Machine learning is a powerful method for modeling in different fields such as education. Its capability to accurately predict students success makes it an ideal tool for decision-making tasks related to higher education. The accuracy of machine learning models depends on selecting the proper hyper-parameters. However, it is not an easy task because it requires time and expertise to tune the hyper-parameters to fit the machine learning model. In this paper, we examine the effectiveness of automated hyper-parameter tuning techniques to the realm of students success. Therefore, we develop two automated Hyper-Parameter Optimization methods, namely grid search and random search, to assess and improve a previous studys performance. The experiment results show that applying random search and grid search on machine learning algorithms improves accuracy. We empirically show automated methods superiority on real-world educational data (MIDFIELD) for tuning HPs of conventional machine learning classifiers. This work emphasizes the effectiveness of automated hyper-parameter optimization while applying machine learning in the education field to aid faculties, directors, or non-expert users decisions to improve students success.
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