Digital Instructional Kits of STEM based on Deep Learning to Improve University Students' HOTS on the Topic of Environmental Pollution

Environmental education, particularly regarding the topic of environmental pollution, needs to be enhanced. This aligns with advancements in science and technology that prioritize the Science, Technology, Engineering, and Mathematics (STEM) approach. The objective of this study was to develop a learning tool—specifically, a deep learning-based Digital Instructional Kits of STEM (DIKSTEM)—to improve university students' Higher-Order Thinking Skills (HOTS) concerning environmental pollution. The study employed a Research and Development (R&D) method based on the Analyze, Design, Develop, Implement, and Evaluate (ADDIE) framework. The results indicate that the developed DIKSTEM tool is highly valid and suitable for use in instruction. Innovations in developing such learning tools should be accompanied by integration with other media, learning models, and student worksheets. These innovations can be applied to models or worksheets designed to foster improvements in students' HOTS. Future research should focus on the broader implementation of the DIKSTEM.