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

arXiv: https://arxiv.org/abs/2204.09652 · HTML: https://mlsanity.com/paper/2204.09652/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions](https://mlsanity.com/paper/2106.03873/) (2021)
- [Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application](https://mlsanity.com/paper/2105.07949/) (2021)
- [What Would a Teacher Do? Predicting Future Talk Moves](https://mlsanity.com/paper/2106.05249/) (2021)

## Built on by (most cited first, 30 of 38)

- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts](https://mlsanity.com/paper/2211.11772/) (2022)
- [MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education](https://mlsanity.com/paper/2404.06711/) (2024)
- [Opportunities and Challenges in Neural Dialog Tutoring](https://mlsanity.com/paper/2301.09919/) (2023)
- [Evaluating Large Language Models in Analysing Classroom Dialogue](https://mlsanity.com/paper/2402.02380/) (2024)
- [Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions](https://mlsanity.com/paper/2404.03429/) (2024)
- [Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation](https://mlsanity.com/paper/2302.13496/) (2023)
- [The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Course](https://mlsanity.com/paper/2503.07928/) (2025)
- [Towards Goal-oriented Intelligent Tutoring Systems in Online Education](https://mlsanity.com/paper/2312.10053/) (2023)
- [BIPED: Pedagogically Informed Tutoring System for ESL Education](https://mlsanity.com/paper/2406.03486/) (2024)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use](https://mlsanity.com/paper/2410.19231/) (2024)
- [Pedagogy-driven Evaluation of Generative AI-powered Intelligent Tutoring Systems](https://mlsanity.com/paper/2510.22581/) (2025)
- [Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues](https://mlsanity.com/paper/2507.06910/) (2025)
- [SEFL: A Framework for Generating Synthetic Educational Assignment Feedback with LLM Agents](https://mlsanity.com/paper/2502.12927/) (2025)
- [AI Annotation Orchestration: Evaluating LLM verifiers to Improve the Quality of LLM Annotations in Learning Analytics](https://mlsanity.com/paper/2511.09785/) (2025)
- [Modeling the Quality of Dialogical Explanations](https://mlsanity.com/paper/2403.00662/) (2024)
- [Towards Actionable Pedagogical Feedback: A Multi-Perspective Analysis of Mathematics Teaching and Tutoring Dialogue](https://mlsanity.com/paper/2505.07161/) (2025)
- [Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach](https://mlsanity.com/paper/2505.07902/) (2025)
- [How well do Large Language Models Recognize Instructional Moves? Establishing Baselines for Foundation Models in Educational Discourse](https://mlsanity.com/paper/2512.19903/) (2025)
- [LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse](https://mlsanity.com/paper/2602.09832/) (2026)
- [HACHIMI: Scalable and Controllable Student Persona Generation via Orchestrated Agents](https://mlsanity.com/paper/2603.04855/) (2026)
- [Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse](https://mlsanity.com/paper/2412.13395/) (2024)
- [Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home](https://mlsanity.com/paper/2306.00482/) (2023)
- [Educator Attention: How computational tools can systematically identify the distribution of a key resource for students](https://mlsanity.com/paper/2502.20135/) (2025)
- [EduCoder: An Open-Source Annotation System for Education Transcript Data](https://mlsanity.com/paper/2507.05385/) (2025)
- [EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus](https://mlsanity.com/paper/2510.12899/) (2025)
- [Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles](https://mlsanity.com/paper/2512.20780/) (2025)
- [Codebook-Injected Dialogue Segmentation for Multi-Utterance Constructs Annotation: LLM-Assisted and Gold-Label-Free Evaluation](https://mlsanity.com/paper/2601.12061/) (2026)
- [Tutor Move Taxonomy: A Theory-Aligned Framework for Analyzing Instructional Moves in Tutoring](https://mlsanity.com/paper/2603.05778/) (2026)

## Abstract (arXiv)

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.