Putting an Explanatory Understanding into a Predictive Perspective: An Exemplary Study on School Track Enrollment

Abstract

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Complementing widely used explanatory models in the educational sciences that pinpoint the resources and characteristics for explaining students’ distinct educational transitions, this paper departs from methodological traditions and evaluates the predictive power of established concepts: to what extent can we actually predict school track enrollment based on a plethora of well-known explanatory factors derived from previous research? Predictive models were established using recursive partitioning adopted from machine learning. The basis for the analyses was the unique Zurich Learning Progress Study in Switzerland, a longitudinal study that followed a sample of 2000 students throughout compulsory education. This paper presents an exemplary examination of predictive modeling, and encourages educational sciences in general to explore beyond the horizon of their disciplinary methodological standards, which may help to consider the limits of an exclusive focus on explanatory approaches. The results provide an insight into the predictive capacity of well-established educational measures and concepts in predicting school track enrollment. The results show that there is quite a bit we cannot explain in educational navigation at the very end of elementary education. Yet, predictive misclassifications mainly occur between adjacent school tracks. Very few misclassifications in the future enrollment of academic-track and basic-track students, i.e., those pursuing the most- and least-prestigious tracks, respectively, occur.

Keywords

• predictive modeling
• educational transitions
• school tracking
• random forests in an applied setting
• educational navigations