DocumentCode :
2963478
Title :
Creating adaptive learning paths using Ant Colony Optimization and Bayesian Networks
Author :
Márquez, José Manuel ; Ortega, Juan Antonio ; González-Abril, Luis ; Velasco, Francisco
Author_Institution :
R&D Dept., Telvent, Sevilla
fYear :
2008
fDate :
1-8 June 2008
Firstpage :
3834
Lastpage :
3839
Abstract :
This paper presents a new way to combine two different approaches of artificial intelligence looking for the best path in a graph, ant colony optimization and Bayesian networks. The main objective is to develop a learning management system which will have the capability of adapting the learning path to the learnerpsilas needs in execution time, taking into account the pedagogical weight of each learning unit and the systempsilas social behavior.
Keywords :
belief networks; learning (artificial intelligence); optimisation; Bayesian networks; adaptive learning paths; ant colony optimization; artificial intelligence; learning management system; Ant colony optimization; Bayesian methods; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2008.4634349
Filename :
4634349
Link To Document :
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