Mengungkap Perspektif Siswa: Peran Deep Learning dalam Visualisasi Konsep dan Pemecahan Masalah Matematika
Abstract
Pendidikan matematika modern menuntut pemahaman konseptual dan pemecahan masalah adaptif. Integrasi deep learning menawarkan potensi revolusioner, namun perspektif siswa belum banyak digali. Penelitian ini bertujuan memahami perspektif siswa SMA terhadap pembelajaran matematika dengan deep learning dan dampaknya pada pemecahan masalah. Menggunakan metode kualitatif studi kasus pada 12 siswa di Jakarta, data dikumpulkan via wawancara, observasi, dan analisis dokumen, lalu dianalisis tematik. Hasil menunjukkan deep learning dianggap asisten visualisasi dan umpan balik instan, meski ada kekhawatiran ketergantungan. Tantangan adaptasi dan interpretasi hasil teratasi melalui pengembangan literasi teknologi dan kolaborasi. Deep learning mendorong pendekatan iteratif dan fokus konseptual, namun validasi solusi tetap krusial. Kontribusi penelitian ini adalah wawasan mendalam dari sudut pandang siswa, esensial untuk perancangan pedagogi deep learning yang efektif dan berpusat pada siswa.
Modern mathematics education demands conceptual understanding and adaptive problem solving. Deep learning integration offers revolutionary potential, but students’ perspectives have not been widely explored. This study aims to understand high school students’ perspectives on mathematics learning with deep learning and its impact on problem solving. Using a qualitative case study method on 12 students in Jakarta, data were collected via interviews, observations, and document analysis, then analyzed thematically. The results show that deep learning is considered an assistant to visualization and instant feedback, although there are concerns about dependency. Challenges of adaptation and interpretation of results are overcome through the development of technological literacy and collaboration. Deep learning encourages an iterative approach and conceptual focus, but validation of solutions remains crucial. The contribution of this study is in-depth insight from the students’ perspective, essential for designing effective and student-centered deep learning pedagogy.
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References
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Crossref
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Crossref
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Crossref
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Crossref
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Crossref
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Crossref
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Crossref
Geiger, V., Gal, I., & Graven, M. (2023). The connections between citizenship education and mathematics education. ZDM - Mathematics Education, 55(5), 923-940. https://doi.org/10.1007/s11858-023-01521-3
Crossref
Gurmu, F., Tuge, C., & Hunde, A. B. (2024). Effects of GeoGebra-assisted instructional methods on students' conceptual understanding of geometry. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2379745
Crossref
Källberg, P. S., & Roos, H. (2025). Meaning(s) of a student perspective in mathematics education research. Educational Studies in Mathematics, 367-392. https://doi.org/10.1007/s10649-024-10374-w
Crossref
Kamberi, M. (2025). The types of intrinsic motivation as predictors of academic achievement: the mediating role of deep learning strategy. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2482482
Crossref
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Crossref
Klang, E., Barash, Y., Levartovsky, A., Lederer, N. B., & Lahat, A. (2021). Differentiation between malignant and benign endoscopic images of gastric ulcers using deep learning. Clinical and Experimental Gastroenterology, 14, 155-162. https://doi.org/10.2147/CEG.S292857
Crossref
Krawitz, J., Schukajlow, S., Yang, X., & Geiger, V. (2025). A Systematic Review of International Perspectives on Mathematical Modelling: Modelling Goals and Task Characteristics. ZDM - Mathematics Education, 193-212. https://doi.org/10.1007/s11858-025-01683-2
