Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

TL;DR

This paper develops a computational method to identify funneling and focusing questions in classroom discourse, using a new annotated dataset and machine learning models, to enhance teacher feedback and improve math instruction.

Contribution

It introduces the first dataset and models for detecting funneling and focusing questions in classroom talk, demonstrating high accuracy and educational relevance.

Findings

Supervised RoBERTa model correlates .76 with human labels and positive outcomes.

Unsupervised measures show weaker but significant correlations.

Model performance suggests potential for automated teacher feedback tools.

Abstract

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.