Predicting School Transition Rates in Austria with Classification Trees

Autor/inn/en	Möller, Annette; George, Ann Cathrice; Groß, Jürgen
Titel	Predicting School Transition Rates in Austria with Classification Trees
Quelle	In: International Journal of Research & Method in Education, 46 (2023) 5, S.538-551 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Möller, Annette) ORCID (Groß, Jürgen)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-727X
DOI	10.1080/1743727X.2022.2128744
Schlagwörter	Foreign Countries; Learning Analytics; Classification; Artificial Intelligence; Prediction; Educational Research; Regression (Statistics); Transitional Programs; Austria + Suchen Sie Ihr Suchwort? Ausland; Classification system; Klassifikation; Klassifikationssystem; Künstliche Intelligenz; Vorhersage; Bildungsforschung; Pädagogische Forschung; Regression; Regressionsanalyse; Österreich
Abstract	Methods based on machine learning have 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 analysing 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, and (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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01