In e-learning context, personalization of the learning characteristics according to user traits i... more In e-learning context, personalization of the learning characteristics according to user traits is a complex factor to treat, in particular, in delivering activities or learning resources adapted to the user. In this work is described how the e-learning platform dotLRN is using to implement two models that potentiate its funcionalities including the adaptation variables of learning styles and interaction. In this paper are presented: 1) an adaptation model to deliver learning resources according to a user learning style and 2) a model that defines the colaborative competences level and that allows bringing recommendations to students according to their interaction level with colaborative tasks. To design and to develop these models were used machine learning techniques, learning technologic standards to specify and to characterize important elements of the learning process, and also, intelligent agents for developing an independent multiagent platform. The models were implemented upon dotLRN and because of its open source feature we can present implementation details. The work implemented will contribute to ADAPTAPLan project (TIN2005-08945-C06-00).
In e-learning context, personalization of the learning characteristics according to user traits i... more In e-learning context, personalization of the learning characteristics according to user traits is a complex factor to treat, in particular, in delivering activities or learning resources adapted to the user. In this work is described how the e-learning platform dotLRN is using to implement two models that potentiate its funcionalities including the adaptation variables of learning styles and interaction. In this paper are presented: 1) an adaptation model to deliver learning resources according to a user learning style and 2) a model that defines the colaborative competences level and that allows bringing recommendations to students according to their interaction level with colaborative tasks. To design and to develop these models were used machine learning techniques, learning technologic standards to specify and to characterize important elements of the learning process, and also, intelligent agents for developing an independent multiagent platform. The models were implemented upon dotLRN and because of its open source feature we can present implementation details. The work implemented will contribute to ADAPTAPLan project (TIN2005-08945-C06-00).
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Papers by Sergio Gomez