No abstract

This manuscript reports the implementation of an Educational Data Warehouse (EDW) at the Federal Institute of North of Minas Gerais by using data from the academic system called Cajuí. The logical model of the system is the Fact Constellation with data persisted into the relational DBMS PostgreSQL. After the loading and setting of the EDW, we ran a set of analytic queries regarding courses from the Computer Science bachelor course at the IFNMG campus of Montes Claros for the 2013/2020 timespan. The data analysis indicates: (i) there were no significant differences in the academic performances of students enrolled by either standard entrance or Brazilian SISU exams, (ii) the number of unofficial dropouts reached up to 19% of students, (iii) the 19.51% of students that took a leave of absence and 15.38% of dropout bachelor candidates had completed at least 1/3 of courses from the entire graduation process, (iv) the first-year courses had more failing students than final-year courses, and the average grade of final-year courses was higher than those of other years, and (v) nearly 60% of students had at least one failure in either Algorithms and Data Structures or Calculus courses.

Decision-support systems benefit from hidden patterns extracted from digital information. In the specific domain of gastropod characterization, morphometrical measurements support biologists in the identification of land snail specimens. Although snails can be easily identified by their excretory and reproductive systems, the after-death mollusk body is commonly inaccessible because of either soft material deterioration or fossilization. This study aims at characterizing Brazilian land snails by morphometrical data features manually taken from the shells. In particular, we examined a dataset of shells by using different learning models that labeled snail specimens with a precision up to 97.5% (F1-Score = .975, CKC = .967 and ROC Area = .998). The extracted patterns describe similarities and trends among land snail species and indicates possible outliers physiologies due to climate traits and breeding. Finally, we show some morphometrical characteristics dominate others according to different feature selection biases. Those data-based patterns can be applied to fast land snail identification whenever their bodies are unavailable, as in the recurrent cases of lost shells in nature or private and museum collections.