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

Jianing Wang,

Eryong Xue

et al.

Journal of Intelligence, Journal Year: 2024, Volume and Issue: 12(11), P. 111 - 111

Published: Nov. 5, 2024

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

Language: Английский

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

Jianing Wang,

Eryong Xue

et al.

Journal of Intelligence, Journal Year: 2024, Volume and Issue: 12(11), P. 111 - 111

Published: Nov. 5, 2024

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

Language: Английский

Citations

0