# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Early Warning Systems
Since this application was specifically designed to be used in laboratory environments, it is able to use some input data that are not available in other EWS, such as where the students are seated within the lab and who their neighbors are. Additionally, class attendance and time spent doing exercises are also tracked. The significance of each type of input data was determined running an ANOVA test, and the variables correlated with student performance were identified using Pearson correlation. Through these tests, it was observed that class attendance and time spent doing tasks had a direct relationship with learning outcomes. Additionally, the location of a student in the classroom and the identity of the closest neighbors also had an impact over performance. [PERSON] et al. were the authors of an experiment featuring the use of an EWS in a practical statistics course at University College Dublin [33]. This course implemented a continuous assessment system: 40% of the final grade was awarded for completing a series of weekly tasks throughout the course, which had a total duration of 12 weeks. The main goal of this study was determining the best time to perform a prediction of each student's final grade: early enough so that corrective measures can be more effectively applied to low-performing students, but not so early that predictions are unreliable due to insufficient information. The input data were obtained from the university's LMS, Blackboard, and included demographic information, number of accesses to learning resources, and the results from the aforementioned weekly tasks. Eight different predictive algorithms were tested, with Bayesian Additive Regressive Trees (BART) yielding the best results. The EWS was able to predict the final grade of the students with a mean absolute error of 6.5% by Week 6, exactly halfway through the course. This performance proves that the EWS was able to make reliable enough predictions at early stages in the course, when corrective measures taken by the teacher can be most effective. Over at Hangzhou Normal University in China, [PERSON] et al. developed an EWS with the objective of reducing student dropout and minimizing delays in graduation [34]. This application stands out due to the types of input data that it uses. As with many similar tools, information related to student grades, attendance to classes, and use of online learning resources is used for prediction purposes. However, this EWS also includes records from the university library, as in the books that are borrowed; and the dormitory, as in the times at which a student enters and leaves the dorm. This extra information enables the possibility of more closely monitoring student habits. This EWS classifies students on a seven-level scale, regarding the nature and severity of the risks that they are exposed to: underperformance, graduation delay, or dropout. The tool was tested for three consecutive semesters using a sample of 1712 students, trying three different classification algorithms: decision tree, artificial neural network, and Naive Bayes. Out of the three algorithms, Naive Bayes yielded the best results, obtaining an accuracy of 86%. Additionally, a principal component analysis showed that student grades and book borrowing trends were the most important indicators for predictions. [PERSON] hypothesized that students dropping out from a course will first stop actively using said course's websites and resources. Moreover, this behavior could lead to dropout from degree studies altogether. Keeping this in mind, an EWS was built with the purpose of analyzing student activity in a quantitative way in order to provide an early identification of learner dropout [35]. The tool was developed in the context of a large Israeli university. As input data, it collected student activity indicators from the institution's LMS, Moodle. These indicators included the type and number of actions performed in the LMS, as well as their timing and frequency.