Computational Thinking and Deep Learning on Science Education Framework: A Systematic Review

Abstract

This research investigates the implementation and challenges of incorporating computational thinking (CT) into science education. This research aims to develop a comprehensive framework to improve computational thinking skills in science education through the implementation of a deep learning curriculum, also integrated with computer programming, which leads to enhancing analytical and problem-solving skills for students to tackle real-world problems. This research used a systematic literature review with the PRISMA technique. Data sources used in this research are indexed by the Crossref database and then analyzed using the VOSviewer program. By examining 133 articles included in total, the research identifies key factors that affect effective teaching in science classrooms to enhance computational thinking. Through a series of case studies and empirical analysis, the study highlights obstacles faced in this educational approach. Findings suggest that while deep learning and computer programming integrated into science classrooms can influence the improvement of computational thinking skills and students’ understanding of scientific concepts and their application, challenges such as limited resources are addressed. The proposed framework offers practical strategies for policymakers and educators, especially science educators, in designing learning to overcome these challenges, aiming to prepare students for a technology-driven future better.