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Comparison of Student’s Mathematical Problem-Solving Ability between Realistic Mathematics Education Assisted by GeoSpace and Direct Instruction Ayumni, Febi; Ulya, Himmatul; Sumaji, Sumaji
Kognitif: Jurnal Riset HOTS Pendidikan Matematika Vol. 5 No. 4 (2025): October - December 2025
Publisher : Education and Talent Development Center Indonesia (ETDC Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/kognitif.v5i4.3340

Abstract

This study compares students’ mathematical problem-solving skills between those taught using the GeoSpace-assisted Realistic Mathematics Education (RME) model and those taught through direct instruction. A quantitative approach with a quasi-experimental nonequivalent control group design was employed. The participants were two eighth-grade classes at a junior high school, with one class assigned as the experimental group and the other as the control group. Mathematical problem-solving skills were measured using an essay test based on NCTM indicators: understanding problems, planning strategies, implementing solutions, and reviewing results. The instrument demonstrated adequate validity and reliability. The experimental group received instruction using the RME model supported by the GeoSpace application, while the control group received direct instruction without digital media. Data analysis using an independent samples t-test showed that the experimental group achieved significantly higher posttest scores than the control group (p < 0.05), with a moderate effect size. In addition, learning gains in the experimental group were higher than those in the control group. These findings indicate that the GeoSpace-assisted RME model is more effective in improving students’ mathematical problem-solving skills than direct instruction. The use of realistic contexts and interactive visual representations supports students’ conceptual understanding. However, as the study involved only two classes, further research with a broader sample is recommended to strengthen the generalizability of the results.