Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion

Michael Ilagan, Beata Beigman Klebanov, Jamie Mikeska


Abstract
Leading students to engage in argumentation-focused discussions is a challenge for elementary school teachers, as doing so requires facilitating group discussions with student-to-student interaction. The Mystery Powder (MP) Task was designed to be used in online simulated classrooms to develop teachers’ skill in facilitating small group science discussions. In order to provide timely and scaleable feedback to teachers facilitating a discussion in the simulated classroom, we employ a hybrid modeling approach that successfully combines fine-tuned large language models with features capturing important elements of the discourse dynamic to evaluate MP discussion transcripts. To our knowledge, this is the first application of a hybrid model to automate evaluation of teacher discourse.
Anthology ID:
2024.bea-1.16
Volume:
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
182–198
Language:
URL:
https://aclanthology.org/2024.bea-1.16
DOI:
Bibkey:
Cite (ACL):
Michael Ilagan, Beata Beigman Klebanov, and Jamie Mikeska. 2024. Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 182–198, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion (Ilagan et al., BEA 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2024.bea-1.16.pdf