Cognitive Transformation Through Deep Learning: An Empirical Study on Strengthening Numerical Reasoning in Students at Madrasah Ibtidaiyah Negeri 13 Pidie Jaya
DOI:
https://doi.org/10.62872/za3hbd06Keywords:
deep learning, numerical reasoning, mathematics, higher-order thinkingAbstract
This study investigates the effect of the deep learning pedagogical approach on the numerical reasoning ability of fifth-grade students at Madrasah Ibtidaiyah Negeri 13 Pidie Jaya. The research is motivated by the widespread phenomenon of low student performance on mathematics problems requiring higher-order thinking skills (HOTS). A quasi-experimental design with a nonequivalent control group was employed. The study involved 52 students divided into an experimental group (n=26) and a control group (n=26). The instrument consisted of a 20-item structured essay test of numerical reasoning covering five reasoning dimensions: pattern recognition, quantitative relationships, numerical argumentation, contextual transfer, and estimation. Data were analyzed using paired t-tests and N-Gain Scores based on Hake’s (1999) formula. Results indicate that the deep learning approach significantly improved students’ numerical reasoning (p<0.05), with an average N-Gain of 0.67, classified as moderate-to-high. These findings suggest that deep learning strategies emphasizing conceptual connections, open-ended questioning, and metacognitive reflection are capable of building a stronger mathematical thinking foundation compared to conventional instruction
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