## 4 Results and Discussion
* Do the in-term performance predictions provide sufficient time to contribute to the students? * How early can risk predictions related to courses taken by students contribute to them? In addition to the effect of the datasets on the studies, reduced errors and more interpretable results in classification studies make them more applicable in this field. However, the analysis of individual results in regression tasks complicates the evaluation of the results since each sample has a unique error. The results obtained from the systematic literature review showed that all the reasons described above caused the implementation of classification research (62%) to be significantly higher than regression studies (38%). The considered datasets and problem domain affect the ML model selection in these studies. When the ML models are considered, it can be concluded that the ability of neural-based models to process and learn a considerable amount of data and produce successful results is an essential factor for consideration in most student performance prediction studies [15; 16; 58]. Furthermore, it has been observed that the use of recurrent neural networks, which can learn by remembering past experiences while learning data, especially in time-series data such as online datasets, has become prevalent [18; 57]. Even though the SVM and SVR models were frequently considered in the studies, the optimization of the classification/regression process is one of their crucial characteristics, while the projection of data into another feature space becomes more effective when the data are more informative. For this reason, SVM was generally considered in classification studies with a limited number of data, ensemble/hybrid models, comparative studies, or where attributes or instances were selected using data mining techniques [23; 42; 59]. Therefore, the classification and regression ability of SVM and SVR were optimized with minimized and selected data. However, the uncertainties of neural-based models and SVM-SVR regarding interpretability led researchers to implement models with successful and interpretable results. At this point, DT and RF became the focus models of the researchers. Nevertheless, DT's sensitive approach to data and the risk of obtaining low results highlighted the RF, which optimizes the DT results by producing a certain number of DTs. Thus, the researchers attempted to achieve significant results and identify the factors that directly affect student performance [25; 43]. The contribution of all machine learning models to student performance prediction studies is undeniable. The direct use of models, their use in data analysis and selection, and their use in creating hybrid or ensemble models have directed each study to lead to further developments. The literature review results showed that analysis using deep learning has gained importance in recent years [8; 57]. Additionally, the success of artificial neural network models in transferring the obtained knowledge to other models has started to be investigated in student performance predictions [60]. Since predicting which model would achieve superior results in artificial intelligence and machine learning applications is difficult, it is impossible to determine a specific model that would lead to future studies. However, the data created by developing computer and data storage technologies will make artificial neural networks and deep learning methods, which can process and learn big data, more widespread for regression and classification tasks. Furthermore, other conventional and tree-based models might be considered more frequently in the data analysis and data selection stages of studies on student performance evaluation. However, the models' abilities should not be underestimated for particular datasets with limited inputs and samples [46]. As mentioned above, the dataset, aims, and study domain directly affect the determination of evaluation metrics, which are different in both domains.