Peningkatan Kemampuan Berpikir Kritis Siswa Melalui Penerapan Pendekatan Deep Learning Berbasis Data Kontekstual Pada Materi Statistika

This study aims to improve students’ critical thinking skills through the implementation of a Deep Learning approach in statistics learning. The study employed Classroom Action Research (CAR) conducted in two cycles based on the Kemmis and McTaggart model, consisting of planning, action, observation, and reflection stages. The participants were 33 tenth-grade students from a senior high school in Jember Regency during the second semester of the 2025/2026 academic year. Data were collected through critical thinking tests, observations, and documentation. Students’ critical thinking skills were assessed based on five Facione indicators: interpretation, analysis, evaluation, inference, and explanation. The results showed an increase in the average critical thinking score from 37.49 in Cycle I to 80.25 in Cycle II. The percentage of students achieving the classical mastery criterion also increased from 3,03% to 75.76%. Classroom observations further indicated changes in students’ engagement during learning, particularly in group discussions and expressing their ideas. These findings suggest that the implementation of Deep Learning based on contextual data can serve as an alternative approach to support the improvement of students’ critical thinking skills in statistics learning.