Comparing the predictive effects of three programming self-efficacy components on pre-service teachers’ intention to integrate computational thinking

This study compares the predictive effects of self-efficacy components (i.e., efficacy expectation, performance outcome expectation, and affective outcome expectation) on preservice teachers' intention to integrate Computational Thinking (CT) in their teaching. It also examines how block-based and robot programming tools impact preservice teachers' CT integration intention differently. The sample consisted of 106 preservice teachers enrolled in the online course “Computational Thinking in Education.” Hierarchical regression analysis considered pre-service teachers' age and technology competence as covariates. The findings revealed that overall, preservice teachers’ self-efficacy across both programming tools significantly predicted their intention to integrate CT into teaching. Specifically, programming affective outcome expectations related to both block-based programming (BBP) and robot programming (RP) tools emerged as significant predictors of CT integration intentions. Furthermore, the predictive power of overall RP self-efficacy exceeded that of BBP self-efficacy. These results indicate the importance of incorporating strategies that promote positive emotional engagement, in addition to fostering knowledge mastery, in teacher training programs. Moreover, RP appears to be a more effective medium for delivering CT training to preservice teachers.