Universities in Latin America commonly gather much more information about their students than allowed by data protection regulations in other parts of the world. We have tackled the question of whether abundant socio-economic data can be harnessed for the purpose of predicting academic outcomes and, thereby, taking proactive actions in student attention, course planning and resource management. A study was conducted to analyze the data gathered by a private university in Ecuador over more than 20 years, to normalize them and to parameterize a Multi-Layer Perceptron neural network, whose best-performing configuration would be used as a benchmark for the comparison of more recent and sophisticated Artificial Intelligence techniques. However, an extensive scan of hyperparameters for the perceptron—exploring more than 12,000 configurations—revealed no significant relationships between the input variables and the chosen metrics, suggesting that there is no gain from processing the extensive socio-economic data. This finding contradicts the expectations raised by previous works in the related literature and in some cases highlights important methodological flaws.

Nowaday, several researches focused on human beings have highlighted the importance of considering the psychological factor as part of their profile to understand their performance and behavior. The objective of this article was to create an ontology of the psychological profile that considers the personality traits and facets of the user that incorporates concepts and properties to formally model the user's profile. For the construction of our ontology we use the NeOn methodology. In addition, for the development of the ontological network, relevant ontologies have been taken into account in the scientific field focused on the domain of the user such as: FOAF, VCARD and SKOS. By means of an alignment of these ontologies with a central module of personality traits and a specific module for the integration of the IPIP Test NEO-PI-R, it has been possible to efficiently interoperate the profile information. In the experimentation phase it was possible to obtain the SPARQL query traceability to obtain the registered responses of a person in the IPIP Test NEO-PI-R.

The Higher Education Initiative (HEI) promoted by UNESCO and other international institutions, proposes four fundamental postulates related to the sustainable development. From these postulates, one focuses on the cooperation with local and international networks. In the same way, it is important mentioning that in every group, it members are freely interacting individuals who share accepted norms and goals and have a collective identity. For these reasons, during the university training process, it is fundamental that students learn skills to work in groups as well as to address problems in multidisciplinary teams. In the light of the above, in this paper, we present an intelligent system that determines the best alternatives to automatically generate groups of students to address practice activities and problems. Our proposal uses a genetic algorithm to analyze the personality of the students and their academic profiles. The achieved results are encouraging and show a precision of 87% according to an expert team that evaluated the system.