Educational Data Mining: Discovery Standards of Academic Performance by Students in Public High Schools in the Federal District of Brazil

摘要

This article presents results obtained in research regarding the academic performance of high school students at public schools in the Federal District of Brazil in 2015. Using CRISP-DM data mining methodology, we were able to achieve greater knowledge discovery than studies using traditional descriptive statistical analysis. Subsequently, our data shows that the variables, 'grades' and 'absences', are not the only attributes relevant to whether a student will fail at the end of the school year. Thus, this study presents data indicating other frequently reported attributes relevant to potential academic failure in this context, as well as a detailed explanation of the methodology, and the steps taken to obtain this data.