Abstract

Objective: This study aimed to analyze students' response to the use of computational thinking from the perspective of computational tools and to analyze the influence of gender on students' computational thinking skills. Method: Research design using a comparative approach with data collection techniques involved a survey using a Likert scale questionnaire comprising 25 items, covering five dimensions of computational thinking skills: abstraction, decomposition, algorithm thinking, evaluation, and generalization. The study subjects involved five classes: physics, physics education, geography, mining engineering, and vocational-technical education, focusing on students' ability to analyze data using JASP and IBM SPSS. The data analyze methods included: (1). Comparative Analyze; (2). Correlation analyzes (Spearman); (3). Chi-square test. Finding: The results showed that the computational thinking skills of students from various classes varied, with significant correlations between the skill dimensions. Physics and Physics Education stood out with exemplary achievements, while Geography and Mining Engineering also showed good progress. The vocational-technical education program displayed nearly perfect correlations in all aspects of computational thinking skills. Meanwhile, from the gender aspect, gender significantly influenced computational thinking skills (Sig<0.00). The analyze highlighted the differences in computational thinking skills between classes and the significant influence of gender. Implication: This emphasized the importance of developing computational thinking skills in higher education and the need for inclusive approaches to enhance computational excellence among students. The implications of this study give valuable insights for improving the teaching of computational thinking in physics education. Steps that might be addressed include identifying and enhancing weak components, such as abstraction and generalization, and using particular tactics to increase students' knowledge.

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