Informing Expert Feature Engineering through Automated Approaches: Implications for Coding Qualitative Classroom Video Data

While classroom video data are detailed sources for mining student learning insights, their complex and unstructured nature makes them less than straightforward for researchers to analyze. In this paper, we compared the differences between the processes of expert-informed manual feature engineering and automated feature engineering using positional data for predicting student group interaction in four middle school and high school mathematics classroom videos. Our results highlighted notable differences, including improved model accuracy for the combined (manual features + automated features) models compared to the only-manual-features models (mean AUC = .778 vs. .706) at the cost of feature interpretability, increased number of features for automated feature engineering (1523 vs. 178), and engineering approach (domain-agnostic in automated vs. domain-knowledge-informed in manual). We carried out feature importance analyses and discuss the implications of the results for potentially augmenting human perspectives about qualitatively coding classroom video data by confirming and expanding views on which body areas and characteristics may be relevant to the target interaction behavior. Lastly, we discuss our study's limitations and future work.