# Educational improvement through machine learning: Strategic models for better PISA scores

## TL;DR

This study uses machine learning to identify key factors influencing student success in PISA exams, offering insights for better education policies.

## Contribution

The study introduces new prediction models using variables like access to IT and metacognition to improve PISA scores.

## Key findings

- Access to information technology significantly impacts student success in PISA exams.
- Metacognition and weekly instructional hours are key predictors of student achievement.
- Proposed models help policymakers design effective education strategies for better PISA outcomes.

## Abstract

In this study, in addition to traditional variables such as economic wealth or the number of books read, on which many studies have already been conducted, variables that are thought to influence student achievement and better predict success are identified. Random Forest algorithm was used to identify important variables based on the PISA 2018 data, covering all three domains of science, mathematics and reading. The study found that the main factors influencing the success of students in countries that perform well in the PISA exam are essentially access to information technology, weekly hours of instruction in the subject, economic-social and cultural status, parents’ occupation, level of metacognition, awareness of PISA, sense of competition and attitudes towards reading. New prediction models based on these variables were proposed. The proposed models will give a significant advantage to policy makers who want to improve their country’s PISA score and implement appropriate education policies.