Crossref
Lipnevich, A. A., & Smith, J. K. (2009). Effects of Differential Feedback on Students' Examination Performance. Journal of Experimental Psychology: Applied, 15(4), 319-333. https://doi.org/10.1037/a0017841
Crossref
Maffia, A., Manolino, C., & Miragliotta, E. (2025). There is more to algebra than meets the eye: the case of blindness. Educational Studies in Mathematics, 63-77. https://doi.org/10.1007/s10649-025-10394-0
Crossref
Maouche, S. (2019). Google AI: Opportunities, Risks, and Ethical Challenges. Contemporary French and Francophone Studies, 23(4), 447-455. https://doi.org/10.1080/17409292.2019.1705012
Crossref
Nababan, E., Huda, S., Hasibuan, M., Mika, S., Amanda, T., Mailani, E., & Rarastika, N. (2025). Penerapan Pendekatan Deep Learning untuk Mendukung Pembelajaran Matematika di Sekolah Dasar. 8(1). El Banar: Jurnal Pendidikan dan Pengajaran. https://doi.org/10.54125/elbanar.v8i1.539
Crossref
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Crossref
Sanusi, M. S. (2022). Action research to reassess the acceptance and use of technology in a blended learning approach amongst postgraduate business students. Cogent Education, 9(1).https://doi.org/10.1080/2331186X.2022.2145813
Crossref
Shi, L., Muhammad Umer, A., & Shi, Y. (2023). Utilizing AI models to optimize blended teaching effectiveness in college-level English education. Cogent Education, 10(2). https://doi.org/10.1080/2331186X.2023.2282804
Crossref
Shimizu, Y., & Kang, H. (2025). Research on classroom practice and students' errors in mathematics education: a scoping review of recent developments for 2018-2023. ZDM - Mathematics Education. https://doi.org/10.1007/s11858-025-01704-0
Crossref
Sigrist, R., Rauter, G., Riener, R., & Wolf, P. (2013). Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychonomic Bulletin and Review, 20(1), 21-53. https://doi.org/10.3758/s13423-012-0333-8
Crossref
Strohmaier, A. R., Schiepe-Tiska, A., Chang, Y. P., Müller, F., Lin, F. L., & Reiss, K. M. (2020). Comparing eye movements during mathematical word problem solving in Chinese and German. ZDM - Mathematics Education, 52(1), 45-58. https://doi.org/10.1007/s11858-019-01080-6
Crossref
Suryani, M., Jufri, L. H., & Putri, T. A. (2020). Analisis Kemampuan Pemecahan Masalah Siswa Berdasarkan Kemampuan Awal. Musharafa: Jurnal Pendidikan Matematika, 9, 119-130.
https://doi.org/10.31980/mosharafa.v9i1.597
Crossref
Umi, N., Purna, R. |, Nugroho, B., Karsoni, |, & Dinata, B. (2021). Pengembangan Video Pembelajaran Berbasis Pemecahan Masalah Berbantuan Adobe Captivate Materi Matriks di Sekolah Kejuruan (SMK) 3 Kota Bumi. International Journal of Progressive Mathematics Education, 1(3), 234-255. https://doi.org/10.22236/ijopme.v1i3.7689
Crossref
Wang, H., Lu, H., Sun, J., & Safo, S. E. (2024). Interpretable deep learning methods for multiview learning. BMC Bioinformatics, 25(1), 1-30. https://doi.org/10.1186/s12859-024-05679-9
Crossref
Winje, Ø., & Løndal, K. (2023). 'Wow! is that a birch leaf? In the picture it looked totally different': a pragmatist perspective on deep learning in Norwegian 'uteskole.' Education 3-13, 51(1), 142-155. https://doi.org/10.1080/03004279.2021.1955946
Crossref
Yu, I. C., Guo, J. M., Lin, W. C., & Fang, J. T. (2025). Development of nonverbal communication behavior model for nursing students based on deep learning facial expression recognition technology. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2024.2448059
Crossref
Zijlstra, F., & While, P. T. (2024). Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning. Magnetic Resonance Materials in Physics, Biology and Medicine, 37(6), 1059-1076. https://doi.org/10.1007/s10334-024-01193-4
Crossref
Crossref
Brooks, P. T., Munthe-Fog, L., Rieneck, K., Banch Clausen, F., Rivera, O. B., Kannik Haastrup, E., Fischer-Nielsen, A., & Svalgaard, J. D. (2021). Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells. Adipocyte, 10(1), 621-630. https://doi.org/10.1080/21623945.2021.2000696
