Abstract
Assessing knowledge acquisition by the student is a main task of an Intelligent Tutoring System. Assessment is needed in order to adapt learning materials and activities to students capacities. To evaluate knowledge acquisition, different techniques can be used, such as probabilistic inference. In this paper we present a proposal based on Bayesian Networks to infer the level of knowledge possessed by the student. We implemented a kind of test to know what student knows. During the test, the software system chooses the new questions based on the responses to the previous ones, that is, the software system makes an adaption in real time. To get the inferences, we use a network of concepts, which contains the relationships between those concepts. This work is focused on the design of the Bayesian Network and the algorithm to do inferences about students knowledge.
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Alan, RN., Reyes, JR., Yobani, MR., Samantha, J., Sergio, I. (2016). Using Bayesian Networks for Knowledge Representation and Evaluation in Intelligent Tutoring Systems. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-31232-3_16
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DOI: https://doi.org/10.1007/978-3-319-31232-3_16
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