## 5 Final Remarks
#### Rq2. Does the resulting predictive models generated by the use of the GA approach for hyperparameter optimization perform better than models with default hyperparameters? In comparison with the classifications using the default hyperparameters, the GA produced significantly better results. In the first 20 weeks of the course, the difference between the two methods varies from 10% and 15%. [PERSON]'s test demonstrated that the overall values obtained are significantly different. However, all techniques have advantages and limitations. The drawback of the GA is the lack of assurance that the solution is global; the positive aspect is the number of resultant hyperparameters accepted without significantly altering the processing cost and the final results. In grid-search, the computational cost is the biggest issue, as previously reported; however, it delivers the best possible combination of hyperparameters. Concerning the standard classifiers, we highlight the cost-benefit factor as the method produces satisfactory results in short processing time, which, depending on the project, can be an essential point. The main limitation of the proposed methodology presents is that for each course analyzed, the calendar must be studied to identify periods without classes, such as holidays. This causes extra work, which does not occur when socio-demographic data are used. Another limitation concerns generalization; although the methodology may be generalized, the models are unlikely to be suitable for courses that do not follow the same timetable as ETec. Models that seek long-term predictions are more susceptible to failures due to external situations, such as economic and epidemiological crises. An important point to note is that the GA possibly presents slightly different results for each execution. Thus, it may be interesting to run the GA multiple times (e.g., 10). Analysis of other metrics, such as overall accuracy and true positive (TP) and true negative (TN), may provide different perspectives. The application of other hyperparameter search methods, such as random search, and algorithms, such as XGBOOST, can still be explored. These questions will possibly be studied in the future stages of this project, as well as hybrid choice methods such as the vote theory, for final classification selection. The results obtained in this work enable the development of an early warning system using the proposed approach. Currently, the development of this system is occurring in the form of a plugin integrated with Moodle. Another future work toward improving the results is the application of survival analysis to increase student retention and consequently reduce dropout. Author Contributions: E.M.Q. : Experimental data analysis, algorithms development, data pre-processing, experiments conduction, results description, and manuscript writing; J.L.L. : Course lecturer, conceived and designed, and methodology definition; K.K. : Algorithms development and writing review; M.A. : manuscript writing and algorithms development; R.M.A. : Algorithms development and data pre-processing; R.V. : writing--review and editing; R.M. : writing--review and editing. C.C. : methodology definition, algorithm developed, experiments setup, manuscript writing, and writing--review and editing. The final manuscript was written and approved by all authors. All authors have read and agreed to the published version of the manuscript. This work was supported by CNPq (Brazilian National Council for Scientific and Technological Development) [Edital Universal, proc.404369/2016-2][DT-2 Productivity in Technological Development and Innovative Extension scholarship, proc.315445/2018-1]. R.V. and R.M. were funded by Corporacion de Fomento de la Produccion (CORFO) 14 ENI2-26905 \"Nueva Ingenieria para el 2030\"--Pontificia Universidad Catolica de Valparaiso, Chile. The authors declare no conflict of interest.