Deep Learning-Based Digital Physics Comics for Fostering Critical Thinking in Alternative Energy Education
DOI:
https://doi.org/10.26740/jdpe.v2i2.55938Keywords:
Alternative Energy, Critical Thinking Skills, Deep Learning, Digital Physics ComicsAbstract
Objective: This study aimed to develop a Deep Learning-based Digital Physics Comic on alternative energy to improve senior high school students’ critical thinking skills. Method: This Research and Development (R&D) study adopted the ADDIE model, including analysis, design, development, implementation, and evaluation. The product was implemented with Grade X students at a senior high school in Sidoarjo, Indonesia, during the 2025/2026 academic year using a non-equivalent control group pretest–posttest design. Data were collected through expert validation sheets, learning implementation observations, critical thinking tests, and student response questionnaires. The media was evaluated for validity, practicality, and effectiveness. Results: The Digital Physics Comic achieved a validity score of 89.31% (very valid) and a learning implementation score of 91.67% (very practical). Student responses indicated high effectiveness (90.35%). The experimental class obtained a higher average N-Gain score (0.7332) than the control class (0.6209). The Mann–Whitney test showed a significant difference between the groups (p = 0.007 < 0.05), with the greatest improvement found in the inference indicator. Novelty: This study integrates a deep learning approach and Problem-Based Learning (PBL) into a digital physics comic on alternative energy. The media combines meaningful, mindful, and joyful learning through contextual stories, visual narratives, and problem-solving activities, offering an innovative approach to strengthening students’ conceptual understanding and critical thinking skills in physics.
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