Predicting Student Dropout and Academic Success

Higher education institutions record a significant amount of data about their students, representing considerable potential to generate information, knowledge, and monitoring. Both school dropout educational failure in higher are an obstacle economic growth, employment, competitiveness, productivity, directly impacting the lives students families, institutions, society as whole. The dataset described here results from aggregation information different disjointed sources includes demographic, socioeconomic, macroeconomic, academic on enrollment performance at end first second semesters. is used build machine learning models for predicting dropout, which part Learning Analytic tool developed Polytechnic Institute Portalegre that provides tutoring team with estimate risk failure. useful researchers who want conduct comparative studies student also training area.