Are affective factors a good predictor of computational thinking? Examining the role of affective factors based on a conceptual model.
Abstract: Although computer science (CS) is becoming a part of a regular curriculum in both K-12 and higher education, affective factors influencing computational thinking (CT) and problem-solving skills have yet to be fully explored. The paper proposed a conceptual model to predict: (a) four affective factors (i.e., attitude toward computer science, perceived usefulness of computer science, attitude toward programming, and programming self-efficacy) which influence computational thinking self-efficacy and (b) six CT components (i.e., decomposition, pattern recognition, planning, algorithm, reusing, and debugging) which affect problem-solving self-efficacy. Based on the model, the current study identified that computational thinking self-efficacy affected problem-solving self-efficacy as well. Structural equation modeling was used to analyze self-report data from a total of 69 college students to examine the direct relationships among study variables. The findings showed that two affective factors (i.e., programming self-efficacy and perceived usefulness of computer science) significantly predicted computational thinking self-efficacy, and it significantly influenced problem-solving self-efficacy. Also, two computational thinking components (i.e., algorithm and debugging) were identified as the significant determinants of problem-solving self-efficacy. The results validated the impact of affective factors on CS education and suggest CT activities to facilitate problem-solving skills.