Deep Learning-Based Islamic Religious Education: Transforming Higher-Order Thinking Skills in Contemporary Classrooms - Husna Husna; Khalid Abdullah Al Muzaini; Maryam Rashid Saleh Al Tamimi; Muneera Mohammed Al Dossary (PT Barkah Ilmi Fiddunya, 2025)
The implementation of deep learning has emerged as a major educational paradigm emphasizing conceptual understanding, critical inquiry, and lifelong learning. Nevertheless, Islamic Religious Education continues to rely predominantly on memorization-oriented instructional approaches. This study aims to investigate the effectiveness of deep learning strategies in improving higher-order thinking skills among students studying Islamic Religious Education. A quasi-experimental research design involving 216 secondary school students was implemented using experimental and control groups. Data were collected through critical thinking tests, classroom observations, learning motivation scales, and reflective journals. Statistical analyses revealed that students exposed to deep learning strategies demonstrated significantly higher levels of critical thinking, analytical reasoning, problem-solving ability, collaborative learning, and conceptual understanding than those receiving conventional instruction. Deep learning also promoted reflective engagement with Islamic teachings and encouraged students to relate religious principles to contemporary social issues. The novelty of this study lies in proposing a Deep Learning-Based Islamic Religious Education Model that integrates inquiry-based pedagogy, reflective practice, and authentic assessment to transform Islamic learning into a more meaningful and intellectually engaging educational experience