PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Towards Short Term Content Adaptation
Michela Acquaviva and Marco Benini
Electronic Workshops in Computing 2005. ISSN 1477-9358


Recent works in the E-Learning field tend to focus on the learner, leaving the representation and treatment of contents in the background. One of the principal actors that contribute on this focus shift is the adoption of adaptive techniques that profile learners and adjust the contents according to the inferred profiles. In contrast with most adaptive approaches, this work introduces a short-term adaptive strategy whose aim is to capture the instantaneous interests of users. The proposed model fills the temporal gap that other adaptive strategies leave open. In fact, as far as we know, all proposed adaptive strategies have been conceived to deduce complex and accurate profiles in a long amount of time. Instead, the proposed strategy operates observing few actions to deduce a rough profile that is useful to provide continuos adaptive behaviour even if a more precise profile has not yet being constructed.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:3083
Deposited By:Marco Benini
Deposited On:15 December 2007