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
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
Computer
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Identifying patterns in learner's behavior Using Markov chains and n-gram models
CSCC'11 Proceedings of the 2nd international conference on Circuits, Systems, Communications & Computers
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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.