Using deep learning to automatically detect talk moves in teachers'mathematics lessons
Abhijit Suresh, Tamara Sumner, Isabella Huang, Jennifer Jacobs, Bill Foland, Wayne Ward · 2018
Currently, providing teachers with detailed feedback about their classroom discourse strategies requires highly trained observers to hand code transcripts of classroom recordings to identify talk moves and/or one-on-one expert coaching. Both approaches are time-consuming and expensive, require considerable human expertise, and do not scale to large numbers of teachers. We are currently developing an innovative application, the TalkBack application, a new type of teacher learning environment based on the automated analysis of classroom recordings. The TalkBack application will utilize a big data infrastructure for managing and analyzing classroom recordings, including an embedded automated talk move classifier. The application will provide teachers with a detailed record of the discourse strategies used in their lessons. A central premise of our research is that this type of personalized, automated feedback can dramatically enhance teacher learning and support improvements in their instruction.The project will exemplify how next-generation repositories of classroom recordings can be architected to support large-scale research by enabling automated analyses based on machine learning models.