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- W2127659628 abstract "Intelligent tutoring systems (ITS) are interactive learning environments based on instruction assisted by computers. The intelligence of these systems is largely attributed to their ability to adapt to a specific student during the teaching process. In general, the adaptation process can be described by three phases: (i) getting the information about the student, (ii) processing the information to initialize and update a student model, and (iii) using the student model to provide the adaptation. In this paper we studied aspects related with student modeling (SM) in intelligent tutoring systems. First we make a qualitative comparison of two techniques: Bayesian networks (BN) and case-based reasoning (CBR) for SM. We apply both techniques to a case study concerning the development of an ITS for e-learning in the medical domain. Finally, we discuss the results obtained" @default.
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- W2127659628 date "2006-01-01" @default.
- W2127659628 modified "2023-09-26" @default.
- W2127659628 title "A Qualitative Comparison of Techniques for Student Modeling in Intelligent Tutoring Systems" @default.
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- W2127659628 doi "https://doi.org/10.1109/fie.2006.322537" @default.
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