Towards short term content adaptation

Abstract 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

Towards Short-Term Content Adaptation

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