Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/6577
Title: Advancing Computational Grounded Theory for Audiovisual Data from Mathematics Classrooms
Authors: D'Angelo, Cynthia
Dyer, Elizabeth
Krist, Christina
Rosenberg, Joshua
Bosch, Nigel
Keywords: Teaching and Teacher Learning
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: D'Angelo, C., Dyer, E., Krist, C., Rosenberg, J., & Bosch, N. (2020). Advancing Computational Grounded Theory for Audiovisual Data from Mathematics Classrooms. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 4 (pp. 2393-2394). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: This poster will discuss early findings from a project that is developing theory-based approaches to combine computational methods and qualitative grounded theory in order to analyze classroom video data of middle school mathematics classrooms. These early findings involve the feasibility of using out-of-the-box implementations of video and audio processing algorithms for analysis of video and audio data, focusing on methods to capture instances of collaboration and student–teacher interactions.
URI: https://doi.dx.org/10.22318/icls2020.2393
https://repository.isls.org//handle/1/6577
Appears in Collections:ICLS 2020

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