# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Predictors  
Predictors in education may target a specific course or an educational program or degree as a whole. In either case, two main types of prediction goals can be identified: final grade of a student, or whether the student will succeed, fail, or drop out. A few prominent examples of predictors in educational environments are described next. [PERSON] and [PERSON] developed a Naive Bayesian classifier that was applied in 13 different courses at Rio Salado Community College (Tempe, AZ, USA) [10]. The courses belonged to degree programs in the fields of science and humanities. The study used data from the institution's LMS, related to both student engagement (determined by LMS logins and participation in online activities) and student performance (meaning the points earned in course tasks). The goal of the classifier was to predict student success, defined as obtaining a grade of C or higher in a course. For most of the courses in which this tool was tested, the classifier managed to achieve an accuracy of over 90%. However, there were three scientific courses for which accuracy dropped to values between 80% and 90%. According to the authors, this could be explained by the differences in complexity compared to other courses. This experiment was conducted with a big student population, with a training sample of 5936 students and a validation sample of 2722. The dataset was also fairly balanced, with failure and success rates of 40% and 60% respectively. [PERSON] et al. created a classifier based on logistic regression, targeting an introductory biology course taught during the first semester of a university major [11]. Similar to the previous example, this classifier had the goal of predicting whether the student would pass the course or not. However, instead of using data collected throughout the course, this study was based on results from tests with no direct relationship with the course itself. These were taken right at the start of the semester with the purpose of evaluating the students' scientific reasoning and mathematical abilities. More specifically, the tests were Lawson's Classroom Test of Scientific Reasoning and the ACT Mathematics Test. The predictor was tested with a population of 413 students, showing that the scores of the tests were significant predictors of student success, with a \(p\)-value smaller than 0.05 for both of them. The fact that a prediction of success can be made before the course even starts is certainly interesting, and it lays the groundwork for a possible concept of an EWS. However, it was acknowledged that academic ability, as measured by these two tests, is not necessarily what defines the odds of success, with factors such as motivation and engagement often playing a more important role. [PERSON] et al. presented a classifier for student success that used data related to student procrastination as input [12]. To obtain these data, students were surveyed regarding the time they spent using social networks and playing online games. Three different classification algorithms were tested: support vector machines (SVM), k-nearest neighbors (KNN), and random forest (RF), using 10-fold validation to evaluate the performance of each of them. The predictor was tested with a cohort of 100 computer science students from a university in the Philippines. The SVM classifier performed better than the ones based on KNN and RF. SVM registered an F-measure of 0.984, while KNN and RF could only reach 0.806. Nevertheless, it must be taken into account that a population of 100 students can be too small to properly evaluate a predictor. In this case, all three classification algorithms yielded a precision of 100%, a benchmark that is not expected to be reached in a bigger scenario. [PERSON] et al. attempted to estimate the earliest possible time at which a reliable prediction of the students' final performance in a course could be made [13]. This study involved 99 students enrolled in a 16-week-long introductory mathematics course taught at an Australian university.