Short-Term Content Adaptation in Web-Based Learning Systems
Michela Acquaviva, Marco Benini and Alberto Trombetta
In: The IASTED International Conference on Web Technologies, Applications and Services - WTAS 2005, Calgary, Canada(2005).

Abstract

In recent years, a concentration of effort to design adaptive web application has arisen: generally, each user has different information needs, depending on her/his social role, culture, etc. Especially in the field of web-based learning, it has become progressively clearer that these needs do not only depend on a long-term characterisation of the user, but also on the contingent situation the user lives. In this paper, a model for short-term content adaptation is proposed, whose aim is to satisfy contingent needs of users by adjusting the information a web-based learning system provides on the basis of a short-term user profile. The model results in the design of an adaptive filter that acts in the interface between the data and the logic layer of the system. The designed filter is provably correct in the model, that is, it is guaranteed to exhibit a coherent shortterm adaptive behaviour.

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