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

Fritz Schiltz (), Chiara Masci (), Tommaso Agasisti () and Daniel Horn ()
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Fritz Schiltz: Leuven Economics of Education Research, University of Leuven, Belgium
Chiara Masci: Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Italy
Daniel Horn: Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences

No 1704, Budapest Working Papers on the Labour Market from Institute of Economics, Centre for Economic and Regional Studies

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.

Keywords: machine learning; education production function; interaction effects; non-linear effects (search for similar items in EconPapers)
JEL-codes: C18 C49 C5 H75 I21 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2017-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-edu and nep-ure
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Citations: View citations in EconPapers (2)

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