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|Title:||Advancing Computational Grounded Theory for Audiovisual Data from Mathematics Classrooms|
|Keywords:||Teaching and Teacher Learning|
|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.|
|Appears in Collections:||ICLS 2020|
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