Automatic Teacher Modeling from Live Classroom Audio

Resource type
Conference Paper
Authors/contributors
Title
Automatic Teacher Modeling from Live Classroom Audio
Abstract
We investigate automatic analysis of teachers' instructional strategies from audio recordings collected in live classrooms. We collected a data set of teacher audio and human-coded instructional activities (e.g., lecture, question and answer, group work) in 76 middle school literature, language arts, and civics classes from eleven teachers across six schools. We automatically segment teacher audio to analyze speech vs. rest patterns, generate automatic transcripts of the teachers' speech to extract natural language features, and compute low-level acoustic features. We train supervised machine learning models to identify occurrences of five key instructional segments (Question & Answer, Procedures and Directions, Supervised Seatwork, Small Group Work, and Lecture) that collectively comprise 76% of the data. Models are validated independently of teacher in order to increase generalizability to new teachers from the same sample. We were able to identify the five instructional segments above chance levels with F1 scores ranging from 0.64 to 0.78. We discuss key findings in the context of teacher modeling for formative assessment and professional development.
Date
July 13, 2016
Proceedings Title
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization
Place
New York, NY, USA
Publisher
Association for Computing Machinery
Pages
45–53
Series
UMAP '16
ISBN
978-1-4503-4368-8
Accessed
2021-03-07
Library Catalogue
ACM Digital Library
Citation
Donnelly, P. J., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., Kelly, S., Nystran, M., & D’Mello, S. K. (2016). Automatic Teacher Modeling from Live Classroom Audio. Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, 45–53. https://doi.org/10.1145/2930238.2930250