Mengungkap Perspektif Siswa: Peran Deep Learning dalam Visualisasi Konsep dan Pemecahan Masalah Matematika
Abstract
Purpose: This study explores high school students' perspectives on the integration of deep learning in mathematics education to address the dominance of procedural paradigms and barriers to visualizing abstract concepts.Design/methodology/approach: Utilizing an instrumental qualitative case study, the research focused on grade eleventh science students in Jakarta. Data were collected over eight weeks through in-depth interviews, participant observations, and document analysis of learning artifacts specifically within Geometric Transformations and Vectors.Findings: Deep learning acts as a visualization assistant and instant feedback provider, significantly reducing cognitive load. Despite initial learning curve challenges, students shifted toward iterative-experimental problem-solving approaches and enhanced conceptual focus. However, technological dependency and weak independent solution validation were identified as critical psychological barriers.Practical implications: Educators must integrate deep learning as a tool for critical thinking and exploration, supported by pedagogical strategies that cultivate data interpretation skills and critical solution validation. Originality/value: This study offers a unique contribution by providing deep qualitative insights into student agency within deep learning environments, filling a literature gap previously dominated by technical and quantitative evaluations.
Purpose: Penelitian ini bertujuan mengeksplorasi perspektif siswa SMA terhadap integrasi deep learning dalam pembelajaran matematika guna mengatasi dominasi paradigma prosedural dan hambatan visualisasi konsep abstrak. Design/methodology/approach: Menggunakan studi kasus kualitatif instrumental pada siswa kelas XI MIPA di Jakarta. Data dikumpulkan selama delapan minggu melalui wawancara mendalam, observasi partisipan, dan analisis dokumen artefak pembelajaran pada materi Transformasi Geometri dan Vektor. Findings: Deep learning berfungsi sebagai asisten visualisasi dan penyedia umpan balik instan yang mengurangi beban kognitif. Meskipun terdapat tantangan kurva pembelajaran awal, siswa menunjukkan pergeseran ke arah pendekatan pemecahan masalah iteratif-eksperimental dan peningkatan fokus konseptual. Namun, risiko ketergantungan teknologi dan lemahnya validasi solusi mandiri teridentifikasi sebagai hambatan psikologis. Practical implications: Pendidik harus mengintegrasikan deep learning sebagai alat bantu berpikir kritis dan eksplorasi, didampingi strategi pedagogis yang melatih keterampilan interpretasi data dan validasi solusi kritis. Originality/value: Studi ini memberikan kontribusi unik dengan menyediakan wawasan kualitatif mendalam mengenai agensi siswa dalam lingkungan deep learning, mengisi kesenjangan literatur yang sebelumnya didominasi oleh evaluasi teknis dan kuantitatif.
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References
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. Science China Information Sciences, 68(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 open-ended 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
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 & 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(1), 119–130. https://doi.org/10.31980/musharafa.v9i1.625
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
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