Evolutionary approaches for curriculum sequencing
Proceedings of the 13th annual conference on Innovation and technology in computer science education
An evolutionary approach for competency-based curriculum sequencing
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Competency-Based Intelligent Curriculum Sequencing: Comparing Two Evolutionary Approaches
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
PC2PSO: personalized e-course composition based on Particle Swarm Optimization
Applied Intelligence
Evolutionary computation approaches to the Curriculum Sequencing problem
Natural Computing: an international journal
A self-adjusting e-course generation process for personalized learning
Expert Systems with Applications: An International Journal
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In e-learning initiatives, sequencing problem concerns arranging a particular set of learning units in a suitable succession for a particular learner. Sequencing is usually performed by instructors, who create general and ordered series rather than learner personalized sequences. This paper proposes an innovative intelligent technique for learning object automated sequencing using particle swarms. E-learning standards are promoted in order to ensure interoperability. Competencies are used to define relations between learning objects within a sequence, so that the sequencing problem turns into a permutation problem and AI techniques can be used to solve it. Particle Swarm Optimization (PSO) is one of such techniques and it has proven with good performance solving a wide variety of problems. An implementation of the PSO, for learning object sequencing, is presented and its performance in a real scenario is discussed.