Implementation of Deep Learning to Improve Critical Thinking Skills of Elementary School Students

Abstract

Education in the 21st century faces increasingly complex demands along with the rapid development of technology, information, and social change. This study aims to analyze the application of Deep Learning in improving the critical thinking skills of elementary school students. The study used a quantitative approach with a quasi-experimental method through a one-group pretest-posttest design. The research subjects were 30 elementary school students selected using a purposive sampling technique. Data collection was carried out through critical thinking skills tests, observation, and documentation. Data were analyzed using descriptive and inferential statistics through normality tests and paired sample t-tests, as well as N-Gain analysis to determine the level of improvement in students' critical thinking skills. The results showed that the application of Deep Learning was able to improve students' critical thinking skills. The average pretest score of 62.40 increased to 82.17 in the posttest, with an increase of 19.77 points. Improvements occurred in all critical thinking indicators, namely information interpretation, problem analysis, argument evaluation, conclusion drawing, and the ability to provide logical explanations. The results of statistical tests showed a significance value of 0.000 <0.05, which indicates a significant difference between students' critical thinking skills before and after the application of Deep Learning. The application of learning through problem exploration, discussion, information analysis, problem-solving, argumentation, and reflection creates a more active and meaningful learning experience. Thus, Deep Learning can be an effective alternative learning strategy to improve elementary school students' critical thinking skills and support the development of higher-order thinking skills in students.