The objective of Model@ is to implement an automated system that can collect data from the UOC datamart and generate predictive models with them.
These data extracted from the UOC datamart are processed with ETL tools, according to different grouping criteria, in order to generate the necessary data models to be introduced to the calculation of the predictive models.
A critical feature of the project is the elasticity of the system: through different definitions in files, indicate which are the data to collect from the datamart and what is the structure of the models. In this way, it ensures that all configurations can be modified dynamically avoiding having to change the code.
For example, one of the implemented models analyzes information related to the student's activity to make predictive estimates around the possibility of success in their evaluation.
The inLab is in charge of processing the data and preparing them to be able to introduce them in the predictive model and execute the models specified by the UOC.