Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
User Modeling and User-Adapted Interaction
INSPIRE: An INtelligent System for Personalized Instruction in a Remote Environment
Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE
User Modeling and User-Adapted Interaction
Adaptive feedback generation to support teachers in web-based distance education
User Modeling and User-Adapted Interaction
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Hybrid model for learner modelling and feedback prioritisation in exploratory learning
International Journal of Hybrid Intelligent Systems - CIMA-08
ICCOM'10 Proceedings of the 14th WSEAS international conference on Communications
Context-dependent feedback prioritisation in exploratory learning revisited
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
An empirical study on the quantitative notion of task difficulty
Expert Systems with Applications: An International Journal
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In this paper we propose a method that implements student diagnosis in the context of the Adaptive Hypermedia Educational System INSPIRE - INtelligent System for Personalized Instruction in a Remote Environment. The method explores ideas from the fields of fuzzy logic and multicriteria decision-making in order to deal with uncertainty and incorporate in the system a more complete and accurate description of the expert's knowledge as well as flexibility in student's assessment. To be more precise, an inference system, using fuzzy logic and the Analytic Hierarchy Process to represent the knowledge of the teacher-expert on student's diagnosis, analyzes student's answers to questions of varying difficulty and importance, and estimates the student's knowledge level. Preliminary experiments with real students indicate that the method is characterized by effectiveness in handling the uncertainty of student diagnosis, and is found to be closer to the assessment performed by a human teacher, when compared to a more traditional method of assessment.