## 4 Survey Results
### Experimental Datasets and the Context of Performance Prediction  
All selected studies reported using at least one educational dataset to test its prediction model or understand the factors influencing the attainment of student outcomes. Thirty-two studies (51.6%) reported collecting performance data from traditional classroom learning, 21 studies (33.8%) from virtual learning environments, and nine studies (14.5%) from blended learning environments (e.g., a mix of online and face-to-face learning activities). The performance prediction models were applied to the university (72.58%), school (25.81%), and kindergarten data (1.61%). When we explored the type of degrees pursued by the students whose performance was being predicted, 43 (69.35%) studies examined the performance of undergraduate university students, and seven (11.29%) studies investigated the performance of high school students. Only two studies (3.22%) reported examining the performance of postgraduate students [43; 44]. When we looked at the context of prediction, the datasets and prediction models were applied mainly to courses in the natural sciences field (i.e., STEM) (33 studies, 53.22%). Figure 7 shows less emphasis on the courses belonging to the social sciences field (8 studies, 12.90%). More precisely, learning outcomes predictions were developed for Computer Science (13 studies, 20.96%), Mathematics (5 studies, 8.06%), and Engineering majors (4 studies, 6.45%), as depicted in Figure 8.  
Figure 6: Frequency of articles based on the number of authors in each publication. Figure 7: Discipline of academic performance predictions using learning outcomes. Since the studies were performed in 23 different countries, we chose to cluster them per continent, as shown in Figure 9. Twenty-five (40%) studies took place in the USA alone, followed by Europe (18 studies, 29%) and Asia (13 studies, 20%). Moreover, the training datasets for 59 studies were collected from one country only. However, two studies collected their data from students enrolled in more than one country [45,46]. All models attempted to predict academic outcomes except for one [47], which predicted academic and non-academic outcomes. The non-academic outcomes were measured using students' self-reports of self-esteem, satisfaction with life, and sense of meaning. Thirty-seven (59.67%) experimental datasets were collected from the same environment (i.e., a single school or university). However, there are a few studies that expanded their data collection activities to multiple schools or universities within the same district, e.g., ref. [48] collected student data from 750 schools, [49] from 113 schools, and [45] from 5 universities. Generally, it can be observed that the studies investigating student performance in schools collected their educational data from multiple schools, as reported in Table 5. In contrast, studies investigating academic performance in higher education employed data from a single university (36 articles, 58.06%). However, 12 articles did not specify the number of schools or universities involved in the data collection process. Figure 8: Frequency of prediction studies per type of major. Figure 9: Distribution of studies across the continents. When we inspected the number of courses from which the experimental data were drawn, we discovered that ten studies used a single course/subject, eight studies used two courses, and three studies used four courses, amounting to 35.48% of the surveyed articles. Moreover, 18 (29.03%) studies used between four courses (i.e., [80]) and 270 courses (i.e., [70]) to test the correctness of their predictive models. Nonetheless, it was unclear how many courses were used in the remaining 22 (35.48%) studies. The experimental datasets included performance data about as little as less than 1000 students (50% of the surveyed studies). Figure 10 shows that the number of studies using datasets including 1001 to 10,000 students, amounts to 13 (20.96%) articles.