Class 5.0

PI: Andrew Olney
Co-PIs: Art Graesser, Sidney D'Mello

Class 5.0For over a century, research has documented the dominant configuration of lecture, recitation, and seatwork in American schools. Recent research looking at the role of classroom discourse, i.e., interactions between teachers and students, has confirmed, as an alternative to this configuration, the importance of open discussions prompted by open-ended teacher questions ("authentic teacher questions") in reading and literature instruction. The goal of our project, using cutting-edge research in speech recognition, discourse classification, and natural language understanding (NLU), is to develop CLASS 5.0, a computer program that will autonomously code classroom interactions between teachers and their students. CLASS 5.0 will radically simplify and accommodate automated coding and assessment of classroom discourse, which, in turn, could revolutionize classroom research.

Funding:

  • Automating the Measurement and Assessment of Classroom Discourse. Funding Agency: IES (subcontract from University of Wisconsin - Madison; Martin Nystrand is PI). $523,422.

Selected Publications:

  • Blanchard, N., Donnelly, P., Olney, A., Samei, B., Ward, B., Sun, X., Kelly, S., Nystrand, M., and D'Mello, S. (2016). Identifying Teacher Questions using Automatic Speech Recognition in Live Classrooms. Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2016) (pp. 191-201). Association for Computational Linguistics.
  • Donnelly, P., Blanchard, N., Samei, B., Olney, A., Sun, X., Ward, B., Kelly, S., Nystrand, N., and D'Mello, S. (2016). Automatic Teacher Modeling from Live Classroom Audio. Proceedings of the 2016 ACM on International Conference on User Modeling, Adaptation, & Personalization (UMAP 2016) (pp. 45-53). ACM: New York.
  • Blanchard, N., Brady, M., Olney, A.M., Glaus, M., Sun, X., Nystrand, M., Samei, B., Kelly, S., and D'Mello, S. (2015). A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis. Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015) (pp. 23-33). C. Conati, et al., Editors. Springer-Verlag: Berlin Heidelberg. 
  • D'Mello, S., Olney, A, Blanchard, N., Samei, B.,Ward, B., and Kelly, S. (2015). Multimodal Capture of Teacher-Student Interactions for Automated Dialogic Analysis in Live Classrooms. Proceedings of the 2015 International Conference on Multimodal Interaction (ICMI 2015) (pp. 557-566). ACM: New York. 
  • Samei, B., Olney, A., Kelly, S., Nystrand, M., D'Mello, S., Blanchard, N., and Graesser, A. (2015). Modeling Classroom Discourse: Do Models that Predict Dialogic Instruction Properties Generalize across Populations? Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 444-447). C. Romero, et al., Editors. International Educational Data Mining Society.
  • Samei, B., Olney, A., Kelly, S., Nystrand, M., D'Mello, S., Blanchard, N., Sun, X., Glaus, M., and Graesser, A. (2014). Domain Independent Assessment of Dialogic Properties of Classroom Discourse. Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 233-236). J. Stamper, et al., Editors. International Educational Data Mining Society.