The Effect of Academic Supervision and Deep Learning Approach on Student Learning Outcomes through Learning Quality in Private Junior High Schools Across Serang City

Abstract

This study departs from the still-suboptimal learning outcomes of students at private junior high schools across Serang City, a condition presumably linked to how academic supervision is carried out, how deeply the deep learning approach is applied, and the quality of classroom instruction itself. The study set out to examine the effect of academic supervision and the deep learning approach on learning quality, to test the effect of all three variables on student learning outcomes, and to explore the mediating role of learning quality in that relationship. A quantitative approach with an explanatory survey design was employed. The population consisted of teachers from private junior high schools in Serang City, with 200 respondents selected through purposive sampling. Data were gathered using a five-point Likert-scale questionnaire and analyzed through Structural Equation Modeling-Partial Least Squares (SEM-PLS) with SmartPLS. Findings show that academic supervision has a positive, significant effect on learning quality (β = 0.244; t = 2.673; p = 0.008), as does the deep learning approach (β = 0.418; t = 4.522; p < 0.001). Both variables also directly and significantly affect student learning outcomes (β = 0.284, p = 0.001; and β = 0.254, p = 0.006, respectively). Learning quality emerged as the strongest direct predictor of student outcomes (β = 0.613; t = 6.737; p < 0.001). Indirectly, learning quality significantly mediates the effect of academic supervision (β = 0.149; p = 0.012) and the deep learning approach (β = 0.256; p < 0.001) on student learning outcomes. These results confirm that learning quality functions as a mediating mechanism that strengthens the influence of academic supervision and the deep learning approach on improving student learning outcomes.