Crossref
Canedo Junior, N., Borba, M. C., & Villa-Ochoa, J. A. (2025). Contributions of Digital Videos in Mathematical Modelling Practices: Meanings and Resources Semiotics. ZDM - Mathematics Education, 57(2/3), 473-488. https://doi.org/10.1007/s11858-025-01681-4
Crossref
Chatikobo, M. V., & Pasipamire, N. (2024). Readiness to embrace artificial intelligence in information literacy instruction at a Zimbabwean University. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2425209
Crossref
Chen, X., Hu, X., Huang, Y., Jiang, H., Ji, W., Jiang, Y., Jiang, Y., Liu, B., Liu, H., Li, X., Lian, X., Meng, G., Peng, X., Sun, H., Shi, L., Wang, B., Wang, C., Wang, J., Wang, T., … Zhang, L. (2025). Deep learning-based software engineering: progress, challenges, and opportunities. In Science China Information Sciences (Vol. 68, Issue 1). https://doi.org/10.1007/s11432-023-4127-5
Crossref
Dang, Y., Chen, Z., Li, H., & Shu, H. (2022). A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2074129
Crossref
El-Latif, E. I. A., El-Dosuky, M., Darwish, A., & Hassanien, A. E. (2024). A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning. Scientific Reports, 14(1), 26463. https://doi.org/10.1038/s41598-024-75830-2
Crossref
Emara, M., Hutchins, N. M., Grover, S., Snyder, C., & Biswas, G. (2021). Examining student regulation of collaborative, computational, problem-solving processes in openended learning environments. Journal of Learning Analytics, 8(1), 49-74. https://doi.org/10.18608/JLA.2021.7230
Crossref
Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM - Mathematics Education, 56(2), 281-292. https://doi.org/10.1007/s11858-023-01530-2
Crossref
Engelbrecht, J., Borba, M. C., & Kaiser, G. (2023). Will we ever teach mathematics again in the way we used to before the pandemic? ZDM - Mathematics Education, 55(1), 1-16. https://doi.org/10.1007/s11858-022-01460-5
Crossref
Fan, L., Luo, J., Xie, S., Zhu, F., & Li, S. (2022). Chinese students' access, use and perceptions of ICTs in learning mathematics: findings from an investigation of Shanghai secondary schools. ZDM - Mathematics Education, 54(3), 611-624. https://doi.org/10.1007/s11858-022-01363-5
Crossref
Geiger, V., Gal, I., & Graven, M. (2023). The connections between citizenship education and mathematics education. ZDM - Mathematics Education, 55(5), 923-940. https://doi.org/10.1007/s11858-023-01521-3
Crossref
Gurmu, F., Tuge, C., & Hunde, A. B. (2024). Effects of GeoGebra-assisted instructional methods on students' conceptual understanding of geometry. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2379745
Crossref
Källberg, P. S., & Roos, H. (2025). Meaning(s) of a student perspective in mathematics education research. Educational Studies in Mathematics, 367-392. https://doi.org/10.1007/s10649-024-10374-w
Crossref
Kamberi, M. (2025). The types of intrinsic motivation as predictors of academic achievement: the mediating role of deep learning strategy. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2482482
Crossref
Kaur, D. P., Mantri, A., & Horan, B. (2021). A Framework Utilizing Augmented Reality to Enhance the Teaching-Learning Experience of Linear Control Systems. IETE Journal of Research, 67(2), 155-164. https://doi.org/10.1080/03772063.2018.1532822
Crossref
Klang, E., Barash, Y., Levartovsky, A., Lederer, N. B., & Lahat, A. (2021). Differentiation between malignant and benign endoscopic images of gastric ulcers using deep learning. Clinical and Experimental Gastroenterology, 14, 155-162. https://doi.org/10.2147/CEG.S292857
Crossref
Krawitz, J., Schukajlow, S., Yang, X., & Geiger, V. (2025). A Systematic Review of International Perspectives on Mathematical Modelling: Modelling Goals and Task Characteristics. ZDM - Mathematics Education, 193-212. https://doi.org/10.1007/s11858-025-01683-2
Crossref
Lipnevich, A. A., & Smith, J. K. (2009). Effects of Differential Feedback on Students' Examination Performance. Journal of Experimental Psychology: Applied, 15(4), 319-333. https://doi.org/10.1037/a0017841
