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All Journal Ar-Rayyan: Journal of Islamic Education
Husna Husna
Sekolah Tinggi Agama Islam (STAI) Al-Jami Banjarmasin

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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
Ar-rayyan: Journal Of Islamic Education Vol. 2 No. 2: Juli-Des 2025, Ar-Rayyan: Journal of Islamic Education
Publisher : PT Barkah Ilmi Fiddunya

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Abstract

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
Learning Analytics in Islamic Religious Education: Predicting Students’ Learning Achievement through Educational Data Mining Husna Husna; Khalid Abdullah Al Muzaini; Maryam Rashid Saleh Al Tamimi; Muneera Mohammed Al Dossary
Ar-rayyan: Journal Of Islamic Education Vol. 3 No. 1: Jan-Jun 2026, Ar-Rayyan: Journal of Islamic Education
Publisher : PT Barkah Ilmi Fiddunya

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The increasing availability of educational data has opened new opportunities for improving instructional decision-making through learning analytics. However, its application in Islamic Religious Education (IRE) remains limited, particularly in predicting students’ academic achievement and learning behavior. This study aims to develop a learning analytics model capable of identifying learning patterns and predicting students’ performance in IRE. A quantitative research design employing Educational Data Mining (EDM) techniques was conducted using learning management system (LMS) data from 512 secondary school students. Data included attendance records, assignment completion, online participation, quiz scores, and learning engagement indicators. Machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, were employed to develop predictive models. The findings reveal that learning engagement, assignment consistency, and reflective participation significantly predict students’ academic achievement. Furthermore, predictive analytics enables teachers to identify at-risk learners early and provide personalized interventions to improve learning outcomes. The novelty of this study lies in proposing the Islamic Learning Analytics Framework, integrating educational data mining, predictive modeling, and Islamic pedagogical principles to support evidence-based instructional decision-making
Co-Authors Khalid Abdullah Al Muzaini Maryam Rashid Saleh Al Tamimi Muneera Mohammed Al Dossary