 AEFishBIT provides simultaneous measures of fish respiration and locomotion.  Device measures highlight species differences in anatomical and locomotor features.  Coupling of activity and respiration rhythms emerges as fish performance indicator.

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Abstract. In this paper we present an extension of a previously developed generic student model based on Bayesian Networks. A new layer has been added to the model to include prerequisite relationships. The need of this new layer is motivated from different points of view: in practice, this kind of relationships are very common in any educational setting, but also their use allows for improving efficiency of both adaptation mechanisms and the inference process. The new prerequisite layer has been evaluated using two different experiments: the first experiment uses a small toy example to show how the BN can emulate human reasoning in this context, while the second experiment with simulated students suggests that prerequisite relationships can improve the efficiency of the diagnosis process by allowing increased accuracy or reductions in the test length.

Abstract. Most of the information of the WWW is not adaptive, rather it is dispersed and disorganized. Another difficulty is to find tools that help to create adaptive courses. SIGUE is an author tool that makes it possible to build adaptive courses using web pages that already exist. This means that if there is a lot of information on the web about the same topic the author doesn't have to design the content of a specific course, he can reuse these pages to build his own course, taking the best pages for the concepts he wants to explain. The author can also construct adaptive courses reusing previously non-adaptive ones. SIGUE provides an enhanced interface for the student, controls his interaction, and annotates the visited links in a student model.

A learner model must store all the relevant information about a student, including knowledge and attitude. This paper proposes a domain independent learner model based in the classical overlay approach that can be used in a distributed environment. The model has two sub-models: the learner attitude model, where the static information about the user is stored (user's personal and technical characteristics, user's preferences, etc.) and the learner knowledge model, where the user's knowledge and performance is stored. The knowledge model has four layers: estimated, assessed, inferred by prerequisite and inferred by granularity. The learner model is used as a part of the MEDEA system, so the first and second layers are updated directly by the components of MEDEA and the third and fourth are updated by Bayesian inference.