Home > Research > Publications & Outputs > Student and School Performance Across Countries

Electronic data

  • article_Masci_2018

    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 269, 3, 2018 DOI: 10.1016/j.ejor.2018.02.031

    Accepted author manuscript, 943 KB, PDF document

    Embargo ends: 20/02/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Student and School Performance Across Countries: a Machine Learning Approach

Research output: Contribution to journalJournal article

Published
Close
<mark>Journal publication date</mark>16/09/2018
<mark>Journal</mark>European Journal of Operational Research
Issue number3
Volume269
Number of pages14
Pages (from-to)1072-1085
Publication statusPublished
Early online date20/02/18
Original languageEnglish

Abstract

In this paper, we develop and apply novel machine learning and statistical methods to analyse the determinants of students' PISA 2015 test scores in nine countries: Australia, Canada, France, Germany, Italy, Japan, Spain, UK and USA. The aim is to find out which student characteristics are associated with test scores and which school characteristics are associated to school value-added (measured at school level). A specific aim of our approach is to explore non-linearities in the associations between covariates and test scores, as well as to model interactions between
school-level factors in affecting results. In order to address these issues, we apply a two-stage methodology using flexible tree-based methods. We first run multilevel regression trees in the first stage, to estimate school value-added. In the second stage, we relate the estimated school value- added to school level variables by means of regression trees and boosting.

Results show that while several student and school level characteristics are significantly associated to students' achievements, there are marked differences across countries. The proposed approach allows an improved description of the structurally different educational production functions
across countries.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 269, 3, 2018 DOI: 10.1016/j.ejor.2018.02.031