  Methods based on machine learning become increasingly popular in many areas
as they allow models to be fitted in a highly-data driven fashion, and often
show comparable or even increased performance in comparison to classical
methods. However, in the area of educational sciences the application of
machine learning is still quite uncommon. This work investigates the benefit of
using classification trees for analyzing data from educational sciences. An
application to data on school transition rates in Austria indicates different
aspects of interest in the context of educational sciences: (i) the trees
select variables for predicting school transition rates in a data-driven
fashion which are well in accordance with existing confirmatory theories from
educational sciences, (ii) trees can be employed for performing variable
selection for regression models, (iii) the classification performance of trees
is comparable to that of binary regression models. These results indicate that
trees and possibly other machine learning methods may also be helpful to
explore high-dimensional educational data sets, especially where no
confirmatory theories have been developed yet.
