Efektivitas Penerapan Model Pembelajaran Jigsaw Melalui Pendekatan Deep Learning untuk Meningkatkan Aktivitas Belajar Siswa pada Mata Pelajaran Ekonomi
DOI:
https://doi.org/10.65065/vfwhpn65Keywords:
Jigsaw Learning Model, Deep Learning Approach, Student Learning Activity, Economics LearningAbstract
This study aims to determine the effectiveness of applying the jigsaw learning model through the deep learning approach in improving student learning activities in economics subjects at salah satu SMAN di Kabupaten Kampar. The problem underlying this research was the low learning activity of class X 1 students, characterized by lack of enthusiasm, minimal group collaboration, limited social interaction, low active participation, and reluctance to express opinions during learning. This study used a quantitative approach with a pre-experimental design (one group pretest-posttest). The sample consisted of 35 students of class X 1 selected using purposive sampling technique. Data were collected through questionnaires and observation sheets for both teacher and student activities. The research was conducted in three meetings from February to March 2026 on the topic of financial institutions. Data analysis employed descriptive statistics, the Shapiro-Wilk normality test, and the paired sample T-test using SPSS 25. Observational results showed a consistent increase in learning activity scores from 2.06 (51.50%, moderate category) in the first meeting, to 2.73 (68.25%, high category) in the second, and reaching 3.59 (89.75%, very high category) in the third meeting. Questionnaire results showed the pretest mean was 64.57, with 62.86% in the moderate category and 37.14% in the low category. After the intervention, the posttest mean rose to 94.51, with 82.86% achieving the high category and 8.57% the very high category. The paired sample T-test yielded a significance value of 0.000 (< 0.05), meaning H0 was rejected and Ha accepted. The application of the jigsaw learning model through the deep learning approach is statistically effective in improving student learning activities in economics and is recommended as an innovative alternative for economics teachers aligned with the national deep learning-based curriculum
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