Modeling the factors influencing high school students’ computational thinking in educational robotics–based programming learning activities: a multi-method model integrating CATLM, TAM, and TTF

Background and Context Traditional text and blockbased programming often limits the development of computational thinking (CT) skills, while educational robotics (ER) provides a handson alternative that can better foster these skills. However, how robotics environments promote CT remains unclear. This study focuses on Chinese high school students engaged in programming activities using ER.Objective This study aims to develop and validate an integrative model that explains learner-centered and technology-centered determinants of students’ CT skills.Method A cross‐sectional empirical study was conducted with 339 high school students who completed both a questionnaire and a programming fundamentals test. We applied Partial Least Squares Structural Equation Modeling (PLSSEM) to assess the measurement and structural models, Importance-Performance Matrix Analysis (IPMA) to identify priority factors, and Artificial Neural Network (ANN) analysis to capture non‐linear relationships and rank variable importance.Findings Task‐Technology fit (TTF), learning motivation (LM), cognitive engagement, and prior programming knowledge each exhibited significant positive effects on CT, with LM (β = 0.333, p < .001) and TTF (β = 0.251, p < .01) emerging as the most influential factors. The integrated model accounted for 54.1% of the variance in CT. IPMA and ANN results corroborated the centrality of motivation and fit in enhancing CT.Implications By extending the Cognitive Affective Theory of Multimedia Learning through incorporation of TAM and TTF constructs, this study highlights the dual importance of aligning robotics features with task requirements and fostering student motivation.