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Applying a Support Vector Machine (SVM-RFE) Learning Approach to Investigate Students' Scientific Literacy

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This study identified 30 key factors influencing scientific literacy in students across Asia, Europe, and South America. These findings offer crucial insights for educators and policymakers aiming to improve science education globally.

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Area of Science:

  • Educational Psychology
  • Machine Learning in Education
  • Global Education Studies

Background:

  • Scientific literacy is a key educational objective worldwide.
  • Existing research often overlooks contextual factors influencing advanced scientific literacy.
  • A gap exists in identifying specific attributes linked to high scientific literacy.

Purpose of the Study:

  • To identify critical factors associated with advanced scientific literacy in secondary students.
  • To explore the influence of contextual factors on scientific literacy.
  • To compare key factors across different continents.

Main Methods:

  • Utilized the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm, a machine learning technique.
  • Analyzed a broad set of 162 contextual factors.
  • Examined student samples from Asia, Europe, and South America.

Main Results:

  • Pinpointed 30 key factors indicative of outstanding scientific literacy.
  • Provided a comprehensive analysis of these factors across the entire dataset.
  • Conducted a comparative examination of optimal factors between continents.

Conclusions:

  • The identified 30 factors are crucial for developing effective educational policies.
  • Educational policymakers and school leaders should consider these factors to foster scientific literacy.
  • Findings offer a data-driven approach to enhancing science education globally.