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Student and school performance across countries: A machine learning approach

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  • Masci, Chiara
  • Johnes, Geraint
  • Agasisti, Tommaso

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.

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  • Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
    • Handle: RePEc:eee:ejores:v:269:y:2018:i:3:p:1072-1085
    DOI: 10.1016/j.ejor.2018.02.031
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    1. Steven G. Rivkin & Eric A. Hanushek & John F. Kain, 2005. "Teachers, Schools, and Academic Achievement," Econometrica, Econometric Society, vol. 73(2), pages 417-458, March.
    2. Hanushek, Eric A & Rivkin, Steven G & Taylor, Lori L, 1996. "Aggregation and the Estimated Effects of School Resources," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 611-627, November.
    3. Stephen W. Raudenbush, 1988. "Educational Applications of Hierarchical Linear Models: A Review," Journal of Educational and Behavioral Statistics, , vol. 13(2), pages 85-116, June.
    4. Ian Plewis, 2011. "Contextual variations in ethnic group differences in educational attainments," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 419-437, April.
    5. Masci, Chiara & Ieva, Francesca & Agasisti, Tommaso & Paganoni, Anna Maria, 2016. "Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 47-57.
    6. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    7. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    8. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 533-575.
    9. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    10. C. Masci & F. Ieva & T. Agasisti & A. M. Paganoni, 2017. "Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1296-1317, May.
    11. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    12. Savona, Roberto, 2014. "Hedge fund systemic risk signals," European Journal of Operational Research, Elsevier, vol. 236(1), pages 282-291.
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