An interdisciplinary area that combines data analysis, computer science, and educational psychology to enhance learning and educational environments. By collecting, analyzing, and interpreting data about learners and their contexts, it provides insights to personalize education, identify students who need more support, and improve educational outcomes.

The Undergraduate Analysis Model (MAGRA) enables the prediction of dropout rates in face-to-face undergraduate courses.

The Academic Management Support System - SAGA is a tool developed to assist academic managers in monitoring indicators for undergraduate courses.

Full Professor at the University of Brasília (UnB), working in the undergraduate Software Engineering program and the Graduate Program in Applied Computing.

Professor at the University of Brasília (UnB), working in the undergraduate Software Engineering program.

Master in Applied Computing, University of Brasília

Paper presented at IEEE ITHET 2024 in Paris receives Best Paper award.

CEDIS professors stand out in teaching innovation with an honorable mention, in the enhancement of Learning Indicators.

Book on Learning Indicators launched with the participation of CEDIS researchers.