AbstractThe transition from high school to university is a critical step and many students head toward failure just because their final degree option was not the right choice. Both students’ preferences and skills play an important role in choosing the degree that best fits them, so an analysis of these attitudes during the high school can minimize the drop out in a posteriori learning period like university. We propose a subgroup discovery algorithm based on grammars to extract itemsets and relationships that represent any type of homogeneity and regularity in data from a supervised context. This supervised context is cornerstone, considering a single item or a set of them as interesting and distinctive. The proposed algorithm supports the students’ final degree decision by extracting relations among different students’ skills and preferences during the high school period. The idea is to be able to provide advices with regard to what is the best degree option for each specific skill and...