OPTIMIZING MATHEMATICAL PROBLEM SOLVING ABILITY THROUGH DEEP LEARNING INTEGRATION ORIENTED TOWARDS SELF-REGULATED LEARNING
Abstract
Mathematical problem-solving ability is an essential competency in mathematics learning; however, research conducted during 2022–2025 consistently indicates that junior high school students’ problem-solving ability remains in the low category. Students tend to apply formulas without understanding the problem context and rarely verify their solutions. This study aims to examine: (1) whether there is a significant difference in mathematical problem-solving ability between students taught using the Deep Learning approach and those taught using a conventional approach; and (2) whether the improvement in mathematical problem-solving ability of students using the Deep Learning approach is better than that of students using the conventional approach. This study employed a quantitative method with a quasi-experimental design (Nonequivalent Pretest-Posttest Control Group Design), involving 61 eighth-grade students at SMP Negeri 1 Jatiwangi: 31 students in the experimental class (VIII-E) and 30 students in the control class (VIII-H). Data were collected through essay tests based on Polya’s problem-solving indicators and analyzed using independent samples t-test and Welch’s T’ test via IBM SPSS Statistics 26.0. The results show that: (1) there is a significant difference in posttest scores between the two classes (sig./2 = 0.0115 < 0.05), with the experimental class mean (56.42) higher than the control class (51.33); and (2) the improvement of the experimental class (N-Gain = 0.3013; moderate category) is significantly better than the control class (N-Gain = 0.1920; low category), based on Welch’s T’ test (sig./2 = 0.0015 < 0.05). These findings confirm that the Deep Learning effectively enhances students’ mathematical problem-solving ability.
Kemampuan pemecahan masalah matematika merupakan kompetensi penting dalam pembelajaran matematika; namun, penelitian yang dilakukan selama tahun 2022–2025 secara konsisten menunjukkan bahwa kemampuan pemecahan masalah siswa SMP masih berada pada kategori rendah. Siswa cenderung menerapkan rumus tanpa memahami konteks masalah dan jarang memverifikasi solusi mereka. Penelitian ini bertujuan untuk menguji: (1) apakah terdapat perbedaan signifikan dalam kemampuan pemecahan masalah matematika antara siswa yang diajar menggunakan pendekatan Deep Learning dan siswa yang diajar menggunakan pendekatan konvensional; dan (2) apakah peningkatan kemampuan pemecahan masalah matematika siswa yang menggunakan pendekatan Deep Learning lebih baik daripada siswa yang menggunakan pendekatan konvensional. Penelitian ini menggunakan metode kuantitatif dengan desain kuasi-eksperimental (Desain Kelompok Kontrol Non-ekuivalen Pretest-Posttest), yang melibatkan 61 siswa kelas delapan di SMP Negeri 1 Jatiwangi: 31 siswa di kelas eksperimen (VIII-E) dan 30 siswa di kelas kontrol (VIII-H). Data dikumpulkan melalui tes esai berdasarkan indikator pemecahan masalah Polya dan dianalisis menggunakan uji t sampel independen dan uji T Welch melalui IBM SPSS Statistics 26.0. Hasil menunjukkan bahwa: (1) terdapat perbedaan signifikan pada skor posttest antara kedua kelas (sig./2 = 0,0115 < 0,05), dengan rata-rata kelas eksperimen (56,42) lebih tinggi daripada kelas kontrol (51,33); dan (2) peningkatan kelas eksperimen (N-Gain = 0,3013; kategori sedang) secara signifikan lebih baik daripada kelas kontrol (N-Gain = 0,1920; kategori rendah), berdasarkan uji T Welch (sig./2 = 0,0015 < 0,05). Temuan ini menegaskan bahwa Deep Learning secara efektif meningkatkan kemampuan pemecahan masalah matematika siswa.
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