The approach that we propose in this paper is part of the optimization of the collaborative learning in the e-learning environment. It is particularly interested in the automatic creation of groups called homogeneous ...
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ISBN:
(纸本)9781509007790
The approach that we propose in this paper is part of the optimization of the collaborative learning in the e-learning environment. It is particularly interested in the automatic creation of groups called homogeneous team., based on a set of criteria to highlight the similarity between the learners. This similarity is calculated using a similarity index that is the Euclidean distance. Then an optimal path guiding each community is built, through a dynamic adaptation of the optimization technique by ant colony. In this article, we have implemented the methodological framework and the algorithmic modeling of our approach, given that our future work will focus on the implementation and the empirical validation of our design.
This article aims to present two intelligent agents, APL planner agent and ARL regulatory agent, enabling distance learners to plan and dynamically regulate their personalized learning time .The planning considers the...
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This article aims to present two intelligent agents, APL planner agent and ARL regulatory agent, enabling distance learners to plan and dynamically regulate their personalized learning time .The planning considers the speed of learner's understanding, the teaching units expected time of learning, the constraint learning path, the pace and the periodic learning time of the learner, while the regulation takes into account the timetable's perturbations during the learning process. To achieve our goal we have based our work on modeling diagrams, pedagogical graph and the learner's properties. In this paper we focus on the work modeling of APL and ARL, while remaining to validate in a more elaborate version.
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