Abstract
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) implemented 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.
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Tato, A., Nkambou, R., Brisson, J., Robert, S. (2017). Predicting Learner’s Deductive Reasoning Skills Using a Bayesian Network. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_32
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