Teacher needs analysis for a STEAM-based renewable energy electronics module integrating deep learning to enhance collaborative problem-solving and entrepreneurship skills - Mirantika, Rizki; Abdurrahman, Abdurrahman; Viyanti, Viyanti (Indonesian Journal of Science and Mathematics Education, 2026)
Renewable energy learning requires integrating STEAM approaches and deep learning principles to support students’ collaborative problem-solving and entrepreneurial skills. However, school learning practices show a gap between these demands and the availability of supporting teaching materials. This study aims to analyze teachers’ needs in developing STEAM-based renewable energy e-modules that integrate deep learning principles. This study used a mixed-methods exploratory sequential design. Quantitative data were obtained through a survey of 35 high school physics teachers, followed by in-depth interviews with three selected teachers. Quantitative data were analyzed descriptively, while qualitative data were analyzed using thematic analysis. The survey results showed that 97.1% of teachers reported that learning media were not integrated with STEAM principles and deep learning. In addition, more than 80% of teachers reported difficulties in assessing students’ collaborative problem-solving and entrepreneurial skills. Qualitative findings revealed teachers’ limited understanding of deep learning, partial implementation of STEAM, and the need for structured digital teaching materials. The findings suggest that developing STEAM-based renewable energy e-modules aligned with teachers’ authentic needs is essential for strengthening contextual and skills-oriented physics learning in the 21st century.