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Beyond scores: A machine learning approach to comparing educational system effectiveness.

Rogério Luiz Cardoso Silva Filho1,2,3, Anvit Garg1, Kellyton Brito4

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This study introduces a novel machine learning approach for unbiased educational system comparisons, moving beyond average scores to analyze regional and state-level effectiveness in Brazil from 2009-2019.

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Area of Science:

  • Educational research
  • Comparative education
  • Machine learning applications in education

Background:

  • Large-scale international assessments are crucial for understanding global education delivery.
  • Existing analyses often rely on average student scores, lacking contextual depth.
  • Traditional parametric models for educational effectiveness have limitations with large datasets and strong assumptions.

Purpose of the Study:

  • To introduce a flexible, model-independent machine learning approach for comparing educational system effectiveness.
  • To enable paired comparisons and overcome limitations of traditional parametric models.
  • To analyze differences in educational effectiveness across Brazilian administrative units (regional and state) from 2009 to 2019.

Main Methods:

  • Development of a novel machine learning methodology for educational effectiveness analysis.
  • Application of the new approach to large-scale assessment data from Brazil (2009-2019).
  • Comparison of machine learning results with traditional methods.

Main Results:

  • The machine learning approach proved suitable for exploring educational effectiveness differences in Brazil.
  • Results align with existing literature while revealing novel findings.
  • The methodology identified new insights not captured by traditional comparative approaches.

Conclusions:

  • Machine learning offers a flexible and powerful alternative for unbiased educational system comparisons.
  • The new method enhances the understanding of educational effectiveness by considering system contexts.
  • This approach provides valuable new perspectives on educational disparities within Brazil.