# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 4 Discussion
Implementing a classifier to predict which students are at risk of failing a course is by far the most popular goal of the predictors and EWS described in this review, appearing in 15 out of the 26 selected papers. It is worth mentioning that some of the documents define this goal with different words, such as \"predicting low and high-performing students\", but, in practice, these studies are trying to achieve the same objective. The following goal in terms of popularity is the prediction of student grades, which could be of an exam, a course or the average of a term or degree program. A few other papers focused on estimating students' risk of dropping out of a course or degree program. The study published by [PERSON] [16] did not fall into any of the previous three categories, as its prediction goal was the graduation rate: the fraction of students that finish their studies in the intended number of years. This was also the only predictor that analyzed data corresponding to academic institutions as a whole, rather than individual students. Figure 4: Number of papers by publication year. Another important detail is the general prevalence of classifiers over regression algorithms in these applications. Classifiers are typically used in the assessment of failure and dropout risks, since the prediction outputs are categorical variables. Regression algorithms were mostly used for numerical estimations of student grades. It was apparent that for most contexts, and especially in the case of EWS, the prediction of categorical outcomes such as \"failing or succeeding student\" provides enough information to instructors, advisors, and/or students in order to implement corrective measures if needed. Results provided by regression algorithms, on the other hand, are usually not as reliable as those obtained with classifiers, and, in many cases, estimations of numeric grades are unnecessary considering the purpose that these tools are trying to fulfill. In fact, the main research goal of some of the studies using regression algorithms was not the prediction outcome itself, but rather assessing the strength of correlations between inputs and outputs, as seen in the papers by [PERSON] [16] and [PERSON] and [PERSON] [22]. In terms of specific prediction algorithms, the most commonly used classifiers were Naive Bayes, logistic regression, RF, KNN, SVM, and neural network. Meanwhile, regression-based predictors usually relied on some variant of the linear regression algorithm. However, many authors tried more obscure algorithms, or even self-defined ones, such as Student Explorer's \"student success algorithm\" [24]. Additionally, one importance characteristic of many of these studies is their experimental nature, leading them to try out and compare the performance of many different algorithms. Examples of this trend are [PERSON] et al. [36], who tested 13 different classification algorithms, and [PERSON] and [PERSON] [18], who tried both classifiers and regression algorithms in their work. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Prediction Goal** & **Type** & **Number Published** \\ \hline Risk of failing a course & Classification & 15 \\ Dropout risk & Classification & 3 \\ Grade prediction & Regression & 7 \\ Graduation rate & Regression & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of papers by prediction goal. Figure 5: Number of papers by location where the study took place. One of the most important defining characteristics of each predictive application was the types of data that it used as input. Table 3 shows how many studies made use of each category of input data. It can be observed that the most common indicators by far are those related to student performance and engagement. Performance is measured in terms of students' grades in past exams, tasks, or courses. Most authors used this information if available, since past grades are always a strong indicator of how the student will perform in the future.