Merging Information Using Uncertain Gates: An Application to Educational Indicators

Knowledge provided by human experts is often imprecise and uncertain. The possibility theory provides a solution to handle these problems. The modeling of knowledge can be performed by a possibilistic network but demands to define all the parameters of Conditional Possibility Tables. Uncertain gates allow us, as noisy gates in probability theory, the automatic calculation of Conditional Possibility Tables. The uncertain gates connectors can be used for merging information. We can use the T-norm, T-conorm, mean, and hybrid operators to define new uncertain gates connectors. In this paper, we will present an experimentation on the calculation of educational indicators. Indeed, the LMS Moodle provides a large scale of data about learners that can be merged to provide indicators to teachers. Therefore, teachers can better understand their students’ needs and how they learn. The knowledge about the behavior of learners can be provided by teachers but also by the process of datamining. The knowledge is modeled by using uncertain gates and evaluated from the data. The indicators can be presented to teachers in a decision support system.