Crossref
Maffia, A., Manolino, C., & Miragliotta, E. (2025). There is more to algebra than meets the eye: the case of blindness. Educational Studies in Mathematics, 63-77. https://doi.org/10.1007/s10649-025-10394-0
Crossref
Maouche, S. (2019). Google AI: Opportunities, Risks, and Ethical Challenges. Contemporary French and Francophone Studies, 23(4), 447-455. https://doi.org/10.1080/17409292.2019.1705012
Crossref
Nababan, E., Huda, S., Hasibuan, M., Mika, S., Amanda, T., Mailani, E., & Rarastika, N. (2025). Penerapan Pendekatan Deep Learning untuk Mendukung Pembelajaran Matematika di Sekolah Dasar. 8(1). El Banar: Jurnal Pendidikan dan Pengajaran. https://doi.org/10.54125/elbanar.v8i1.539
Crossref
Salau, L., Mohamed, H., Abdulsalam, Y. S., & Mohammed, H. (2025). Deep learning based multi-criteria recommender system for technology-enhanced learning. Scientific Reports, 15(1), 1-17. https://doi.org/10.1038/s41598-025-97407-3
Crossref
Sanusi, M. S. (2022). Action research to reassess the acceptance and use of technology in a blended learning approach amongst postgraduate business students. Cogent Education, 9(1).https://doi.org/10.1080/2331186X.2022.2145813
Crossref
Shi, L., Muhammad Umer, A., & Shi, Y. (2023). Utilizing AI models to optimize blended teaching effectiveness in college-level English education. Cogent Education, 10(2). https://doi.org/10.1080/2331186X.2023.2282804
Crossref
Shimizu, Y., & Kang, H. (2025). Research on classroom practice and students' errors in mathematics education: a scoping review of recent developments for 2018-2023. ZDM - Mathematics Education. https://doi.org/10.1007/s11858-025-01704-0
Crossref
Sigrist, R., Rauter, G., Riener, R., & Wolf, P. (2013). Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychonomic Bulletin and Review, 20(1), 21-53. https://doi.org/10.3758/s13423-012-0333-8
Crossref
Strohmaier, A. R., Schiepe-Tiska, A., Chang, Y. P., Müller, F., Lin, F. L., & Reiss, K. M. (2020). Comparing eye movements during mathematical word problem solving in Chinese and German. ZDM - Mathematics Education, 52(1), 45-58. https://doi.org/10.1007/s11858-019-01080-6
Crossref
Suryani, M., Jufri, L. H., & Putri, T. A. (2020). Analisis Kemampuan Pemecahan Masalah Siswa Berdasarkan Kemampuan Awal. Musharafa: Jurnal Pendidikan Matematika, 9, 119-130.
https://doi.org/10.31980/mosharafa.v9i1.597
Crossref
Umi, N., Purna, R. |, Nugroho, B., Karsoni, |, & Dinata, B. (2021). Pengembangan Video Pembelajaran Berbasis Pemecahan Masalah Berbantuan Adobe Captivate Materi Matriks di Sekolah Kejuruan (SMK) 3 Kota Bumi. International Journal of Progressive Mathematics Education, 1(3), 234-255. https://doi.org/10.22236/ijopme.v1i3.7689
Crossref
Wang, H., Lu, H., Sun, J., & Safo, S. E. (2024). Interpretable deep learning methods for multiview learning. BMC Bioinformatics, 25(1), 1-30. https://doi.org/10.1186/s12859-024-05679-9
Crossref
Winje, Ø., & Løndal, K. (2023). 'Wow! is that a birch leaf? In the picture it looked totally different': a pragmatist perspective on deep learning in Norwegian 'uteskole.' Education 3-13, 51(1), 142-155. https://doi.org/10.1080/03004279.2021.1955946
Crossref
Yu, I. C., Guo, J. M., Lin, W. C., & Fang, J. T. (2025). Development of nonverbal communication behavior model for nursing students based on deep learning facial expression recognition technology. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2024.2448059
Crossref
Zijlstra, F., & While, P. T. (2024). Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning. Magnetic Resonance Materials in Physics, Biology and Medicine, 37(6), 1059-1076. https://doi.org/10.1007/s10334-024-01193-4
Crossref
Authors
Slamet, S., Hendriana, B., & Supiat. (2025). Mengungkap Perspektif Siswa: Peran Deep Learning dalam Visualisasi Konsep dan Pemecahan Masalah Matematika. International Journal of Progressive Mathematics Education, 5(1), 225–237. https://doi.org/10.22236/ijopme.v5i1.19310
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