Penerapan Pendekatan Deep Learning dalam Meningkatkan Kemampuan Berfikir Kritis Materi Hukum Newton

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Authors: Syamiah Alfi, Agung Tralisno
Cited by: 0

Abstract

Newton’s laws constitute a fundamental subject in physics education, forming the basis for comprehending motion phenomena and their practical applications in everyday life. Understanding this topic requires students to apply critical thinking by linking theoretical concepts, physical laws, and real-life occurrences. However, physics instruction in many schools still relies heavily on teacher-centered lectures and memorization of formulas, which limits the development of students’ critical thinking. This approach often results in difficulties for students in grasping abstract ideas and using them in real-world contexts. This study seeks to improve students’ critical thinking abilities regarding Newton’s laws through the implementation of a deep learning approach. The research used a Classroom Action Research (CAR) design implemented in two cycles, each comprising planning, action, observation, and reflection stages. The participants were 30 students from class X A at a senior high school in Jambi, selected through purposive sampling. Data were gathered using learning outcome tests designed based on critical thinking indicators. The findings indicate an enhancement in students’ critical thinking, as shown by an N-gain of 0.49 in cycle I, which rose to 0.70 in cycle II, both falling within the moderate category. Additionally, students’ learning completeness improved from 78% to 88%, and the performance across six critical thinking indicators increased from 74% to 82.67%. These results suggest that the deep learning approach effectively boosts students’ critical thinking skills and promotes more meaningful physics learning centered on scientific reasoning.

Fields of Study & Subjects

Educational Methods and Outcomes Education and Critical Thinking Development STEM Education

Publication details

Journal / source
MAGNETON Jurnal Inovasi Pembelajaran Fisika
Volume
4
Issue
1
Pages
58–69
Publication date
31 January 2026