## 4 Results and Discussion
In general, the results of the models generated by GA per the AUC were satisfactory, allowing the prediction of at-risk students in the early stages of the courses. Data were naturally balanced, with similar percentages of dropout and success students. The models developed here produced similar or better results in comparison to some of the works in the literature that focused on the early prediction of dropout students. According to [31; 52], one of the main factors involved in the acceptance of learning analytics by teachers and students when using prediction models is the correctness rates involved in the process. The GA proposed in this work was able to increase these rates compared to the results obtained in previous works [23; 24]. However, direct comparison with these experiments is somewhat complicated, as they used the true positive (TP) and true negative (TN) of the models as metrics, and we used AUCROC. In these previous experiments, the results obtained in scenarios similar to this experiment showed rates of TP and TN varying between 58 and 82 in the first 25 weeks of the course. However, with the approach proposed in this work, it was possible to reach an initial AUCROC above 0.75, which increased over the first 25 weeks until reaching values above 0.90. The comparison with prominent works of predictors of educational environments is necessary to situate the results obtained. Some limitations for comparison include the various techniques used to measure the results, such as accuracy, TP, TN, AUC, and AUCROC, among others [32] [PERSON] et al. [33] [PERSON] et al. [30]. Still, a significant part of the works on LA are characterized by the exploration of data from disciplines of a specific course or semester, whereas the work presented in this paper is characterized by the use of data from a course of two years in duration [32]. However, even when we compare the results obtained with the related works, the rates are satisfactory. Previous studies [PERSON] et al. [5], [PERSON] [37], [PERSON] et al. [38] reported rates of 85% and [PERSON] et al. [26] reported 94% overall accuracy, but only 66.7% dropout prediction. The results obtained in the optimization with the proposed GA are close to those of the literature [PERSON] and [PERSON] [43], which obtained an optimization of 12%. The proposed GA was able to reach values above 10% in the experiments until the 20 th week compared to the algorithms in their standard configuration. When compared to the other optimization method, Gridsearch, in that same period, GA obtained values always above 6%, sometimes reaching 15%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hyperparameter Optimization** & **Hidden Layer** & **Activation** & **Solver** & **Alpha** & **max Iter** & **Warm Start** & **AUC** \\ \hline yes & 30 & logistic & sgd & 0.2855486101 & 353 & False & 0.9154 \\ no & 100 & relu & adam & 0.0001 & 200 & False & 0.849 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between configurations of models for week 25. The method followed here is the result of an incremental process of a series of experiments previously performed [23; 24]. As such, when comparing the results achieved in this work with the results from previous actions, the hyperparameters generated by the GA allows the generation of more robust models and higher performance. This is also demonstrated in comparison to the other methods tested in this article. Thus, the methodology used both for the development of the GA and for the generation of input data from genetic algorithms demonstrated that it could be used for early prediction of students at risk of dropout. Concerning data modeling, although the use of interaction count is not unprecedented, the methodology used in this study has several attributes that produced the results.