Implementasi Model PBL Terintegrasi STEM-EDP dengan Prinsip Deep Learning untuk Meningkatkan Computational Thinking Peserta Didik - Melati, Indah Sukma; Abdurrahman, Abdurrahman; Viyanti, Viyanti; Herlina, Kartini (Program Studi Pendidikan Fisika FKIP Universitas Jambi, 2026)
This research evaluated whether a Problem-Based Learning (PBL) model combining STEM-EDP and Deep Learning principles could strengthen students’ computational thinking in sound-wave instruction. A quasi-experimental pretest-posttest control group design was implemented at YP Unila High School, with class XI-6 assigned as the experimental class and XI-9 as the control class. Computational thinking was measured through essay questions. The experimental class achieved an average N-gain of 0.54, compared with 0.37 in the control class. The Independent Sample T-Test produced Sig. (2-tailed) < 0.05, while ANCOVA also yielded Sig. < 0.05 and a partial eta-squared value of 0.362, categorized as a large effect. These results show that integrating PBL, STEM-EDP, and Deep Learning principles effectively improved computational thinking. The contribution of the study lies in demonstrating how the three components can operate simultaneously in sound-wave learning: PBL establishes an authentic problem, STEM-EDP structures the engineering response, and Deep Learning principles maintain meaningful reflection. The design offers physics teachers and curriculum developers a reproducible option for strengthening computational thinking within Merdeka Belajar through engineering tasks connected to local contexts.