A Framework for Building Intelligent Tutoring Systems
Abstract
Intelligent tutoring systems provide customized instruction or feedback to learners, without intervention from a human teacher. This feature causes that intelligent tutoring systems attract attention because they allow learning everywhere, every time and the cost of courses is cheaper than traditional in-class learning. In this work we propose a formal framework for building intelligent tutoring systems. The particular elements of those systems such as: learner profile, domain model, methods for determination and modification of a learning scenario and for computer adaptive tests are presented. Additionally, we describe an application of rough classification in e-learning systems. The conducted experiments and analysis demonstrate that the personalization has a significant influence on a learning process and the probability of passing all lessons from the learning scenario is greater if the opening learning scenario is selected using a worked-out methods than chosen in a random way. The obtained results proof the correctness of our assumptions and have significant implications for development of intelligent tutoring systems.
Keywords
Bayesian Network Item Response Theory Knowledge Structure Learning Material Hamiltonian PathPreview
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References
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