The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves

TL;DR

The TalkMoves dataset offers a comprehensive collection of annotated K-12 mathematics lesson transcripts, enabling analysis of discourse patterns to improve teaching practices and student engagement.

Contribution

This paper introduces a novel, richly annotated dataset of classroom discourse transcripts, combining speaker segmentation, discursive move labels, and dialogue acts for educational research.

Findings

Dataset includes 567 transcripts with detailed annotations

Portions of the dataset have been used to develop an automated feedback tool

Supports research on discourse patterns in K-12 mathematics classrooms

Abstract

Transcripts of teaching episodes can be effective tools to understand discourse patterns in classroom instruction. According to most educational experts, sustained classroom discourse is a critical component of equitable, engaging, and rich learning environments for students. This paper describes the TalkMoves dataset, composed of 567 human-annotated K-12 mathematics lesson transcripts (including entire lessons or portions of lessons) derived from video recordings. The set of transcripts primarily includes in-person lessons with whole-class discussions and/or small group work, as well as some online lessons. All of the transcripts are human-transcribed, segmented by the speaker (teacher or student), and annotated at the sentence level for ten discursive moves based on accountable talk theory. In addition, the transcripts include utterance-level information in the form of dialogue act labels based on the Switchboard Dialog Act Corpus. The dataset can be used by educators, policymakers, and researchers to understand the nature of teacher and student discourse in K-12 math classrooms. Portions of this dataset have been used to develop the TalkMoves application, which provides teachers with automated, immediate, and actionable feedback about their mathematics instruction.