Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling

This paper evaluates Logic-Muse, an Intelligent Tutoring System designed to help learners improve logical reasoning skills through a hybrid student modeling approach combining Bayesian Knowledge Tracing and Deep Knowledge Tracing, validated with data from nearly 300 students completing 48 reasoning activities. The system uses expert-validated Bayesian networks to model student knowledge and provides adaptive scaffolding for learning logical reasoning in propositional logic.

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 catalog of reasoning errors (syntactic and semantic) has been established, in addition to an explicit representation of semantic knowledge and the structures and meta-structures underlying co

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