An Investigation of Affective Factors Influencing Computational Thinking and Problem-Solving

This study investigated the effects of affective factors on computational thinking and problem-solving. Computer science subjects are becoming part of the regular curricula in K-12 and higher education to enhance computational problem-solving skills. However, affective factors influencing computational thinking skills and computational thinking components predicting problem-solving skills have yet to be fully explored. This paper proposed a conceptual model to predict (a) four affective factors that influence computational thinking self-efficacy and (b) six computational thinking components that affect problem-solving self-efficacy. Structural equation modeling was used to analyze self-report data from college students to examine the direct relationships among study variables. The findings showed that two affective factors (i.e., programming self-efficacy and computer science usefulness) significantly predicted computational thinking self-efficacy and influenced problem-solving self-efficacy. Also, two computational thinking components (i.e., algorithm and debugging) were the significant determinants of problem-solving self-efficacy. The results validate the importance of affective factors in computer science education and suggest specific computational thinking activities that should be emphasized in computer science curricula to facilitate problem-solving skills.