DEVELOPMENT OF AN RME-BASED GAME WITH LOCAL WISDOM CONTENT AND DEEP LEARNING STRATEGIES FOR MATHEMATICS LEARNING

Rahmi Hayati(1*), Edi Syahputra(2), Edy Surya(3), Nuraina Nuraina(4), Rohantizani Rohantizani(5),

(1) Universitas Almuslim
(2) Unimed
(3) unimed
(4) Unimal
(5) Unimal
(*) Corresponding Author


Abstract


Abstract

The main problem in elementary school mathematics learning is the low student motivation and learning outcomes caused by conventional approaches that tend to be abstract and lack connections to real-life contexts. This study aims to develop an educational game based on Realistic Mathematics Education (RME) with the integration of Acehnese local wisdom to improve students’ motivation and learning outcomes. The research employed a modified Research and Development (R&D) model of Borg & Gall, which consisted of needs analysis, design, expert validation, limited trial, revision, main field trial, and final revision. The research subjects included 30 fifth-grade students in the limited trial stage and 90 students from three different elementary schools in the main field trial, using a quasi-experimental design with a pretest-posttest control group. The instruments used were student response questionnaires, expert validation sheets, and achievement tests. Expert validation results indicated that the product was in the “highly valid” category. The limited trial showed that 90% of students considered the game easy to use and 85% felt more motivated, with learning achievement increasing from an average pretest score of 62 to 78 in the posttest; a paired-sample t-test yielded t(29) = 5.84, p < 0.001. The main field trial demonstrated a significant improvement, where the experimental class increased from an average score of 61 to 84, while the control class only increased from 63 to 72; the ANOVA results showed F(1,88) = 12.35, p < 0.01 with an effect size of 0.82 (large category). It is concluded that the RME-based educational game with Acehnese local wisdom is effective in enhancing students’ motivation and learning outcomes, and it has the potential to be further developed by adding adaptive and interactive features to strengthen student engagement.

Keywords: Deep Learning; Game; Local Wisdom; RME

 

Abstrak

Permasalahan utama dalam pembelajaran matematika di sekolah dasar adalah rendahnya motivasi dan hasil belajar siswa akibat pendekatan konvensional yang cenderung abstrak serta minim keterkaitan dengan konteks nyata kehidupan sehari-hari. Penelitian ini bertujuan mengembangkan game edukatif berbasis Realistic Mathematics Education (RME) dengan integrasi kearifan lokal Aceh untuk meningkatkan motivasi dan hasil belajar siswa. Metode penelitian menggunakan Research and Development (R&D) model Borg & Gall yang dimodifikasi, melalui tahapan analisis kebutuhan, perancangan, validasi ahli, uji coba terbatas, revisi, uji coba lapangan utama, hingga revisi akhir. Subjek penelitian melibatkan 30 siswa kelas V SD pada tahap uji terbatas dan 90 siswa dari tiga sekolah dasar berbeda pada tahap uji lapangan dengan desain quasi-experiment menggunakan pretest-posttest control group design. Instrumen yang digunakan meliputi angket respon siswa, lembar validasi ahli, serta tes hasil belajar. Hasil validasi ahli menyatakan produk berada pada kategori sangat valid. Uji terbatas menunjukkan 90% siswa menilai game mudah digunakan dan 85% merasa lebih termotivasi, dengan peningkatan hasil belajar dari skor rata-rata pretest 62 menjadi 78 pada posttest; analisis uji t berpasangan menghasilkan t(29)=5,84, p<0,001. Uji lapangan menunjukkan peningkatan signifikan, di mana kelas eksperimen meningkat dari skor rata-rata 61 menjadi 84, sedangkan kelas kontrol hanya meningkat dari 63 menjadi 72; hasil ANOVA F(1,88)=12,35, p<0,01 dengan effect size 0,82 (kategori besar). Disimpulkan bahwa game edukatif berbasis RME dengan kearifan lokal Aceh efektif meningkatkan motivasi dan hasil belajar siswa, serta berpotensi dikembangkan lebih lanjut dengan penambahan fitur adaptif dan interaktif untuk memperkuat keterlibatan siswa.

Kata kunci: Deep Learning; Game; Kearifan Lokal; RME

 


Keywords


Deep Learning; Game; Local Wisdom; RME

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DOI: http://dx.doi.org/10.24127/ajpm.v14i4.14372

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