# Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application

arXiv: https://arxiv.org/abs/2105.07949 · HTML: https://mlsanity.com/paper/2105.07949/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)

## Built on by (most cited first, 10 of 10)

- [The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves](https://mlsanity.com/paper/2204.09652/) (2022)
- [Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction](https://mlsanity.com/paper/2306.03090/) (2023)
- [Edu-ConvoKit: An Open-Source Library for Education Conversation Data](https://mlsanity.com/paper/2402.05111/) (2024)
- [The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education](https://mlsanity.com/paper/2404.02444/) (2024)
- [Automated Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting Global Predictions to Specific Feedback](https://mlsanity.com/paper/2310.01132/) (2023)
- [Analyzing Large Language Models for Classroom Discussion Assessment](https://mlsanity.com/paper/2406.08680/) (2024)
- [From Robots to Books: An Introduction to Smart Applications of AI in Education (AIEd)](https://mlsanity.com/paper/2301.10026/) (2023)
- ["Mistakes Help Us Grow": Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms](https://mlsanity.com/paper/2310.10637/) (2023)
- [Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers](https://mlsanity.com/paper/2407.01274/) (2024)
- [Multi-Stage Speaker Diarization for Noisy Classrooms](https://mlsanity.com/paper/2505.10879/) (2025)

## Abstract (arXiv)

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.