Developing Local Wisdom-Based Fraction Teaching Materials Through a Deep Learning Approach

Abstract

Fraction learning in elementary schools is often presented procedurally and lacks meaningful connections to students’ daily experiences, resulting in limited conceptual understanding. This study aimed to develop and evaluate local wisdom-based teaching materials using a deep learning approach to enhance fourth-grade students’ understanding of fraction concepts. The research employed a Research and Development (R&D) design following the ADDIE model: analysis, design, development, implementation, and evaluation. The teaching materials integrated local cultural contexts, particularly traditional regional foods and everyday activities, to provide authentic and meaningful learning experiences. Validation by media and content experts indicated that the materials were highly feasible for classroom use. The effectiveness of the product was tested using a one-group pretest–posttest design involving 50 students. The results showed a significant improvement in students’ learning outcomes, with the mean score increasing from 54.04 to 77.12 and an average N-gain of 0.545, categorized as medium improvement. These findings suggest that integrating local wisdom with a structured deep learning approach effectively strengthens students’ conceptual understanding of fractions while promoting contextual and engaging mathematics learning.