Compiling Possibilistic Networks To Compute Learning Indicators

University teachers, who generally focus their interest on pedagogy and students, may find it difficult to manage e-learning platforms which provide learning analytics and data. But learning indicators might help teachers when the amount of information to process grows exponentially. The indicators can be computed by the aggregation of data and by using teachers' knowledge which is often imprecise and uncertain. Possibility theory provides a solution to handle these drawbacks. Possibilistic networks allow us to represent the causal link between the data but they require the definition of all the parameters of Conditional Possibility Tables. Uncertain gates allow the automatic calculation of these Conditional Possibility Tables by using for example the logical combination of information. The calculation time to propagate new evidence in possibilistic networks can be improved by compiling possibilistic networks. In this paper, we will present an experimentation of compiling possibilistic networks to compute course indicators. Indeed, the LMS Moodle provides a large scale of data about learners that can be merged to provide indicators to teachers in a decision making system. Thus, teachers can propose differentiated instruction which, better corresponds to their student's expectations and their learning style.