  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.
