Academic Evasion. Employment of Data Mining in the discovery of Driving Factors

      Аннотация: Dropout is one of the most intriguing and crucial problems in education. This problem permeates the various levels and modalities of education and generates social, economic, political, academic, and financial losses for all those involved in the educational process. Therefore, it is essential to develop efficient methods to predict the risk of student dropout, allowing institutions to adopt proactive actions to minimize the situation. Thus, the objective of this paper is to present a knowledge discovery approach, using a database, to identify groups of students at risk of dropping out of in-class courses of higher education.

Ключевые слова: Academic Evasion, Knowledge Discovery, Machine Learning, descoberta de conhecimento, Evasão acadêmica