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This study identified key factors beyond traditional metrics that predict student success in science, math, and reading. Access to technology and instructional hours significantly impact performance on international assessments like the Programme for International Student Assessment (PISA).

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Area of Science:

  • Educational Research
  • Data Science in Education
  • International Comparative Studies

Background:

  • Traditional studies on student achievement often focus on economic wealth and reading habits.
  • Existing research may not fully capture the multifaceted nature of academic success across diverse educational systems.
  • The Programme for International Student Assessment (PISA) provides a crucial dataset for understanding global educational performance.

Purpose of the Study:

  • To identify novel variables that significantly influence student achievement and predict academic success.
  • To move beyond conventional predictors by exploring a broader range of socio-economic, pedagogical, and psychological factors.
  • To develop enhanced predictive models for student performance in science, mathematics, and reading.

Main Methods:

  • Utilized the Random Forest algorithm for variable importance analysis.
  • Analyzed the PISA 2018 dataset, encompassing science, mathematics, and reading domains.
  • Identified key predictive variables influencing student success in high-performing countries.

Main Results:

  • Key factors identified include: access to information technology, weekly instructional hours, economic-social and cultural status, parental occupation, metacognitive awareness, PISA awareness, competitive spirit, and reading attitudes.
  • These variables demonstrated significant predictive power for student success in the PISA 2018 assessment.
  • New prediction models incorporating these variables were developed.

Conclusions:

  • Student success is influenced by a combination of technological access, instructional time, socio-economic background, and cognitive/attitudinal factors.
  • The proposed models offer a significant advantage for policymakers aiming to enhance national PISA scores.
  • These findings can guide the implementation of targeted educational policies to improve student outcomes.