  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
interface for providing teachers with feedback on their use of talk moves
during individual teaching episodes. We present the series of model
architectures we developed, and the studies we conducted, to develop our
best-performing, transformer-based model (F1 = 79.3%). We also discuss several
technical challenges that need to be addressed when working with real-world
speech and language data from noisy K-12 classrooms.
