Development of a Deep Learning-Based E-Worksheet Integrated with Multiple Intelligences for Teaching the Fundamental Laws of Chemistry

Abstract

This study aimed to develop and evaluate the feasibility of an electronic student worksheet (E-Worksheet) designed by integrating Deep Learning principles and Multiple Intelligences theory into the teaching of the Fundamental Laws of Chemistry. The research employed a Research and Development (R&D) approach using the ADDIE model, focusing on the analysis, design, and development stages. A needs assessment was conducted through curriculum analysis, classroom observations, teacher interviews, and a review of relevant literature. The resulting product was an interactive digital worksheet that incorporated the principles of mindful, meaningful, and joyful learning while accommodating students’ diverse intelligence profiles. The feasibility of the product was assessed by two chemistry education experts and one chemistry teacher using expert validation instruments. The findings revealed validity scores of 0.921 for the content aspect and 0.909 for the media aspect, both of which fall within the high validity category. These results indicate that the developed E-Worksheet meets the required standards in terms of content quality, presentation, language use, and visual design. The product is characterized by an engaging visual interface, the integration of Deep Learning activities, the accommodation of various intelligence types, and learning tasks designed to foster creative thinking skills. Based on the validation results, the developed E-Worksheet is considered suitable for use in chemistry learning.