## 1 Introduction
[PERSON] and [PERSON], in [13], suggested a model that uses an interaction-based graph neural network module to learn local academic performance representations from online interaction activities and an attribute-based graph neural network to learn global-academic performance representations from the attribute features of all students, by using dynamic graph convolution operations. [PERSON] et al. proposed two novel approaches, namely, Conv-GRU and P-NNN, to predict students' learning performance and explain the predicted results in [14]. In this study, k-nearest neighbors, support vector regressor, decision tree, random forest, adaptive boosting algorithm, extra trees regressor, gradient boosting regressor, bagging regressor, histogram-based gradient boosting regressor, and extreme gradient boosting methods are used to predict students' academic performance. In the modeling process, variables such as students' demographic information, academic performance, and attendance rates were evaluated. The main objective of this study is to compare the prediction success of different machine learning algorithms and determine the best model. In order to improve the performance of machine learning algorithms and minimize the risk of model overfitting, hyperparameter optimization was performed. Hyperparameter optimization ensures that the most appropriate values of certain parameters are selected, in order to ensure the best performance of the model. In this study, hyperparameter optimization is performed using a genetic algorithm and with the use of grid search methods. With the genetic algorithm method, which is inspired by evolutionary computational techniques, natural selection, mutation, and crossover mechanisms are used to determine the optimal hyperparameter combination. The grid search method aims to find the best parameter combination by systematically searching within predetermined hyperparameter ranges. The models were retrained using the best hyperparameter combinations determined for each model, and their overall performances were evaluated using 10-fold cross-validation. The optimization process played a critical role in improving the success of machine learning algorithms on training data sets, especially by revealing how small changes in hyperparameter selection impact model accuracy. Besides these methods, the performance of machine learning models is evaluated using the metrics of the determination coefficient, mean absolute error, mean squared error, root mean squared error, mean squared error, mean squared log error, maximum error, explained variance, and ten additional metrics. Thus, by analyzing the performance of the hyperparameter-optimized machine learning algorithms, their applicability relative to different data sets can be evaluated. This study aims to provide a decision support mechanism for predicting student achievement in educational institutions. By providing a reference model for the prediction of the potential success of students in educational institutions, it contributes to a more effective adaptation of teaching strategies. It provides educational administrators with a decision support mechanism that can be used for the early identification of students at risk of low performance and the provision of necessary academic support. By emphasizing the benefits of the effective application of educational data mining methods, it guides future academic-achievement prediction studies. Considering that the existing models for success prediction in education are mostly evaluated with default parameters, and the effect of hyperparameter optimization on model success has infrequently been examined, this study aims to fill this gap by comparatively applying multiple optimization methods. In the following section, the machine learning models used in this study are explained, and in the third section, the data and the data preprocessing method are given. In the fourth section, hyperparameter optimization techniques and model comparisons are discussed in detail.