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

ALKAN B. B., Kuzucuk S., Odabasi Ş. Y., Karakuş L.

PLOS ONE, cilt.20, sa.7 July, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 7 July
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1371/journal.pone.0326121
  • Dergi Adı: PLOS ONE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, Chemical Abstracts Core, Food Science & Technology Abstracts, Index Islamicus, Linguistic Bibliography, MEDLINE, Pollution Abstracts, Psycinfo, zbMATH, Directory of Open Access Journals
  • Akdeniz Üniversitesi Adresli: Evet

Özet

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.