The Effect of Metacognitive-Based Deep Learning Models in Mathematics Learning on the Academic Resilience of Elementary School Students

Mathematics learning at the elementary level is often associated with anxiety and low academic resilience, which may hinder students’ persistence and performance. Strengthening resilience through innovative instructional models is therefore essential. This study aims to examine the effect of a Metacognitive-Based Deep Learning model on the academic resilience of elementary school students in mathematics. A quantitative true experimental design with a posttest-only control group was employed. The sample consisted of 120 fourth- and fifth-grade students divided equally into experimental and control groups. Data were collected using a 20-item mathematical resilience questionnaire measuring value, struggle, growth, and perseverance. Confirmatory Factor Analysis using PLS-SEM confirmed the validity and reliability of the instrument. Data were analyzed through an independent samples t-test, Cohen’s d effect size, and two-way ANOVA. The results revealed a highly significant difference between groups (p < .001), with a mean difference of 24.233 points and a very large effect size (d = 3.46). The findings indicate that the intervention consistently improved students’ academic resilience across grade levels without significant interaction effects. This study contributes theoretically by reinforcing the role of deep learning integrated with metacognitive strategies in developing non-cognitive competencies in elementary education. Practically, it offers an evidence-based instructional alternative to enhance students’ resilience and reduce mathematics anxiety.