Integration of the Steam Approach in Deep Learning to Stimulate Students' Critical Thinking

Abstract

This quantitative study examines the impact of integrating STEAM (Science, Technology, Engineering, Arts, and Mathematics) approaches into deep learning models to enhance students' critical thinking skills. A quasi-experimental design was employed with 120 high school students (60 experimental, 60 control) using pre-test/post-test measures based on the Cornell Critical Thinking Test (CCTT). Results indicated a statistically significant improvement in critical thinking scores (p < 0.05, Cohen’s d = 0.89) among students exposed to STEAM-deep learning modules. Qualitative analysis of project artifacts further revealed enhanced problem-solving and creativity. The study underscores the efficacy of interdisciplinary STEAM pedagogy in fostering 21st-century skills