Md. Navri Zulirfan
Universitas Negeri Padang

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Development of Digital Teaching Modules to Enhance Differentiated Learning in Geography Subjects Md. Navri Zulirfan; Syafri Anwar; Dedi Hermon; Bayu Wijayanto
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 4 (2025): DECEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i4.8041

Abstract

Differentiated learning in geography education requires innovative digital resources that cater to diverse student learning styles. This study aimed to design, validate, and evaluate the effectiveness of a digital teaching module that supports differentiated instruction based on visual, auditory, and kinesthetic learning preferences for Phase E students at SMAN 2 Sungai Apit. This research employed a Research and Development (RD) approach using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The product was validated by three experts (subject matter, media, and instructional design), tested for practicality through small and large group trials, and evaluated for effectiveness through pretest-posttest experimental design involving 63 students divided into experimental and control groups. Validation results showed the module was highly valid, with expert ratings of 91.5% (content), 90.15% (media), and 83.25% (instructional design). Practicality scores from student trials reached 81.35% (small group) and 83.21% (limited group), both classified as "very practical." The module's effectiveness was confirmed through a significant difference in posttest scores between the experimental group (M = 80.21) and the control group (M = 73.03), with p 0.001. The average N-Gain score for the experimental group was 66.45%, indicating moderate improvement. These findings suggest that the developed digital teaching module is a valid, practical, and effective tool to enhance differentiated learning in geography. Its structure supports personalized learning experiences and can serve as a scalable model for other contexts.