Automating analysis and feedback to improve mathematics teachers' classroom discourse

Abstract

Our work builds on advances in deep learning for natural language processing to automatically analyze transcribed classroom discourse and reliably generate information about teachers' uses of specific discursive strategies called”talk moves.” Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Currently, providing teachers with detailed feedback about the talk moves in their lessons requires highly trained observers to hand code transcripts of classroom recordings and analyze talk moves and/or one-on-one expert coaching, a time-consuming and expensive process that is unlikely to scale. We created a bidirectional long short-term memory (bi-LSTM) network that can automate the annotation process. We have demonstrated the feasibility of this deep learning approach to reliably identify a set of teacher talk moves at the sentence level with an F1 measure of 65%.