## 4 Results and Discussion
The most significant problem encountered in classification experiments is the accuracy obtained in experiments using imbalanced data. In a classification study, where a class with a large number of samples can be better learned, achieving a high accuracy result does not provide information about the efficiency of the other class results, and the accuracy causes misleading results. Therefore, the vast majority of students can be identified in classes, such as pass or fail, risky or not, etc. In imbalanced data, the F1 score and ROC AUC score are evaluated as metrics that show the model's overall performance more efficiently. In contrast, recall and precision metrics show the success level for specific classes and offer insight for learning the relevant classes. For this reason, the use of the accuracy metric alone might lead to difficulties in the real-life implementation of studies. The standardization of the F1 and ROC AUC scores in all studies would contribute significantly to the studies' analysis, the success of models, and the trend of future studies for classification studies. Similarly, the standardization of the \(R^{2}\) score, which provides a scaled and consistent evaluation of the models, would provide a more effective evaluation of the proposed models. However, using additional metrics in regression problems is essential to measure error minimization since the results might differ from obtaining high \(R^{2}\) scores. In this case, it is challenging to determine which metric is more efficient, while the aim in measured data would be the determinant. Therefore, the use of a minimum of two error metrics, such as MAE and MSE (or root MSE), could be standardized for all student performance prediction studies. In both problem domains, the applicability of the obtained results can be defined directly proportionally to the validation techniques. In order to optimize the learning process, a method that is widely used is to select the instances to be used in training with different techniques. This could increase training and testing accuracy while reducing computational time in big data. However, in studies where attributes are selected rather than instances, it should be considered that each different student entry contains new and independent data for both the training and test phase. Studies have shown that other training instances could change the accuracy by more than 10% [2,46]. The hold-out method does not use all samples during the training and testing phases. It also creates problems in determining the ratio of dividing the dataset into training, testing, or validation sets. Nevertheless, even the hold-out method is preferred to reduce the computational time for big data; computers today can perform a vast number of operations in student performance prediction studies. For this reason, in studies where instances are not analyzed and selected, k-fold cross-validation, which considers each sample during the learning and testing phase, should be a standard approach for training the model. The average number of folds results should be evaluated as the overall success rate of the model. This will provide a more objective approach for evaluating the obtained results, and the actual abilities of the models will be determined as a result of the evaluations made by considering all the data. Many different factors, such as changes in the conditions of student admission to educational institutions in countries, education abroad, foreign language level, education in the mother tongue, cultural differences, demographic structure, personal preferences, place of residence, weather conditions of the country of residence, health conditions, etc., and factors that directly affect the university lives of students, were reported in different studies [64, 65, 66]. Additionally, it is known that students' skills and their inclination and interest in courses also affect the level of success.