ECR Methods DCL: Advancing Computational Grounded Theory for Audiovisual Data from STEM Classrooms

Video data are complex. They involve visual, acoustic, spatial, and temporal features that can be reduced in several ways. To date, analysis of video data of STEM classrooms has not been able to leverage computational power to take advantage of their richness. However, recent advancements in data science, coupled with existing speech analytics methods, make it possible to computationally identify important features from video in ways that preserve complexity and nuance. These advancements will improve research replicability. The methods developed through this project will facilitate use of sophisticated computational analysis with video data by more researchers. Application of these new methods will help increase the scale and generalizability of video research and lead to the building of new theory.