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One of the greatest challenges for computer science in education is the capacity to provide environments that are intelligent and adaptable to the real needs of students. In order to create efficient adaptive mechanisms for educational content, student models are proposed to identify and to predict the real knowledge level of students. Such models are useful not only for computer systems but also...
Adapting an educational environment to students considering its features and individuals is a necessity due to the large amount of learning objects in the repositories. Thus, organizing learning objects so that they can be efficiently recommended is a real need. In this way, this work presents a proposal for clustering learning objects in repositories considering the learning styles they support,...
Adaptive Educational Systems (AES) make use of Artificial Intelligence techniques aiming at adapting themselves to the real needs of the student, and through such provide a personalized and individualized teaching. In order for this adaptation to be successful, it is important that the system knows the level of knowledge concerning the real cognitive state of the students. In this manner, this article...
Dynamic adaptation of educational content has been an important research topic. Therefore, in order for it to run effectively, student models that properly describe and monitor the cognitive state of students are needed. In this sense, this paper presents a hybrid student model approach that combines ontologies and Bayesian Networks to identify the knowledge of each student based on their characteristics...
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