# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Predictors
The goal of the predictor was determining graduation rate, that is the fraction of students that would finish their degree program in the intended number of years. The underlying prediction algorithm was multivariate regression. As a result of this study, it was observed that institutions with higher rates of violent crime reported lower graduation rates. On the other hand, institutions that made more referrals to the student conduct system for minor offenses reported higher graduation rates, the same as those with a low number of arrests. This suggests that referrals are more constructive intervention methods than arrests. It is important to note that,as opposed to the other examples of predictors listed here, this one did not target individual students, but rather university communities as a whole. [PERSON] et al. investigated whether final grades of subjects in a specific semester could be predicted using results from courses in the previous one [17]. This study was performed in the Technological Educational Institution of Western Greece, targeting 592 first-year Business Administration students who started their studies between 2013 and 2017. Predictions were performed at the end of the first semester. Input data included student gender, final grades obtained by the student in first semester subjects on a 1-10 scale, and the number of unsuccessful attempts to pass each subject in previous semesters, if any. Several prediction algorithms were tested to estimate each student's final grade in second semester subjects, also on a scale from 1 to 10. These algorithms were linear regression (LR), RF, instance-based regression, M5 algorithm, SVM, Gaussian processes, and bootstrap aggregating. Ten-fold validation was used for performance assessment. In this study, RF outperformed all other algorithms, achieving a mean absolute error between 1.217 and 1.943 points, depending on the predicted subject. A study by [PERSON] and [PERSON] had the objective of predicting the grade point average (GPA) of a student at the end of a five-year-long degree program, using as input the GPA obtained in each of the first three years [18]. This concept resembles the work done by [PERSON] et al. : grades from already completed courses were used to predict future performance. The study was carried out at Covenant University in Nigeria, using grade data from 1841 students belonging to seven different engineering programs. Many prediction algorithms were tested and compared in this study, divided into two categories: classifiers and regression models. For classifiers, the final GPA as a prediction goal was discretized and turned into a four-level scale. The tested algorithms were probabilistic neural network, RF, decision tree, Naive Bayes, tree ensemble, and logistic regression. The best performing algorithm, achieving an accuracy of 89.15%, was logistic regression. On the other hand, linear and pure quadratic models were used to estimate the final GPA as a numeric value. These algorithms achieved R\({}^{2}\) coefficients of 0.955 and 0.957, respectively. [PERSON] et al. designed a predictive model to be used in courses following the flipped classroom teaching method [19]. This predictor aimed to estimate each student's grade in the final exam of the course. To do this, it used data regarding interactions with pre-class learning activities. These activities consisted of videos and documents with multiple choice questions, as well as sequences of problems. The input data included indicators on the regularity of student interactions with learning material and their level of engagement with each resource type. These indicators could be either generic or course-specific. Generic indicators represented information not directly related to any course, such as the frequency of LMS logins. On the other hand, course-specific indicators were related to interactions with resources belonging to an individual course. Multiple linear regression was used as the prediction algorithm.