Needs Analysis for a Renewable Energy E-Module Based on Deep Learning Approach and Local Wisdom Assisted by Canva AI to Facilitate Students' Creative Thinking Skills
DOI:
https://doi.org/10.31851/Abstract
Creative thinking skills are an important 21st-century competency that needs to be developed in physics learning. However, mastery of this ability is still relatively low because the teaching materials used have not been designed to facilitate students to think deeply, contextually, and creatively. This study aims to analyze the design needs of renewable energy e-modules based on deep learning and Minangkabau local wisdom assisted by Canva AI to facilitate students' creative thinking skills. The study used a 4D Research and Development (R&D) model approach (Define, Design, Develop, and Disseminate) which was limited to the Define stage, conducted at SMAN 15 Padang involving 34 grade X Phase E students and physics teachers. Data were collected through creative thinking skills tests, needs questionnaires, observations, and interviews. Quantitative data were analyzed using descriptive statistics, while qualitative data were analyzed through data reduction, data presentation, and drawing conclusions. The results of the study revealed three main findings: (1) students' creative thinking skills were in the very low category with an average of 34.38%; (2) the teaching materials used have not integrated deep learning approaches, local wisdom, or artificial intelligence technology so that students' opportunities to develop creativity are still limited; (3) as many as 78.67% of students need digital open materials that are more interactive, interesting, and easily accessible. In conclusion, the development of renewable energy e-modules based on deep learning and Minangkabau local wisdom assisted by Canva AI is very necessary to support meaningful physics learning while facilitating students' creative thinking skills.
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