Examination of the Computer Programming Self-Efficacy’s Prediction towards the Computational Thinking Skills of the Gifted and Talented Students

Abstract

The study's goal was to examine the correlation between the computer programming self-efficacy and computational thinking skills of gifted and talented students. The capacity of the computer programming self-efficacy of gifted and talented students to predict their computational thinking skills were also examined. The relational screening model has been implemented in the research. The participants of the study were composed of 106 secondary school gifted and talented students studying the Individual Ability Recognition Program (IAR) at the Science and Art Center in the city center. Typical case sampling was applied for the student identification of the participants, 46 are female and 60 are male. Gifted and talented students' computational thinking skills were assessed using the "Computational Thinking Skills Scale” and the computer programming self-efficacy was measured by using the "Computer Programming Self-Efficacy Scale". Data were analysed by Pearson correlation analysis and simple linear regression analysis in statistical software SPSS 22. Research results found that there was a positive and high correlation between the computer programming self-efficacy and computational thinking skills. The gifted and talented students' computer programming self-efficacy demonstrated 31.5% of the total variance in computational thinking skills. This finding supports the claim which is present in the literature that self-efficacy in computer programming is the affective aspect of computational thinking skills. To predict computational thinking skills, it may be recommended to build multiple models for cognitive and affective skills of gifted and talented students.