Computer-controlled systems: theory and design (2nd ed.)
Computer-controlled systems: theory and design (2nd ed.)
Using artificial neural nets to predict academic performance
SAC '96 Proceedings of the 1996 ACM symposium on Applied Computing
A Practical Student Model in an Intelligent Tutoring System
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
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
Early and dynamic student achievement prediction in e-learning courses using neural networks
Journal of the American Society for Information Science and Technology
A reactive blended learning proposal for an introductory control engineering course
Computers & Education
Learning motivation in e-learning facilitated computer programming courses
Computers & Education
Bayesian networks for student model engineering
Computers & Education
IEEE Transactions on Education
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The goal of the work is to improve the teaching-learning process through the inclusion of prediction features in a control system proposal namely Reactive Blended Learning. To achieve this goal, a model of the student has been proposed, whose considered outputs are the performance and a participation index that measures the activity level of the student in the class. The controller is based on fuzzy logic and uses the predictions of the model to anticipate the student's state. An important issue that has been taken into account is the limited time to identify the dynamics of the student learning before the course ends. This limitation has been treated through a three-stage process. It is important to remark that this work is not focused on obtaining a complete student model, but on getting useful information for the detection of trends in the teaching-learning process. Preliminary results on a real course are presented to attest the efficiency of the proposed control strategy.