  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 study's 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.
