Exploring Computational Thinking in Physics Education: An AI-Driven Analysis of Decomposition and Abstraction Challenges
DOI:
https://doi.org/10.61987/ijai.v1i1.664Keywords:
Computational Thinking, Physics Education, Vector Material, Physics Students, Higher EducationAbstract
The importance of Computational Thinking (CT) ability is not only one of the essential competencies of the 21st century but also a foundation for solving complex problems and building high-level thinking skills. This study aims to explore the computational thinking (CT) ability of new students in the physics education study program on vector material. The research method used is descriptive qualitative, involving eight new physics education students at the Islamic University of Madura as respondents. Data were collected through a test instrument, and each question refers to the computational thinking indicator (Decomposition, abstraction, algorithm, and generalization). The results of data analysis show that the indicators of students' CT abilities on a scale of 4 consist of decomposition with an average value of 3.125, abstraction 2.438, algorithm 2.125, and generalization 1.687. Based on the study results, it can be concluded that the CT skills of first-year students at the Islamic University of Madura on vector material are included in the moderate category. Several indicators of CT abilities are still challenging for students, especially in developing algorithms and generalizations. Therefore, further research is needed to improve students' computational thinking abilities to face the challenges of the 21st century.
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