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Alternative Assessment Strategies and Accommodative Teaching Module Design: A Case Study of Deep Learning Implementation in Inclusive Education Schools Tarsidi, Iding; Homdijah, Oom Sitti; Rochyadi, Endang; Santoso, Yoga Budhi; Nitya, Nita; Fatimatuzzahra, Ana; Rahman, Alif
IJORER : International Journal of Recent Educational Research Vol. 7 No. 4 (2026): July
Publisher : Faculty of Teacher Training and Education Muhammadiyah University of Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46245/ijorer.v7i4.1073

Abstract

This study examines the implementation of inclusive education in Indonesia across three critical dimensions: (1) alternative assessment strategies for students with special needs (e.g., dyslexia and anxiety), (2) inclusive teaching module design grounded in Universal Design for Learning (UDL), and (3) the integration of deep learning through project-based activities. These focal areas address gaps in teacher preparedness and systemic challenges in inclusive classrooms. A descriptive quantitative survey was conducted with 20 teachers comprising regular, subject-specific, and special education educators from inclusive elementary and junior high schools nationwide. Data were gathered via validated Likert-scale questionnaires, analyzed descriptively using MS Excel, and interpreted within Florian’s (2014) inclusive education model. Reliability testing confirmed strong internal consistency (Cronbach’s α > 0.70). Results showed that 85% of teachers applied project-based or portfolio assessments for dyslexic students, 70% integrated UDL principles into multimodal modules, and 65% used contextual projects (e.g., gardening) to foster deep learning. Main challenges included time limitations, scarce resources, and weak teacher collaboration. The study offers empirical insights into integrating adaptive assessment, UDL-based design, and deep learning in inclusive classrooms, proposing a practical framework for teacher training and policy reform. Limitations include the small sample and self-reported data, warranting further mixed-methods research.