Inferring knowledge from active learning simulators for physics
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Active Learning Simulators (ALS) allow students to practice and carry out experiments in a safe environment - at any time, and in any place. Furthermore, well-designed simulations may enhance learning, and provide the bridge from conceptual to practical understanding. By adding an Intelligent Tutoring System (ITS), it is possible to provide personal guidance to students. The main objective of this work is to present an ALS suited for a Physics scenario in which we incorporate elements from ITS, and where a Probabilistic Relational Model (PRM) based on a Bayesian Network is used to infer student knowledge, taking advantage of relational models. A discussion of the methodology is addressed and preliminary results are presented. Ours first results go in the right direction as proved by a relative learning gain. © 2009 Springer-Verlag Berlin Heidelberg.