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Download full textEdelsbrunner, Peter; Schneider, Michael – Frontline Learning Research, 2013
Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…
Descriptors: Prediction, Statistical Analysis, Structural Equation Models, Academic Achievement
Korpershoek, Hanke – Frontline Learning Research, 2016
The aims of the present study were (1) to identify to what extent school motivation and school commitment contributed to the explanation of students' academic achievement in addition to the effect of students' cognitive capacities, (2) to find out whether school commitment mediated the relation between school motivation and academic achievement,…
Descriptors: Correlation, Secondary School Students, Student Motivation, Academic Achievement
