This paper investigates the problem of personalization in massive open online courses (MOOC) based on a target competency profile and a learning scenario model built for the course. To use such a profile for adaptive learning and resource recommendation, we need to be able to compare competencies to help match the competencies of learners with those involved in other learning scenario components (actors, activities, resources). We present a method for computing relations between competencies based on a structured competency model. We use this method to define recommendation agents added to a MOOC learning scenario. This approach for competency comparison has been implemented within an experimental platform called TELOS. We propose to integrate these functionalities to a MOOC platform such as Open-edX. We present a personalization process and we discuss the tools needed to implement the process.
Nous présentons un modèle générique d'assistance aux acteurs du téléapprentissage. Le modèle permet de décrire des systèmes d'assistance pour tout système informatisé distribué ou non, quel que soit le domaine de connaissances et quel que soit l'acteur du téléapprentissage (apprenant, concepteur, formateur-tuteur, etc.) qui utilise l'assistance. Le système d'assistance peut aider ces usagers en leur offrant des conseils et en adaptant l'interface en fonction de leurs caractéristiques propres ou de celles de leur groupe de travail, de leur progression dans la tâche, de leurs interactions et de l'historique de l'assistance déjà offerte. Le modèle générique identifie les diverses composantes d'un tel système d'assistance (mode d'accès à l'assistance, objets, buts et thèmes d'assistance, conditions et actions d'assistance, etc.). Un exemple de l'application de ce modèle générique est présenté pour l'assistance à l'acteur concepteur dans un campus virtuel, et plus spécifiquement aux usagers de l'atelier distribué d'ingénierie pédagogique ADISA.
We propose an organic approach to educational web-based systems where learning objects, operations on these objects, and actors that perform them are aggregated in meaningful ways. The users of a learning system must be able to observe it globally, at different levels and from diverse viewpoints. They must be able to propose adaptations and improvements constantly using means of observation integrated with the means of action. For this, we need to provide inspectable and executable models of the learning system, to be used as prisms for understanding and control of operations. We propose to reference these models with educational ontologies developed for instructional engineering. The implementation of some of these ideas in the Explor@-II system provides examples. Conversely, the next Explor@ implementation will benefit from the discussion presented here.
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