Abstract

This presentation reports on a research project aimed at developing and validating an observational analytic tool to capture key data science practices in K–8 classroom instruction. The project responds to the growing integration of data science into school curricula and the corresponding need for rigorous instruments that can document how data science practices are enacted in authentic classroom settings. The development process follows a validation framework analogous to those used for previously established analytic tools and proceeds in three phases. The first phase involves a systematic review of data science standards and curriculum frameworks for the K–8 level and the construction of a database of classroom video lessons. The second phase uses content analysis of theoretical frameworks to identify and operationalize the key dimensions of data science instruction. The third phase tests and refines the instrument through an interactive validation process that incorporates expert review for content validity, inter-rater reliability training for rater validity, and correlation analysis of dimensions with overall scores and, where available, student achievement data. The anticipated outcome is a validated observational rubric that can serve both as a formative assessment tool for educators and as an analytic instrument for researchers studying data science instruction across diverse national and educational contexts.