Towards Convergence: Characterizing Students’ Design Moves in Computational Modeling Through Log Data with Video and Cluster Analysis

Programming computational models is foundational for students to better understand scientific phenomena, mirroring the methods employed by real scientists. Previous research has used qualitative and quantitative methods to investigate engagement in computational practices through modeling. This paper compares two methods of using log data – alongside video analysis or through cluster analysis - to analyze sixth-grade students’ design moves in a block-based environment for science learning. We compare patterns identified through each approach and elaborate on their commonalities and differences. Our findings highlight how the methods add to each other, providing insights for future research at the intersection of computer science and science education.