AN ENSEMBLE OF CLASSIFIERS APPROACH TO USER MODELING ON ADAPTIVE LEARNING COMMUNITIES
Elena Gaudioso, Jesús G. Boticario · International Journal of Artificial Intelligence Tools · 2004
Nowadays, web-based distance learning is benefiting from improved communication services. However, the mere fact of setting up an environment for students and lecturers does not guarantee mutual collaboration or successful student learning. This is partly due to the fact that an unique response is given to every user with different background knowledge, different interests, or different skill levels in the use of the services provided. To resolve this situation, adaptive systems provide an adapted response to each user's needs based on a constructed user model containing his/her characteristics. In this paper we will see that the construction of this user model is not trivial. In a user model in a web-based collaborative environment, there are very diverse attributes related with the interaction of the user with the services and the value of these attributes are obtained in very different ways. In addition, as any adaptive system the user model constructed should be explicit and accesible by the user and tutor. We will also see how we dynamically manage the user model in an adaptive learning environment by means of a ensemble of classifiers.