Where Socioeconomic Differences in Computational Thinking Become Visible: Integrating Diagnostic and Log-Based Behavioral Assessment

This study examines where socioeconomic differences in students’ computational thinking (CT) learning become visible by comparing a diagnostic assessment of conceptual CT knowledge with behavioral indicators derived from interaction data in a digital programming environment. The study involved 444 elementary school students who completed a structured sequence of programming tasks while their activity was recorded. Conceptual CT knowledge was assessed using a validated diagnostic instrument, and four behavioral indicators were derived from learning logs: average first-try stars, attempts per challenge, highest challenge reached, and average solution time. Analyses were conducted at two complementary levels: individual indicators and integrated digital behavioral types identified through clustering. The findings revealed no meaningful socioeconomic differences in diagnostic CT performance and no consistent differences across most individual behavioral indicators, with the exception of average first-try stars. However, socioeconomic differences became visible when students’ interaction patterns were examined as multidimensional configurations of engagement. These results suggest that socioeconomic variation is reflected primarily in students’ engagement with digital problem-solving processes rather than in conceptual knowledge alone. The study highlights the value of combining diagnostic and log-based measures for understanding how educational inequality may become observable in computational thinking development.