Undergraduate Students' Effectiveness In An Institution With High Dropout Index

Currently, the dissemination of open data in conjunction with Educational Data Mining (EDM), learning analytics, e-learning, intelligent systems, intelligent tutors, and online judge techniques have had made useful contributions to the field of education through the knowledge generated from data analysis. Identifying factors that allow us to understand how students learn and their behavior has aided managers and teaching professionals to identify the best teaching settings. This study aims to do a comparison of academic success with other studies in the literature. Two thousand four hundred ninety-nine students were analyzed for over 11 years. These students belong to a Brazilian university and three undergraduate courses of computing (Computer Science, Software Engineering and Information Systems). The Statistical and data mining techniques were used to extract information that can validate the hypotheses of this study. Our Main objective is to seek which factors tend to contribute to students' retention, dropout, difficulties, and academic success. For reach this objective, we compare gender effectiveness and course curriculum grade. The data showed that some factors, not previously analyzed by other studies, tend to influence student performance.