Applying a Support Vector Machine (SVM-RFE) Learning Approach to Investigate Students’ Scientific Literacy Development: Evidence from Asia, Europe, and South America

Abstract

Cultivating scientific literacy is a goal widely shared by educators and students around the world. Many studies have sought to enhance students’ proficiency in scientific literacy through various approaches. However, there is a need to explore the attributes associated with advanced levels of scientific literacy, especially the influence of contextual factors. In this context, our study employs a machine learning technique—the SVM-RFE algorithm—to identify the critical characteristics of students with strong scientific literacy in Asia, Europe, and South America. Our research has pinpointed 30 key factors from a broader set of 162 contextual factors that are indicative of outstanding scientific literacy among 15-year-old secondary school students. By utilizing student samples from the three continents, our study provides a comprehensive analysis of these factors across the entire dataset, along with a comparative examination of the optimal set of key factors between continents. The findings highlight the importance of these key factors, which should be considered by educational policymakers and school leaders when developing educational policies and instructional strategies to foster the most effective development of scientific literacy.