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All Journal Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal)
Luluk Faridah
Universitas Islam Darul ‘Ulum Lamongan
Agriculture, Biological Sciences & Forestry Chemistry Education Physics

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Analisis Berpikir Kritis Siswa SMA melalui Pendekatan Deep Learning ditinjau dari Kecerdasan Logis Matematis Pinggir Laras Nurlinawati; Luluk Faridah; Abdur Rohim
Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Vol. 7 No. 3 (2026): August (Inpres)
Publisher : Mataram University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/goescienceed.v7i3.2875

Abstract

Mathematical critical thinking represents a foundational competency that demands deliberate cultivation within mathematics education, with logical-mathematical intelligence frequently cited as a key underlying factor. This study aimed to analyze and describe the profile of students' mathematical critical thinking skills under a Deep Learning framework, specifically looking at variations in their logical-mathematical intelligence when solving probability concepts. Utilizing a descriptive qualitative design, this inquiry involved 31 tenth-grade students at SMA Darul Ulum Sugio. From this cohort, six subjects were selected via purposive sampling—comprising two students each from high, moderate, and low intelligence levels—based on their logical-mathematical intelligence scores. Data collection was instrumented through a diagnostic questionnaire, a customized critical thinking test, and semi-structured interviews. The empirical results demonstrated that students with high logical-mathematical intelligence successfully satisfied all critical thinking criteria. Meanwhile, those in the moderate category met three distinct indicators, whereas students with low logical-mathematical intelligence only managed to satisfy the elementary clarification stage. These outcomes suggest that a more pronounced level of logical-mathematical intelligence yields substantially better mathematical critical thinking performance when engaging with probability tasks within a Deep Learning environment.
Co-Authors Abdur Rohim Pinggir Laras Nurlinawati