Simulation Modeling and Analysis
Simulation Modeling and Analysis
User Modeling and User-Adapted Interaction
Architecture of Systems Problem Solving
Architecture of Systems Problem Solving
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
WBE'06 Proceedings of the 5th IASTED international conference on Web-based education
Using genetic algorithms for data mining optimization in an educational web-based system
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Extraction of logical rules to describe students' learning behavior
WBED'07 Proceedings of the sixth conference on IASTED International Conference Web-Based Education - Volume 2
Neural Networks to Predict Schooling Failure/Success
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Handling Uncertainty and Multiple Perspectives for Learner Modeling by Cognitive Mapping
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Nowadays, e-learning systems such as virtual campus environments have established themselves as a strong alternative to traditional distance education. In turn, the Internet allows the gathering of information on many aspects of students' online behavior in nearly real time. The knowledge extracted from this information can be used to discover students' learning behavior and usability patterns of the courses, as well as to anticipate future behavior related with the teaching-learning process. In this brief study we use the Fuzzy Inductive Reasoning methodology to predict the final mark of the users of a real virtual campus and to determine relevant features involved in the evaluation process. Experiments carried out with the available data indicate that the final mark can be predicted with a low error and that the number of relevant features identified is small, reducing considerably the complexity of the evaluation process and minimizing the teachers' workload.