A Learning Object Approach to Personalized Web-based Instruction
Mohammad Issack Santally [m.santally@uom.ac.mu],
Alain Senteni,
Virtual Center for Innovative Learning Technologies
University of Mauritius, Reduit, Mauritius
English Abstract
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.
French Abstract
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.
Keywords
e-Learning, Personalisation, Adaptation, Web-based Learning, Learning Objects, Learning & Cognitive Styles.
Introduction
The concept of web-based learning and the use of the Internet in teaching and learning have received increasing attention over the recent years. One of the main advantages of delivering web-based educational materials is that the same content is delivered to a number of students and can be accessed with no restrictions of time and place. However, there is a wide belief that using the web as only a new kind of delivery medium for educational materials does not add significant value to the teaching and learning process. The integration of technology in learning, needs to address the very important issue of enhancing the teaching and learning process, rather than just being seen as a new flexible delivery medium (Nichols, 2003). 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).
This article focuses on the personalisation issue in web-based learning environments. A brief overview of the concepts of adaptation is presented and a literature review of different related work that has been carried out in the field is made. The paper also discusses on the different factors that are necessary to take into account in attempts to provide personalisation in web-based learning. A method is devised to select the most appropriate learning object from a pool of potential objects that exist in the repository. To achieve this purpose, the learning object metadata extended to contain attributes pertinent for the personalisation process to individual differences. A first evaluation of the proposed algorithm is carried out and reported in the article.
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