Jatmiko Wahyu Nugroho; Dina Apryani; Ayla Anar Babayeva
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.