GASING vs Deep Learning: Which Instructional Approach More Effectively Improves Primary Students’ Understanding of Whole Numbers? - Putra, Yudi Braja; Sardin,; Al Jupri, (Jurnal Paedagogy, 2026)
This study analyzes the comparative effectiveness of the GASING (Easy, Engaging, and Enjoyable) approach and the Deep Learning approach in improving the mathematics learning outcomes of elementary school students regarding whole number content. A quasi-experimental pretest-posttest design was employed with 18 students from two classes at a public elementary school in Siak Regency, Riau Province (Class A: Deep Learning, n = 9; Class B: GASING, n = 9). Data were collected using a 20-item validated multiple-choice achievement test (KR-20 = 0.78) and semi-structured interviews. Quantitative analysis included normalized N-Gain, paired-samples t-tests, the Mann-Whitney U test, and Cohen's d effect size. Results indicated that both approaches significantly improved learning outcomes. The Deep Learning approach yielded a medium N-Gain ($\langle g \rangle$ = 0.547, t = 6.874, p < 0.001), whereas the GASING approach yielded a high N-Gain ($\langle g \rangle$ = 0.827, t = 8.847, p < 0.001). Between-group comparison confirmed that the GASING approach was significantly more effective (U = 70.5, p = 0.009, Cohen's d = 1.57, indicating a very large effect). Interview data corroborated these findings, showing that Class B students demonstrated greater self-confidence and active engagement. Because whole number understanding serves as the foundation for subsequent mathematical concepts—such as arithmetic operations, fractions, and algebraic reasoning—mastering this content is critical for long-term mathematical development. Therefore, identifying instructional approaches like GASING that effectively support whole number mastery is essential for strengthening students’ readiness for advanced mathematical concepts.