The Effect of Deep Learning Instruction on the Motivation and Learning Outcomes of Grade 11 Students at SMA IT Nur Hidayah

Abstract

This study aimed to investigate the effects of a deep learning–oriented instructional approach on the learning motivation and academic achievement of Grade 11 high school students. A quantitative quasi-experimental method was employed using a pretest–posttest control group design. The participants comprised 49 Grade 11 students, including 27 students in the experimental group and 22 students in the control group. Both groups received instruction on the human excretory system; however, the experimental group was taught using a deep learning approach, whereas the control group received instruction using a non-deep-learning approach. Data were collected using a learning motivation questionnaire, a 10-item pretest, and a 20-item posttest. The data were analyzed using the nonparametric Mann–Whitney U test and Wilcoxon signed-rank test. The findings demonstrated that the deep learning approach significantly improved the cognitive learning outcomes of Grade 11 students at SMA IT Nur Hidayah (Mann–Whitney U test, p = 0.045), with the Wilcoxon signed-rank test indicating a more consistent pattern of improvement. In contrast, no substantive difference was observed in learning motivation, with an increase of only 0.11%, which may be attributed to a ceiling effect resulting from the students’ initially high motivation levels. Empirically, this instructional approach was particularly effective in restructuring students’ cognitive understanding of complex subject matter, although it did not produce a substantial affective impact.