## 3 Results of Systematic Literature Review
### Publication Numbers and Aims  
The total number of publications extracted by the literature research conducted between 2010 and 2020 from the three citation databases mentioned above was 297, but this number decreased to 176 when duplicate studies and the publications that met the exclusion criteria were excluded. It was observed that studies on student performance prediction using artificial intelligence started to gain attention after 2015. While 83.5% (147/176) of the total number of unduplicated studies were published between 2016 and 2020, only 16.5% (29/176) were published between 2010 and 2015. Figure 2 demonstrates the number of publications in the citation databases per year and the publication ratios for the specified years. We can evaluate the aims of the studies from many perspectives, such as machine learning properties or educational implications (i.e., performance evaluation using historical or current data of student records to predict end-of-term or in-term student performance). When we consider the implementation of machine learning in studies, they can initially be categorized into regression and classification tasks. In contrast, regression implementations are aimed at predicting the raw end-of-term points that students can obtain (i.e., 85, 23, 98, etc.). Student achievement can be predicted according to categorized classes in classification applications (i.e., Low-Average-High, Passed-Failed, etc.). In this context, it was observed that the studies obtained in the literature review primarily focused on classification. A total of 62% (109/176) of the studies in the literature were on classification, whereas only 38% (67/176) were regression studies. We can also categorize the studies according to their targeted education level as Higher Education (HE) and High School/Secondary Level (HS/S). The evaluation of the publications demonstrated that the research on student performance prediction primarily focused on the Higher Education level, although studies included both levels. The frequency of publications on the HE Level was 76.60% (135/176), while the frequency of HS/S Level studies was 23.40% (41/176). Figure 3 presents a bar chart showing the regression and classification studies for HS/S, HE, and all levels. Figure 2: The number of publications between 2010 and 2020 (\(n=297\)). The main purpose of the studies should also be evaluated by considering the educational expectations that they directly target. The primary aims of the studies that stand out in the literature without differentiating as regression and classification applications can be generalized as estimation of course drop-outs [21; 22], end-of-term/exam success level prediction [51; 52; 53], in-term success prediction [14], students' risk identification [18; 19; 20], and the estimation of future success [54] for a particular course. Even though the studies were separated into five primary aims, the frequency of the end-of-term success prediction studies was the highest (60.60%--107/176). However, the studies on the prediction of students' in-term success have gained importance in the last few years because of the increase in big data obtained from online platforms. The frequency of in-term prediction publications was 18.20% (32/176). These two primary applications were followed by drop-out estimation and student-at-risk studies (9.00%--15/176 and 6.10%--11/176, respectively). However, it should be noted that we can assign drop-out and student-at-risk studies to the end-of-term or in-term categories because these studies simultaneously focused on determining the drop-out probability of a particular course during or at the end of the semester. The ability of ML models enables all studies to be implemented for estimating future success if they are expanded or modified. However, in this study, the publications focused directly on determining the student success for the following semesters are categorized into future success estimation. As a result, the frequency of these studies is only 6.10% (11/176) within all studies.