Abstract
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 actuallypredictschool 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.
Highlights
Historical and current practice in the educational sciences has relied considerably on explanatory modeling (Hofman et al, 2017; Yarkoni and Westfall, 2017)
The almost exclusive methodological restriction and narrowing in favor of explaining empirical observations can result in the establishment and preservation of theoretically elegant explanatory models and concepts, which may have very limited capacity to predict actual human behavior and outcomes (Hofman et al, 2017; Yarkoni and Westfall, 2017); or at least, the preservation of theories and concepts for which we have no baseline of knowledge of their predictive capacity
We argue that when predictive modeling is applied as a complement on the basis of well-established explanatory concepts, this would enable educational research to explore the predictive power of their explanatory models
Summary
Historical and current practice in the educational sciences has relied considerably on explanatory modeling (Hofman et al, 2017; Yarkoni and Westfall, 2017). On this basis, an elaborate understanding of moderating and mediating relationships explaining educational outcomes is built. Asking how accurate empirically well-established explanations can predict educational outcomes means that it is possible to gauge how much is still “unknown.” the empirically founded understanding from explanatory research is put into perspective. This article explores this issue based on an exemplary study on school track enrollment in Switzerland; a well-researched topic from an explanatory angle in the educational sciences
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