Implementation of Deep Learning–Based Instruction in the Farming Method to Optimize Young Children’s Naturalistic Intelligence (Faculty of Teacher Training and Education Muhammadiyah University of Makassar, 2026)
Objective: This study aims to examine the effectiveness of the farming method based on deep learning in developing naturalistic intelligence among early childhood learners. Method: The research employed a Classroom Action Research (CAR) design conducted in one cycle with several learning activities such as planting water spinach, understanding the phenomenon of tsunamis, observing clouds, and studying the process of rainfall. Data were collected through systematic observations using indicators of interest, knowledge, skills, and environmental awareness. Results: The findings show a notable improvement in children’s naturalistic intelligence after the implementation of the method. Children who initially showed greater interest in artificial objects demonstrated increased engagement in farming activities, improved understanding of natural phenomena, and heightened empathy and environmental care. Novelty: This study highlights a unique integration of deep learning–based instruction within the farming method, where learning activities are deliberately structured to promote conceptual understanding, active meaning-making, reflection, and the application of knowledge, core principles of deep learning pedagogy. By aligning experiential farming activities with these instructional components, the approach goes beyond routine hands-on learning and supports children in connecting natural phenomena with prior knowledge, reasoning, and environmental values. This integration offers an innovative and theoretically grounded strategy to cultivate naturalistic intelligence in early childhood settings, providing a practical model for teachers seeking meaningful experiential learning that fosters cognitive depth and environmental awareness.