Computationally Identifying Funneling and Focusing Questions in Classroom Discourse
Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill, Dan Jurafsky
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 depending 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.- Anthology ID:
- 2022.bea-1.27
- Volume:
- Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 224–233
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.bea-1.27/
- DOI:
- 10.18653/v1/2022.bea-1.27
- Cite (ACL):
- Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill, and Dan Jurafsky. 2022. Computationally Identifying Funneling and Focusing Questions in Classroom Discourse. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 224–233, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Computationally Identifying Funneling and Focusing Questions in Classroom Discourse (Alic et al., BEA 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.bea-1.27.pdf
- Code
- sterlingalic/funneling-focusing