Creation of new attributes to forecast student loyalty

The loyalty and retention of students in educational institutions has become one of the greatest challenges for the management area of these institutions. A promising solution to achieve this goal is the use of educational data mining to identify patterns that aid in decision making. This paper presents a proposal for the creation of temporal attributes with the purpose of helping to predict the avoidance of elementary school students in private schools, treated as a classification problem. After the application of the classification algorithms, it was verified that the KNN classifier obtained the best accuracy before the use of the new attribute, but the best algorithm to predict avoidance in the context of this research was the Decision Tree J4.8, since the Even allows the interpretation of the factors that led to the final result. The results show that the approach is viable, with an accuracy of up to 97.87% in the experiments performed.