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The aim of this study was to identify and evaluate the most frequently used research methods and factors influencing academic performance, based on a pool of 95 studies, published after 2012. We considered only peer-reviewed papers containing 78 empirical and 17 meta-analytic studies. Our theoretical background lies in the different approaches of the terms 'university dropout' and 'academic performance'. After the systematic analysis we ascertained the most commonly used methods are Educational Data Mining (EDM) algorithms (decision tree, logistic regression and neural networks) and Structural Equation Modelling (SEM). The strength of the predictive power depends on the dataset, however Support Vector Machines, Multilayer Perceptron, Naïve Bayes algorithm were found to be the most precise in prediction. Regarding factors influencing academic performance we derived our results based on 600,000 university students. Considering the data from meta-analyses and systematic reviews, reaching up to 900 studies, we found grade point average (GPA), obtained credits (ECTS) and gender to be the most consistent and decisive predictors of academic performance. Nevertheless, GPA and ECTS (as output variables) are mediated by student factors (intrinsic motivation, self-regulated learning strategies, self-efficacy, prior education) and throughput factors (work, finances, academic engagement). We had contradictory results on age and family background.
Kocsis et al. (Tue,) studied this question.