Introduction:

This article aims to obtain models based on probabilistic case analysis to help decision-making in the education and learning of UTEQ students. To obtain the final product, the development process has been distributed in several stages.

Objective:

To create a probabilistic model to evaluate and diagnose students based on a set of characteristics, which must be learned automatically through a generalization of the AutoClass model allowing the existence of hidden variables, each of them affecting a set different from observable variables (students' answers to questions raised by an automatic learning system).

Materials and methods:

Our study will be carried out to define another form of structural learning based on the search of structures through evolutionary meta-heuristic models.

Results:

This model will allow the authorities of the UTEQ to identify inconveniences and setbacks in the teaching-learning process. At the same time, the results obtained will allow immediate decision-making to solve the problems detected and thus fulfill the institutional mission of training professionals with a scientific and humanistic vision capable of developing research, creating technologies, maintaining and disseminating our ancestral knowledge and culture, for the construction of solutions to the problems of the region and the country.

Conclusions:

were metaheuristic variable mesh optimization (VMO) to structural learning of Bayesian network classifiers (BVMO).

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