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
Swarm-based Sequencing Recommendations in E-learning
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Competency-Based Learning Object Sequencing Using Particle Swarms
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Evolutionary computation approaches to the Curriculum Sequencing problem
Natural Computing: an international journal
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The process of creating e-learning contents using reusable learning objects (LOs) can be broken down in two sub-processes: LOs finding and LO sequencing. Although semiautomatic tools that aid in the finding process exits, sequencing is usually performed by instructors, who create courses targeting generic profiles rather than personalized materials. This paper proposes an evolutionary approach to automate this latter problem while, simultaneously, encourages reusability and interoperability by promoting standards employment. A model that enables automated curriculum sequencing is proposed. By means of interoperable competency records and LO metadata, the sequencing problem is turn into a constraint satisfaction problem. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) agents are designed, built and tested in real and simulated scenarios. Results show both approaches succeed in all test cases, and that they handle reasonably computational complexity inherent to this problem, but PSO approach outperforms GA.