Integrating Cognitive and Emotional Parameters into Designing Adaptive Hypermedia Environments Zacharias Lekkas, Nikos Tsianos, Panagiotis Germanakos, Kostas Mourlas Faculty of Communication and Media Studies, National & Kapodistrian University of Athens, 5 Stadiou Str, GR 105-62, Athens, Hellas ntsianos@media.uoa.gr, pgerman@media.uoa.gr, mourlas@media.uoa.gr Abstract This paper introduces a “new” user profiling model in the field of adaptive hypermedia, which integrates cognitive and emotional parameters, in particular (but not exclusively) when information perception and processing are involved in a Web- based learning environment. The proposed model combines theories from the field of cognitive psychology, applying them on Web-based interactions, in order to improve learning performance and, most importantly, to personalize Web- content to users’ needs and preferences, eradicating known difficulties that occur in a “one size fits all” approach. The specific article emphasizes on the emotional aspect of our model, since it presents results of our efforts to measure and include Emotional Control parameters, by re-constructing a theory that addresses emotion and is feasible in Web- learning environments. Introduction One of the main challenges in Adaptive Hypermedia research is alleviating users’ orientation difficulties, as well as making appropriate selection of knowledge resources, since the vastness of the hyperspace has made information retrieval a rather complicated task (De Bra, Aroyo, Chepegin, 2004). Adaptivity is a particular functionality that distinguishes between interactions of different users within the information space (Eklund, & Sinclair, 2000; Brusilovsky & Nejdl,, 2004). Adaptive Hypermedia Systems employ adaptivity by manipulating the link structure or by altering the presentation of information, on the basis of a dynamic understanding of the individual user, represented in an explicit user model (Brusilovsky, 2001; 1996). A system can be classified as an Adaptive Hypermedia System if it is based on hypermedia, has an explicit user model representing certain characteristics of the user, has a domain model which is a set of relationships between knowledge elements in the information space, and is capable of modifying some visible or functional parts of the system, based on the information maintained in the user model (Brusilovsky, 2001; 1996; Brusilovsky & Nejdl, 2004). In further support of the aforementioned concept of adaptivity, when referring to information retrieval and The project is co-funded by the European Social Fund and National Resources (EPEAEK II) PYTHAGORAS processing, one cannot disregard the top-down individual cognitive processes (Eysenck & Keane, 2005), that significantly affect users’ interactions within the hyperspace, especially when such interactions involve educational or learning, in general, goals. Consequently, besides “traditional” demographic characteristics that commonly comprise the user model in hypermedia environments, we believe that a user model that incorporates individual cognitive characteristics and triggers corresponding mechanisms of adaptivity, increases the effectiveness of Web- applications that involve learning processes. The goal of our research in general is to integrate individual cognitive and emotional characteristics as main parameters in an adaptive system we have already developed. Our system focuses on educational purposes, and its personalization mechanism relies on mapping the provided content on each user’s preferences and inclinations. This paper focuses on emotional factors that we hypothesize to be proven significant in defining usability and aesthetics aspects, taking into consideration psychometric challenges, as well as the complicated matter of quantifying and subsequently mapping emotions on a hypermedia environment. At a first level, we have experimented with two variables that we expect to correlate with each other, anxiety and Emotional Control. Our main hypothesis is that the moderating role of Emotional Control reduces the negative effect of high levels of anxiety, and should be taken into account in an adaptive e-learning process. User Perceptual Preference Characteristics This is the new component / dimension of the user profiling defined above. It contains visual attention and cognitive processes (including emotional parameters) that could be described as user “perceptual preferences”, aiming to enhance information learning efficacy. User Perceptual Preferences could be described as a continuous mental process, which starts with the perception of an object in the user’s attentional visual field, and involves a number of cognitive, learning and emotional processes that lead to the actual response to that stimulus. This model’s primary parameters formulate a three- dimensional approach to the problem. The first dimension investigates the visual and cognitive processing of the user,