Qur’anic Values-Based Deep Learning Approach and Junior High School Students’ Mathematics Academic Competency Test Outcomes - Marzuki Marzuki; Zulkarnaini Zulkarnaini; Ibrahim Ibrahim; Muhammad Zubir (JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian, 2026)
This study examines the relationship between a Qur’anic values-based Deep Learning approach and junior high school students’ Mathematics Academic Competency Test (ACT) outcomes. The study responds to a critical instructional issue in mathematics education: learning is often reduced to procedural fluency, while contemporary academic assessment requires conceptual understanding, reasoning, reflective judgment, and knowledge transfer. In Islamic educational contexts, this issue is particularly significant because Qur’an-memorizing students possess disciplined learning dispositions that can be pedagogically activated through meaningful, mindful, and joyful learning experiences. This quantitative correlational study involved 40 junior high school students who had memorized at least Juz 30 of the Qur’an. The data consisted of Qur’anic values-based Deep Learning scores and Mathematics ACT scores. Data were analyzed using descriptive statistics, the Shapiro–Wilk normality test, Spearman’s rho correlation, and simple linear regression. The findings showed that the Deep Learning score had a mean of 71.00 (SD = 12.82), while the Mathematics ACT score had a mean of 77.33 (SD = 11.74). Since the data were not normally distributed, Spearman’s rho was applied. The analysis revealed a very strong and significant positive relationship between the variables (rs = 0.991; p < 0.001), with regression explaining 98.2% of the variance in Mathematics ACT outcomes.