## 4 Results and Discussion  
Artificial intelligence and machine learning have been studied in educational sciences as well as in all areas of life. However, research differs in terms of its purpose, the models used, datasets, evaluation criteria, and validation strategy. From the point of view of machine learning, although every study contributes to and accelerates student performance evaluations, the differences make it challenging to implement these studies in real life. Discussions on various subjects have been performed to provide a broad perspective on student performance prediction studies based on the results presented above. Datasets have a significant impact on the aims of studies and can directly affect the domain of the study, the choice of ML model, and the evaluation metrics used. Questionnaires cannot generally be used for in-term estimations because of their reliability and discrete training events when they are obtained. However, they continue to be considered in research on the impact of family and personal preferences on student achievements. Online data have been widely used in end-of-term, in-term, and drop-out studies. The recording of students' interest in the courses at a certain level and as time-series in these datasets has enabled artificial intelligence and machine learning models to learn more meaningful data and produce more successful results. In addition, they have reduced the number of people that the surveys need to reach, the effort, and the financial costs by obtaining data in digital environments in the infrastructures of educational institutions. [PERSON] et al. (2019) have taken the studies one step further by using a dataset that includes observations of students' activities and educational habits on campus. Therefore, in the most basic way, where and how much time students spend, the effects of the books they buy from the library, and other aspects could be observed, and student performance predictions could be performed accordingly. The implementation of classification and regression tasks in student performance prediction studies is also related to the characteristics of the data collected and the aims of the studies. The frequently used Student Performance Dataset ([PERSON] et al., 2019) allows researchers to perform investigations in regression and classification domains with the raw exam results provided. On the other hand, in other datasets ([PERSON] et al., 2019; [PERSON] et al., 2019), the results were classified based on categorized performances or letter grades of students, which complicates the implementation of a regression study on these datasets. The data content might allow researchers to perform prediction studies in the classification domain (i.e., students' withdrawal from the course, end-of-term success: pass/fail) or in the regression domain (i.e., end-of-term success: exam point). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Level** & **Type** & **Courses/Modules** & **\# of Students** \\ \hline SPD & SE & Questionnaire & 2 & 649 and 395 \\ Kaggle MOOC & HE & Online & 247 & \textgreater{}155,000 \\ WorldUC Dataset & HE & Online & 10 & 10,523 \\ Liru Dataset & HE & Online & 18 & 1046 \\ Junyi Dataset & HE & Online & 18 & 2063 \\ OULAD & HE & Online & 22 & 32,593 \\ Real-world dataset smart card & HE & C. Behaviors & - & 9207 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Common datasets and properties. Regardless of whether the studies are in regression or classification, when they are categorized under the five main headings identified above (estimation of course drop-outs, end-of-term success level prediction, in-term success prediction, students' risk identification, and future success estimation), different questions might be asked from an educational perspective:  
* Do drop-out predictions contribute significantly to the student and their success levels? * When does the prediction of end-of-term success contribute to students' self-development and their education?