A Learning Object Approach to Personalized Web-based Instruction
نویسندگان
چکیده
The concept of web-based learning and the use of the Internet in teaching and learning have received increasing attention over the recent years. It is postulated that one of the main problems with e-learning environments is their lack of personalisation (Cristea, 2003; Rumetshofer & Wöß, 2003; Ayersman & Minden, 1995). The concept of extending the learning object metadata to cater for psychological factors has been proposed by Rumetshofer & Wöß (2003). In this article, the latters’ approach has been extended in three ways: (1) More factors relating to individual differences are included in the metadata extension as well as fields related to the pedagogical value and level of difficulty of the learning content, (2) The introduction of fuzzy (or belief) values for each aspect that is modelled, (3) A mechanism is provided to adapt to changing attitudes, characteristics and performance of a student both in the student model and in the learning object attributes model. A method is devised to select the most appropriate learning object from a pool of potential objects that exist in the repository and a first evaluation of the proposed algorithm is carried out and reported in the article. Le concept du e-learning et l'apprentissage par Internet est devenu un domaine très étudié dans le milieu de la recherche en éducation et les technologies éducatives. Cependant une des contraintes de cette nouvelle approche demeure le manque de personnalisation dans ces environnements (Cristea, 2003; Rumetshofer & Wöß, 2003; Ayersman & Minden, 1995). Rumetshofer & Wöß (2003) proposent l'extension des meta-données des objets d’apprentissage (learning objets) afin de prendre en considération les attributs psychologiques des apprenants. Cet article décrit une extension de cette approche en trois étapes : (1) L'extension des meta-données contient plus de facteurs ayant trait aux différences individuelles ainsi que des champs sur la valeur pédagogique et niveau de difficulté de l'objet d’apprentissage, (2) L'introduction des valeurs approximatives (fuzzy) pour les facteurs qui sont considérés, (3) une stratégie d'adaptation aux attitudes variantes et la performance de l'apprenant. La méthode de sélection des objets d’apprentissage qui sont plus appropriés pour un apprenant spécifique est présentée. Finalement, on décrit une première évaluation de l'algorithme proposée pour la méthode
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