AHAM: a Dexter-based reference model for adaptive hypermedia
Proceedings of the tenth ACM Conference on Hypertext and hypermedia : returning to our diverse roots: returning to our diverse roots
The ant colony optimization meta-heuristic
New ideas in optimization
User Modeling and User-Adapted Interaction
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
AHA! The adaptive hypermedia architecture
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Swarm-based Sequencing Recommendations in E-learning
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Computer
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Personalization of web-based systems based on computational intelligence modeling
CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
Computational intelligence-based personalization of interactive web systems
WSEAS Transactions on Information Science and Applications
Evolutionary computation approaches to the Curriculum Sequencing problem
Natural Computing: an international journal
Nature-Inspired Clustering Algorithms for Web Intelligence Data
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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In e-learning initiatives content creators are usually required to arrange a set of learning resources in order to present them in a comprehensive way to the learner. Course materials are usually divided into reusable chunks called Learning Objects (LOs) and the ordered set of LOs is called sequence, so the process is called LO sequencing. In this paper an intelligent agent that performs the LO sequencing process is presented. Metadata and competencies are used to define relations between LOs so that the sequencing problem can be characterized as a Constraint Satisfaction Problem (CSP) and artificial intelligent techniques can be used to solve it. A Particle Swarm Optimization (PSO) agent is proposed, built, tuned and tested. Results show that the agent succeeds in solving the problem and that it handles reasonably combinatorial explosion inherent to this kind of problems.