The study by Ádám Kocsis and Gyöngyér Molnár aims to identify and evaluate the most frequently used research methods and factors influencing academic performance in higher education. The authors analyzed 95 studies, including 78 empirical and 17 meta-analytic studies, published after 2012. The most commonly used research methods are Educational Data Mining (EDM) algorithms (decision tree, logistic regression, and neural networks) and Structural Equation Modelling (SEM). The predictive power of these methods depends on the dataset, with Support Vector Machines, Multilayer Perceptron, and Naive Bayes algorithms being the most precise. Regarding factors influencing academic performance, GPA, obtained credits (ECTS), and gender were found to be the most consistent and decisive predictors. However, GPA and ECTS are mediated by student factors (intrinsic motivation, self-regulated learning strategies, self-efficacy, prior education) and throughput factors (work, finances, academic engagement). The study also found contradictory results for age and family background. The authors conclude that GPA/CGPA, ECTS, and gender are the strongest predictors of academic performance, while throughput factors contribute to these output variables. They also highlight the need for a standardized theoretical framework and questionnaires to facilitate comparison and research in this field.The study by Ádám Kocsis and Gyöngyér Molnár aims to identify and evaluate the most frequently used research methods and factors influencing academic performance in higher education. The authors analyzed 95 studies, including 78 empirical and 17 meta-analytic studies, published after 2012. The most commonly used research methods are Educational Data Mining (EDM) algorithms (decision tree, logistic regression, and neural networks) and Structural Equation Modelling (SEM). The predictive power of these methods depends on the dataset, with Support Vector Machines, Multilayer Perceptron, and Naive Bayes algorithms being the most precise. Regarding factors influencing academic performance, GPA, obtained credits (ECTS), and gender were found to be the most consistent and decisive predictors. However, GPA and ECTS are mediated by student factors (intrinsic motivation, self-regulated learning strategies, self-efficacy, prior education) and throughput factors (work, finances, academic engagement). The study also found contradictory results for age and family background. The authors conclude that GPA/CGPA, ECTS, and gender are the strongest predictors of academic performance, while throughput factors contribute to these output variables. They also highlight the need for a standardized theoretical framework and questionnaires to facilitate comparison and research in this field.