# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Early Warning Systems
performed multiple analytics studies with the help of Student Explorer, the first of which involved determining the reasons why students fall into a medium or high risk category [28]. This was done by using event history analysis techniques to determine the probability that a student enters one of the at-risk levels. This was tested over a population of 556 first-year students belonging to different study programs. As a result of this experiment, it was determined that the main reason students are classified into the \"engage\" level, or high risk, is underperformance in course tasks and exams. However, there was a wider array of circumstances that increased the odds of students falling into the \"explore\" category, or medium risk. These circumstances included being in large classes, sophomore level courses, and courses belonging to pure scientific degrees. * A second study by [PERSON] et al. investigated which were the best ways to help struggling students recover [29]. This study has some similarities with the previous one: this time, the authors used event history analysis to find out which intervention methods are the best for increasing the odds of students being removed from the at-risk levels. After experimenting with a population of 2169 first-year statistics students, they concluded that students at high risk benefited the most from better exam preparation, while those at medium risk required assistance in planning their study behaviors. * Lastly, [PERSON] et al. analyzed the effect of co-enrollment over student performance [30]. This study used binary logistic regression as the main analysis technique. The authors classified certain courses as \"difficult\", according to the criterion of them having a higher amount of students classified as at-risk compared to most other courses. This extension of Student Explorer was implemented in an introductory programming course with 987 enrolled students. The authors determined that, given a specific focal course, students had a significantly higher chance of entering the \"explore\" or \"engage\" categories if they were enrolled in a \"difficult\" course at the same time. There exist many other EWS that deviate from the basic formula created by Course Signals and closely followed by Student Explorer. These are applications usually tailored for use in one specific kind of learning environment. Some notable examples are described next. LADA (\"Learning Analytics Dashboard for Advisors\") was developed by [PERSON] et al. and, as its name implies, has the goal of supporting the decision-making process of academic advisors [31]. Among its features, there is a module that provides predictions of the students' chances of passing a specific course. LADA uses student grades, courses booked by a student, and the number of credits per course as input data. As for the analysis technique, it uses multilevel clustering to assess risk on a five-level scale. It does so by establishing comparisons with students that had similar profiles in previous cohorts. The system was deployed in two different universities: one located in Europe and the other in Latin America. Student advisors were generally satisfied with LADA's utility, stating that the tool allowed them to reduce the time that it took to analyze cases of individual students, enhancing the efficiency of their decision-making. SurveyConnect, created by [PERSON] et al., was presented as a tool to assist the teacher during laboratory sessions of a computer-aided design course at University of Surrey (England) [32]. This application includes features such as the ability to broadcast the computer screen of a student to the rest of the class or to remotely connect to a specific student's computer to provide assistance. The feature that turns SurreyConnect into an EWS is its analytics module, which provides predictions of students potentially at risk of being unsuccessful in the course.