Using Emotional Intelligence in Personalized Adaptation

The process of training and learning in Web-based and ubiquitous environments brings a new sense of adaptation. With the development of more sophisticated environments, the need for them to take into account the user’s traits, as well as the user’s devices on which the training is executed, has become an important issue in the domain of building novel training and learning environments. This chapter introduces an approach to the realization of personalized adaptation. According to the fact that we are dealing with the stereotypes of e-learners, having in mind emotional intelligence concepts to help in adaptation to the e-learners real needs and known preferences, we have called this system eQ. It stands for the using of the emotional intelligence concepts on the Web.

From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior

Humans need assistance in learning. This is particularly true when learning is supported by modern information and communication technologies. Most current IT systems appear as more or less complex tools. The more ambitious the problems in the application domain are, the more complex are the tools. This is one of the key obstacles to a wider acceptance of technology enhanced learning approaches (e-learning, for short). In computer science, in general, and in e-learning, in particular, we do need a paradigmatic shift from tools of a growing complexity to intelligent assistants to the human user. Computerized assistants that are able to adapt to their human users’ needs and desires need some ability to learn. In e-learning, in particular, they need to learn about the learner and to build an internal model of the learner as a basis of adaptive system behavior. Steps toward assistance in e-learning are systematically illustrated by means of the authors’ e-learning projects and systems eBuT and DaMiT. These steps are summarized in some process model proposed to the e-learning community.

Adaptive Presentation and Scheduling of Media Streams on Parallel Storage Servers

One way to implement adaptive software is to allocate resources dynamically during run-time rather than statically at design time. Design of adaptive software and adaptive execution of processes are key factors that improve versatility of software and decrease maintenance costs. In this chapter we study the development of adaptive software focusing on a design strategy for the implementation of parallel media servers with an adaptable behaviour. This strategy makes the timing properties and the quality of presentation of a set of media streams predictable. The proposed adaptive scheduling approach exploits the performance of parallel environments and seems a promising method that brings the advantages of parallel computation in media servers. The proposed mechanism provides deterministic service for both Constant Bit Rate (CBR) and Variable Bit Rate (VBR) streams. We present an efficient placement strategy for data frames as well as an adaptability strategy that allows appropriate frames to be dropped without sacrificing the ability to present multimedia applications predictably in time.

Intelligent Information Personalization

This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based on an individual’s interests, needs and goals. We present intelligent techniques to dynamically compose new personalized information by adapting existing web-based information in line with a dynamic user-model, whilst simultaneously addressing linguistic, factual and functional requirements. This chapter will highlight the different facets, tasks and issues concerning intelligent information personalization to guide researchers in designing intelligent information personalization applications. The chapter presents intelligent methods that address information personalization at the content level as opposed to the traditional approaches that focus on interface level information personalization. To assist researchers in designing intelligent information personalization applications we present our information personalization framework, named AdWISE (Adaptive Webmediated Information and Services Environment), to demonstrate how to systematically integrate various intelligent methods to achieve information personalization. We will conclude with a commentary on the future outlook for intelligent information personalization.

The Next Generation of Personalization Techniques

Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization approaches model user preferences and store them in user profiles, used to deliver personalized content. A traditional method for profile representation is the so called keyword-based representation, where the user interests are modelled using keywords which are selected from the contents of the items which the user has rated. Although, keyword based approaches are simple and are extensively used for profile representation they fail to represent semantic-based information, this information is lost during the pre-processing phase. Future trends in personalization systems necessitate more innovative personalization techniques that are able to capture rich semanticbased information during the representation, modelling and learning phases. In recent years ontologies (key concepts and along with their interrelationships) to express semantic-based information have been very popular in domain knowledge representation. The primary goal of this chapter is to present an overview of the state-of-the art techniques and methodologies which aim to integrate personalization technologies with semantic-based information.

Assessment of Human Factors in Adaptive Hypermedia Environments

The plethora of information and services as well as the complicated nature of most Web structures intensify the navigational difficulties that arise when users navigate their way through this large information space. Personalized services that are highly sensitive to the immediate environment and the goals of the user can alleviate the orientation and presentation difficulties experienced by the relatively diverse user population. User profiles serves as the main component of most Web personalization systems. Main scope of this chapter is to present the various techniques employed by such systems with regards to user profiles extraction and introduce a comprehensive user profile, which includes User Perceptual Preference Characteristics. It further analyzes the main intrinsic users’ characteristics like visual, cognitive, and emotional processing parameters incorporated as well as the “traditional” user profile characteristics that together tend to give the most optimized personalization outcome. It finally overviews a Web adaptation and personalization system and presents evaluation results that further support the importance of human factors in the information space.

Context and Adaptivity-Driven Visualization Method Selection

Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and advanced functionalities. Besides, research in the field of user modeling is progressing in order to personalize these visualization systems, according to its users’ individual profiles. However, employing a single visualization system, may not suit best any information seeking activity. In this paper we present a visualization environment, which is based on a visualization library, i.e. is a set of visualization methods, from which the most appropriate one is selected for presenting information to the user. This selection is performed combining information extracted from the context of the user, the system configuration and the data collection. A set of rules inputs such information and assigns a score to all candidate visualization methods. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria.

A Semantically Adaptive Interface for Measuring Portal Quality in E-Government

This chapter introduces a semantically adaptive interface as a means of measuring the quality of egovernment portals, based on user feedback. The interface is semantic as it uses ontologies in order to formalize well defined semantics about the adaptation criteria used. Furthermore it is adaptive as three axes of adaptation are applied: based on real-time feedback from users, based on problems encountered by the user and based on metadata of the pages visited by the user. The authors hope that applying the proposed adaptive interface as a means of measuring e-government portals’ quality, will not only allow more focused and targeted assessment of quality, but will also increase users’ response rates.

Building Digital Memories for Augmented Cognition and Situated Support

Limitation of the human memory is a well-known issue that anybody has experienced. This chapter discusses typical components and processes involved in the building and the exploitation of augmented memories. SPECTER, an adaptive, self-learning system supports the user in everyday activities by interpreting sensor information captured in the environment and deriving adequate suggestions for actions to be taken in the current situation. A particular form of introspection allows the user to reflect on the digital memory’s contents and the system behavior, thus leaving the user in control. An empirical study in a shopping scenario evaluates the benefits and limitations of the approach taken

Impact of Cognitive Style on User Perception of Dynamic Video Content

This study investigated two dimensions of cognitive style, including Verbalizer/Imager and Field Dependent/ Field Independent and their influence on user perceived quality of multimedia video. Perceived user quality was characterised using the Quality of Perception (QoP) metric, which captures the infotainment duality of multimedia presentations. Results indicate that, generally, clip dynamism impacts on user QoP; in particular, it is worthwhile to remark that in the clips with strong and medium dynamism, Field Dependent users performed worse than the other two groups, while Field Dependent users had a (slightly) better performance than Field Independent users in clips with weak dynamism.