DEEP LEARNING-ORIENTED ISLAMIC RELIGIOUS EDUCATION THROUGH RELIGIOUS EXTRACURRICULAR PRACTICES
A STUDY AT SMA AL-MUHAMMAD CEPU
DOI:
https://doi.org/10.30868/ei.v15i04.10940Keywords:
Islamic Religious Education; Deep Learning; Curriculum ManagementAbstract
Deep learning in Islamic Religious Education (IRE) emphasizes conceptual understanding, active engagement, reflection, and the application of Islamic values in real-life contexts. This study aims to examine the integration of religious extracurricular activities into IRE, the curriculum management processes supporting this integration, and their contribution to deep-learning-oriented IRE at SMA Al-Muhammad Cepu. This study employed a qualitative approach with a field research design. The informants consisted of one IRE teacher, one school principal, and five parents. The IRE teacher and principal provided information on program planning, implementation, supervision, and evaluation, while the parents offered supplementary perspectives on students’ religious experiences and the application of Islamic values in the family environment. Data were collected through semi-structured interviews, observations, curriculum document analysis, and student learning artifacts. The data were analyzed thematically through coding, categorization, theme identification, cross-source comparison, and triangulation. The findings indicate that Islamic studies and worship practices strengthen students’ conceptual understanding and religious skills, while hadrah, da'wah, and social activities provide opportunities for practical engagement, collaboration, communication, reflection, and the application of Islamic values in everyday life. The study identifies a cyclical curriculum management model comprising planning, organizing, implementation, supervision, and evaluation. Evaluation results inform program improvement and subsequent planning. The model positions religious extracurricular activities as integrated learning experiences aligned with IRE objectives to support contextual, reflective, and sustainable deep learning.
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