Keywords
AI in education
Bayesian networks
Student model
How to Cite
Abstract
Pedagogical practices supported by computational resources, especially those that incorporate Artificial Intelligence techniques, can help predict the level of knowledge of students in virtual learning environments. In this context, this article presents a hybrid approach, based on Bayesian Networks and ontologies, to process information about students' level of knowledge and behavior and, thus, measure their performance. A dynamic, probabilistic, domain-independent, extensible and reusable student model was created. An extension of the model was also presented to allow visualization of students' capabilities and limitations. As a case study, the proposed model was integrated into an educational platform, serving as a basis for validating and experimenting with the approach.
References
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