods · Natural Language Processing Techniques · Software Engineering Research
