# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Predictors
Moreover,this course implemented the continuous assessment system: on top of a final exam, students needed to complete five different assignments throughout the course, being due on Weeks 2, 4, 8, 10, and 12. The predictor collected data related to student engagement from Moodle logs--such as views of course modules and submission of tasks--as well as the grades that students obtained in the assignments. Final grades were predicted using a discrete scale (marks ranging from A to D, as well as low failure and dropout), and four different classifier algorithms were tested: KNN, RF, Naive Bayes, and linear discriminant analysis (LDA). The performance of this predictor was evaluated in terms of how well it could classify students into high and low performers, the former corresponding to final grades of C or better and the latter including all other grades. RF was the best performing algorithm, yielding an accuracy of 70% after just one week and without any assignment grade data, and 87% after two assignments had been completed. However, as in the previous example, the small population may hurt the reliability of this performance evaluation. [PERSON] et al. built a co-training based student success predictor with the aim of achieving greater performance than traditional classifier algorithms [14]. Co-training is an analysis technique that consists in dividing the input data features into two independent and sufficient views, leading to the use of two predictive models and taking advantage of redundancy to improve prediction results. This study used data regarding student gender, attendance, and grades in the first view, while indicators regarding LMS activity formed the second view. The system was implemented in an introductory informatics module taught at a Greek open university, with 1073 enrolled students. Its performance was exhaustively tested, using different ratios of labeled-to-unlabeled data, as well as several combinations of classifiers for each view, including KNN, Extra Tree, RF, Gradient Boosting Classifier (GBC), and Naive Bayes. Overall, it was observed that using Extra Tree for the first view and GBC for the second one yielded the best performance. More importantly, tests that used co-training performed better than self-trained variants--without feature splitting--as this study aimed to prove. [PERSON] used item-response theory (IRT) to make estimations of students' abilities regarding the contents of a course [15]. This study targeted introductory calculus and algebra courses at a Japanese higher education institution. These courses followed a continuous assessment system in which students needed to perform a multiple-choice type exam each week. IRT allowed assessing the question difficulty together with students' abilities, resulting in a more fair judgment. Additionally, at several points in time during the course, this study used a KNN classifier in order to predict whether students would pass or fail the course. Results from the weekly tests were used as input data, representing the trends of estimated students' abilities. The predictor was tested with a population of around 1100 students. After seven weeks--midway through the course--the classifier achieved a misclassification rate as low as 18%. However, the author pointed out that the false positive rate was noticeably high, meaning that a significant portion of well-performing students were being classified as at-risk. [PERSON] tried to establish a correlation between student success and the level of violence and crime in the university campus and surrounding areas, in the context of higher education in the USA [16]. This study used data collected from a data analysis tool maintained by the US Department of Education, and included rates of violent crimes, disciplinary measures, and arrests among students. Complementary data on graduation rates and other institutional variables were pulled from a data system associated with the National Center for Education Statistics. Overall, data from 1281 higher education institutions were collected.