M-Powering Teachers: A Machine Learning Tool for Instructional Measurement and Feedback
Joined 2022
Partners
Stanford University
Harvard University
What are we building?
An automated feedback tool using speech recognition to analyze classroom interactions
A high-quality benchmark dataset of math instructional sessions
A system to deliver formative feedback to teachers based on verbal practices
Infrastructure for research using recordings and automated tools
What are we learning?
How to fine-tune automatic speech recognition (ASR) models for instructional settings
How to analyze classroom discourse to develop measures of instructional talk and feedback equity
How to deliver effective automated feedback through experiments, interviews, and coaching collaborations
Products
Automated instructional feedback tool
Benchmark dataset of instructional audio/video
Research findings on feedback practices and instructional equity