# Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

arXiv: https://arxiv.org/abs/2208.04715 · HTML: https://mlsanity.com/paper/2208.04715/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 8 Jul 2022.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions](https://mlsanity.com/paper/2106.03873/) (2021)
- [Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards](https://mlsanity.com/paper/2005.04245/) (2020)
- [Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue](https://mlsanity.com/paper/1708.08572/) (2017)
- [And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue](https://mlsanity.com/paper/1709.05295/) (2017)

## Built on by (most cited first, 15 of 15)

- [The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts](https://mlsanity.com/paper/2211.11772/) (2022)
- [Evaluating Large Language Models in Analysing Classroom Dialogue](https://mlsanity.com/paper/2402.02380/) (2024)
- [Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging](https://mlsanity.com/paper/2406.11709/) (2024)
- [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)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [BIPED: Pedagogically Informed Tutoring System for ESL Education](https://mlsanity.com/paper/2406.03486/) (2024)
- [Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use](https://mlsanity.com/paper/2410.19231/) (2024)
- [Analyzing Large Language Models for Classroom Discussion Assessment](https://mlsanity.com/paper/2406.08680/) (2024)
- [Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations](https://mlsanity.com/paper/2506.03941/) (2025)
- [Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach](https://mlsanity.com/paper/2505.07902/) (2025)
- [Generation Space Size: Understanding and Calibrating Open-Endedness of LLM Generations](https://mlsanity.com/paper/2510.12699/) (2025)
- [EduCoder: An Open-Source Annotation System for Education Transcript Data](https://mlsanity.com/paper/2507.05385/) (2025)
- [From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding](https://mlsanity.com/paper/2511.20547/) (2025)
- [PIVOT: Preference-based Intervention Vectors for Pedagogical Tutor Steering](https://mlsanity.com/paper/2608.07509/) (2026)

## Abstract (arXiv)

Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might "funnel" students towards a normative answer or "focus" students to reflect on their own thinking, deepening their understanding of math concepts. When teachers focus, they treat students' contributions as resources for collective sensemaking, and thereby significantly improve students' achievement and confidence in mathematics. We propose the task of computationally detecting funneling and focusing questions in classroom discourse. We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither. We introduce supervised and unsupervised approaches to differentiating these questions. Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model's potential for use in automated teacher feedback tools. Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions. The high performance of the supervised measure indicates its promise for supporting teachers in their instruction.