Problem-Based Deep Learning Practice in Analyzing the Content of Muslim Hadiths Narrated by Abu Hurairah
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Abstract
Purpose – Learning is a series of important processes in an effort to maximize knowledge transfer between educators and students. The problem-based Deep Learning approach is one of the solutions to increase students' reasoning in understanding a material.
Methodology – This study employs a descriptive qualitative approach. The collected data serve as the primary source of information. The data are then analyzed using Miles and Huberman's theory through three stages: data collection, data reduction, data display, and conclusion drawing/verification.
Findings – The results showed an increase in students' critical reasoning as shown through observation of their analytical exposure in solving problems. This indicates a significant contribution of the deep learning approach to problem-based learning in an effort to strengthen students' deep understanding of the material being taught.
Novelty – This study offers a novel integration of the problem-based learning model with a deep learning approach in the context of Islamic religious education, specifically the analysis of Hadith content narrated by Abu Hurairah. Unlike previous studies that often treat religious learning as rote memorization, this research demonstrates how students can develop critical reasoning and deep conceptual understanding through analytical engagement. It provides a new pedagogical framework that links religious text analysis with higher-order thinking skills.
Significance – This study is particularly beneficial for Islamic education practitioners, curriculum developers, and educators in secondary schools, especially those seeking innovative methods to enhance students' comprehension of religious texts. Furthermore, it contributes to the broader field of educational methodology, offering insights for researchers and policy-makers interested in implementing student-centered and thinking-oriented learning strategies across different subject areas.
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