Dropping out of Higher Education contributes to great social, eco- nomic and academic loss. Among the main reasons for dropping out are the student’s difficulty in following the content, the struc- ture proposed by the course and the lack of financial resources. In recent years, several studies have emerged to try to identify groups of students at risk of dropping out, either by identifying the factors that can contribute to dropout, or by creating classifiers based on Machine Learning. However, researches focus essentially on categorical indicators, that is, with binary results, which denote that the student is or is not in the risk group. This type of anal- ysis is important, however, it does not show the variation in the student’s performance during their academic life, in addition to not offering a score within a performance score. Differently, this project use Machine Learning techniques in the creation of a Score, in order to provide a thermometer to analyze how close the student is or not to the dropout group. Preliminary results are promising, because when using KNN to create the Score, it was possible to develop a Score with the best result of hyperparameters found in the experiments.