## 2 Theoretical Background
In [39], several techniques for pre-processing data were compared in terms of interactions with the virtual environment Moodle in risk prediction. Data from the plugin Virtual Programming Laboratory (VPL) were used for risk prediction in algorithm and programming disciplines in undergraduate courses. Data such as weekly interaction count, an average of interactions, median, number of weeks without interactions, standard deviation, and commitment factor are generated based on a previously proposed technique [25; 40]. Data added included the teacher interaction count, social count, and cognitive count based on a proposed theory Swan [41]. With naturally unbalanced data, the synthetic minority over-sampling technique (SMOTE) was applied to create balance. Several datasets were generated with different variables to compare the techniques. The results demonstrated that the use of only the interaction count as proposed in [24; 25] presented results superior to the other techniques, including their union. For instance, [5] proposed a students' dropout prediction system that combines outcomes from three different algorithms (neural network, support vector machine (SVM), and probabilistic ensemble simplified fuzzy Adaptive Resonance Theory (ARTMAP--PESFAM)). The authors gathered static demographic data, like sex and place of residence; academic data, like performance and scholar degree; and dynamic data, such as the number of interactions in the virtual environment, grades, and even delivery dates of activities. After applying the algorithms, three distinct approaches to the dropout prediction were generated: (1) A student is considered a dropout case if at least one method classified them as such, (2) a student is considered as a dropout if at least two methods indicated the student to be a dropout and, (3) the student is only presumed as a dropout if all three techniques classified them as a dropout. The accuracy of the results obtained ranged from 75% to 85%, and the best results were achieved using the less restrictive approach, the first one, which achieved accuracies up to 85% on the first section of a given course. [PERSON] et al. [26] proposed a warning system focused on student performance to reduce dropout and retention rates. The system provides the student with updated feedback on their potential scholarly performance. To do so, the system uses several types of data, such as demographic (sex and age), student interactions on the VLE, previous academic performance, time passed since the student entered the university, online time spent on the VLE, and outcomes from the scholastic aptitude test (SAT) (verbal and math). Different models of prediction were produced using J48, Bayesian networks with naive Bayes, SVM with minimal sequential optimization (SMO), and logistic regression, considering data from 9938 students. These classifiers presented similar results, with the classifier based on logistic regression producing slightly superior outcomes (94.2% general accuracy and 66.7% precision for identifying students at dropout risk). A classifier able to early predict student dropout using students' interactions inside a VLE was proposed [42]. They used information such as if the student watched all video tutorials, if the student ignored some given material or activity, if the student was delayed in following the virtual classes, and the student performance in the activities. Students were then classified according to three flags: Green (low dropout risk), yellow (medium dropout risk), and red (high dropout risk). The authors did not mention the types of machine learning algorithms used but reported performance (TP accuracy) varying from 40% to 50% to predict dropout students within two weeks in advance. Genetic algorithms are widely used in data mining and can be implemented as the classifier or as a result of the optimizer, as proposed in this approach. One of the applications of genetic algorithms for optimization is a method combining the predictions generated by classifiers.