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Using Machine Learning To Model Interaction Effects In Education: A Graphical Approach

Author

Listed:
  • Fritz Schiltz

    (Leuven Economics of Education Research, University of Leuven, Belgium)

  • Chiara Masci

    (Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Italy)

  • Tommaso Agasisti

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Italy)

  • Daniel Horn

    (Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences)

Abstract

Educational systems can be characterized by a complex structure: students, classes and teachers, schools and principals, and providers of education. The added value of schools is likely influenced by all these levels and, especially, by interactions between them. We illustrate the ability of Machine Learning (ML) methods (Regression Trees, Random Forests and Boosting) to model this complex ‘education production function’ using Hungarian data. We find that, in contrast to ML methods, classical regression approaches fail to identify relevant nonlinear interactions such as the role of school principals to accommodate district size policies. We visualize nonlinear interaction effects in a way that can be easily interpreted.

Suggested Citation

  • Fritz Schiltz & Chiara Masci & Tommaso Agasisti & Daniel Horn, 2017. "Using Machine Learning To Model Interaction Effects In Education: A Graphical Approach," Budapest Working Papers on the Labour Market 1704, Institute of Economics, Centre for Economic and Regional Studies.
    • Handle: RePEc:has:bworkp:1704
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    References listed on IDEAS

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    1. Nicholas Bloom & Renata Lemos & Raffaella Sadun & John Van Reenen, 2015. "Does Management Matter in schools?," Economic Journal, Royal Economic Society, vol. 0(584), pages 647-674, May.
    2. Gabor Kertesi & Gabor Kezdi, 2011. "The Roma/Non-Roma Test Score Gap in Hungary," American Economic Review, American Economic Association, vol. 101(3), pages 519-525, May.
    3. Robert Haveman & Barbara Wolfe, 1995. "The Determinants of Children's Attainments: A Review of Methods and Findings," Journal of Economic Literature, American Economic Association, vol. 33(4), pages 1829-1878, December.
    4. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    5. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    6. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    7. 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.
    8. Brambor, Thomas & Clark, William Roberts & Golder, Matt, 2006. "Understanding Interaction Models: Improving Empirical Analyses," Political Analysis, Cambridge University Press, vol. 14(1), pages 63-82, January.
    9. Coelli, Michael & Green, David A., 2012. "Leadership effects: school principals and student outcomes," Economics of Education Review, Elsevier, vol. 31(1), pages 92-109.
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    Cited by:

    1. Vincze, János & Takács, Olga, 2018. "Bérelőrejelzések - prediktorok és tanulságok [Wage forecasts predictors and lessons]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 592-618.
    2. Rebai, Sonia & Ben Yahia, Fatma & Essid, Hédi, 2020. "A graphically based machine learning approach to predict secondary schools performance in Tunisia," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).

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    More about this item

    Keywords

    machine learning; education production function; interaction effects; non-linear effects;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare

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