Logic-Muse is an Intelligent Tutoring System (ITS) that helps
improve deductive reasoning skills in multiple contexts. All its three main
components (The learner, the tutor and the expert models) have been developed
while relying on the help of experts and on important work in the field of
reasoning and computer science. It is now known that one can’t support a
student in a learning task without being aware of his level of skills (what he/she
knows and what he/she needs to know). Thus, it is important in the setting up of
the learner model to consider an efficient mechanism that can both assess and
predict her skills. This paper describes the Bayesian Network (that allows real
time diagnosis, prediction and modeling of the learner’s state of skills) imple-
mented in the learner component of Logic-Muse. We proved that the BN
(Bayesian Network) is able to predict with an accuracy near 85%, the answers of
learners on different exercises of the domain. Given this result, the system is
therefore able to predict the learner’s deductive reasoning skills at a given time
and help the tutor model for a better assessment and coaching.