Improving the Computational Thinking Abilities of Junior High School Students Through Problem-Based Learning

Abstract

Computational Thinking is a crucial skill that students must master in the 21st century. Computational Thinking encompasses a systematic process that enables individuals to formulate problems, develop strategies, and find effective, efficient, and reusable solutions. This research aims to enhance students' Computational Thinking abilities through the application of the Problem-Based Learning (PBL) model, which is effective in facilitating active learning and problem-solving. The methodology used in this study is Classroom Action Research (CAR) with a spiral approach developed by Stephen Kemmis and Robin McTaggart. Data collection is conducted through post-tests administered at the end of each cycle phase. The obtained data are analyzed using a designed assessment rubric, wherein students are categorized based on their Computational Thinking abilities and foundational Computational Thinking skills. The results indicate that by the end of Cycle II, of students achieved a moderate to high level of Computational Thinking ability. These findings affirm that the implementation of the Problem-Based Learning learning model can significantly improve students' Computational Thinking skills.