In our previous works, we presented Logic-Muse as an Intelligent Tutoring System that helps learners improve logical reasoning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians and reasoning psychologists). A Bayesian network with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner’s behaviour. This paper presents an evaluation of the learner components of Logic-Muse. We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. This data was used in the development a psychometric model, a key element for initializing the learner’s model and for validating and improve the structure of the initial Bayesian network built with experts.
CITATION STYLE
Nkambou, R., Brisson, J., Robert, S., & Tato, A. (2021). Learning Logical Reasoning : Improving the Student Model with a Data Driven Approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12677 LNCS, pp. 60–67). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80421-3_7
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