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At the ACM Hypertext Conference a panel discussed "The Next Big Thing Inc." in the area of hypertext and hypermedia. The Web has been the "Big Thing" during the past 10 years, but its success has also made it very difficult to find the appropriate information in an ocean of over 3 billion pages. Whereas search engines achieve incredible precision, they suffer from the same "one size fits all" approach that characterizes the Web sites they index. The paper defends the position that personalization, and in particular automatic personalization or adaptation, is the key to reach the goal of offering each individual user (or user group) the information they need. During the panel discussion there was debate about whether the user should always have access and control over the entire (hypertextual) information space. There were different views on whether the "right" to all the information is best guaranteed by offering tools that reduce the information space the user perceives so that the user can actually find and reach the information, or by offering unfiltered access to an ocean of information in which everything is available but in which perhaps nothing can be found. We argue in favor of adaptation but at the same time point out flaws in the way adaptive hypermedia has been used until now. The paper then proposes a new, modular adaptive hypermedia architecture that should lead to adaptive Web-based systems as the "Next Big Thing" indeed.

In this paper it is presented our contribution for carrying out adaptive and intelligent Web-based Education Systems (WBES) that take into account the individual student learning requirements, by means of a holistic architecture and Framework for developing WBES. In addition, three basic modules of the proposed WBES are outlined: an Authoring tool, a Semantic Web-based Evaluation, and a Cognitive Maps-based Student Model. As well, it is stated a Service Oriented Architecture (SOA) oriented to deploy reusable, accessible, durable and interoperable services. The approach enhances the Learning Technology Standard Architecture, proposed by IEEE-LTSA (Learning Technology System Architecture) [IEEE 1484.1/D9 LTSA (2001). Draft standard for learning technology - learning technology systems architecture (LTSA). New York, USA. URL: http://ieee.ltsc.org/wg1], and the Sharable Content Object Reusable Model (SCORM), claimed by Advanced Distributed Learning (ADL) [Advanced Distributed Learning Initiative (2004). URL: http://www.adlnet.org].

Research interests in applied technologies and learning/teaching systems. Provides a rich collection of publications and conference presentations.

Recent developments in Web technologies and using AI techniques to support efforts in making the Web more intelligent and provide higher-level services to its users have opened the door to building the Semantic Web. That fact has a number of important implications for Web-based education, since Web-based education has become a very important branch of educational technology. Classroom independence and platform independence of Web-based education, availability of authoring tools for developing Web-based courseware, cheap and efficient storage and distribution of course materials, hyperlinks to suggested readings, digital libraries, and other sources of references relevant for the course are but a few of a number of clear advantages of Web-based education. However, there are several challenges in improving Web-based education, such as providing for more adaptivity and intelligence. Developments in the Semantic Web, while contributing to the solution to these problems, also raise new issues that must be considered if we are to progress. This paper surveys the basics of the Semantic Web and discusses its importance in future Web-based educational applications.

Welcome to the laboratory for Advanced Research in Intelligent Educational Systems (ARIES). The lab was founded in 1987 with the goal of deepening both research and practice in intelligent systems and how they apply to teaching and learning. Faculty, staff, and students within the laboratory contribute to numerous academic areas, such as user modeling, artificial intelligence, adaptive hypermedia, e-learning, and information visualization.

This paper presents an ontology-based framework aimed at explicitly representing the context of the use of a learning object inside of a learning design. The core of the proposed framework is a learning object context ontology that leverages a range of other kinds of learning ontologies (e.g. domain, user modeling, learning design etc.) to capture the context-specific metadata. On top of that framework, we develop the architecture of an adaptive educational system, in order to illustrate the benefits of our proposal for personalization of learning design. Finally, we reflect on how two present educational tools (iHelp Courses and TANGRAM) correspond to the proposed architecture.

ActiveMath is a complex web-based adaptive learning environment with a number of components and interactive learning tools. The basis for handling semantics of learning content is provided by its semantic (mathematics) content markup, which is additionally annotated with educational metadata.

Select ARIES Publications Welcome to the laboratory for Advanced Research in Intelligent Educational Systems (ARIES). The lab was founded in 1987 with the goal of deepening both research and practice in intelligent systems and how they apply to teaching and learning. Faculty, staff, and students within the laboratory contribute to numerous academic areas, such as user modeling, artificial intelligence, adaptive hypermedia, e-learning, and information visualization. The laboratory is headed by Dr. Gordon McCalla, and is funded in part by the the TeleLearning Network Centres of Excellence (1995-1999), and the LORNET NSERC Research Network (2003-Present). ARIES is one of the many laboratories within the Department of Computer Science at the University of Saskatchewan.

Motivation Educational Systems are gradually incorporating Semantic Web technology. The general purpose is to make the Web more understandable by machines and then generate educational systems that are more adaptable and intelligent.