Penggunaan Deep Learning Berbasis Sosiolinguistik untuk Menumbuhkan Keterampilan Berpikir Kritis dan Pemecahan Masalah di Pendidikan Dasar

Abstract

Critical thinking and problem-solving are essential skills for 21st-century learners, yet traditional pedagogies often emphasize rote memorization over cognitive engagement. Deep Learning (DL) technologies offer adaptive, personalized feedback and learning environments that can bridge this gap. This study examines how DL, when integrated with sociolinguistic principles, can enhance students’ critical thinking and problem-solving competencies in primary education. Employing a qualitative, descriptive design through literature review, peer-reviewed journals, conference proceedings, and books were sourced via Google Scholar, using thematic content analysis to distill implementation strategies, outcomes, and challenges. Findings indicate that DL-supported activities—such as authentic discourse analysis, role-playing with linguistic registers, and interactive problem-based tasks—promote metacognitive awareness, communicative flexibility, and pragmatic competence. Students demonstrated improved reasoning skills, confidence in communication, and awareness of sociocultural language variation. However, infrastructure limitations and teacher preparedness remain challenges. The study concludes that DL enriched by sociolinguistic insights forms a robust pedagogical approach, preparing students for complex, real-world communication demands.