Mengungkap Perspektif Siswa: Peran Deep Learning dalam Visualisasi Konsep dan Pemecahan Masalah Matematika - Slamet, Slamet; Hendriana, Benny; Supiat (Universitas Muhammadiyah Prof. DR. HAMKA, 2025)
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.