Implementation of Computational Thinking in Physics Learning to Strengthen Analytical and Logical Reasoning

This study explores the implementation of Computational Thinking (CT) in physics learning to enhance students' analytical and logical reasoning. Using an experimental approach with a problem-solving model, the research involved students enrolled in physics courses, divided into experimental and control groups. The experimental group engaged in simulation-based and algorithmic problem-solving tasks, while the control group received conventional instruction. The research focused on assessing improvements in logical reasoning through pre- and post-tests. Data analysis was conducted quantitatively, comparing the pre-test and post-test results of both groups. Findings revealed that the experimental group showed a significant 31% improvement in logical reasoning skills, while the control group demonstrated a modest 5.8% improvement. This indicates that CT-based activities, such as simulations and algorithmic tasks, are more effective in enhancing students' analytical and problem-solving abilities compared to traditional methods. The study highlights the importance of integrating CT into physics education, as it promotes deeper cognitive engagement, critical thinking, and practical problem-solving skills. These findings suggest that CT strategies can be a powerful tool in improving students' reasoning abilities and better preparing them for complex scientific and real-world challenges.