A b s t r a c t :The semantic web movement has grown around the need to add semantics to the we b in order to make it more usable by people and by information systems. In this paper I argue that even more important than semantics is pragmatics; that is, to re a l l y enhance web usability it is critical to capture and react to aspects of the end use context. Most centrally, to make the web truly re s p o n s i ve to human needs, we need to understand the "u s e r s" of the web and their purposes for using it. In this paper I elaborate this argument in the context of of e-learning systems. I propose an a p p roach to the design of e-learning systems that I call the ecological appro a c h.Moving from the open web to repositories of learning objects, I show how the ecological approach shows promise not only to allow information about learners actual interactions with learning objects to be naturally captured but also to allow it to be used in a multitude of ways to support learners and teachers in achieving their goals. In a phrase, the approach invo l ves attaching models of learners to the learning objects they interact with, and then mining these models for patterns that are useful for various purposes. The ecological approach turns out to be highly suited to elearning applications. It also has interesting implications for e-learning re s e a rc h , and perhaps even for re s e a rch directions for semantic web re s e a rc h .

Abstract. Social capital has recently emerged as an important interdisciplinary research area. It is frequently used as a framework for understanding various social issues in temporal communities, neighbourhoods and groups. In particular, researchers in the social sciences and the humanities have used social capital to understand trust, shared understanding, reciprocal relationships, social network structures, common norms and cooperation, and the roles these entities play in various aspects of temporal communities. Despite proliferation of research in this area, little work has been done to extend this effort to technology-driven learning communities (also known as virtual learning communities). This paper surveys key interdisciplinary research areas in social capital. It also explores how the notions of social capital and trust can be extended to virtual communities, including virtual learning communities and distributed communities of practice. Research issues surrounding social capital and trust as they relate to technology-driven learning communities are identified.

Abstract. Universities, experiencing growths in student enrollment and reductions in operating budgets, are faced with the problem of providing adequate help resources for students. Help resources are needed at an institution-wide and also at a course-specific level, due to the limited time of instructors to provide help and answer questions. The Intelligent IntraNet Peer Help Desk provides an integration and application of previously developed ARIES Lab tools for peer help to university teaching. One of its components, CPR, provides a subject-oriented discussion forum and FAQ -list providing students with electronic help. Another component, PHelpS, suggests an appropriate peer to provide human help. In both cases it is peer help, since the help originates from students themselves. The selection of the appropriate help resource (electronic or human) is based on modelling student knowledge and on a conceptual model of the subject material.

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This paper outlines the research we are doing in acquiring, describing and using learning object metadata. Instead of the IEEE LOM and other standardised metadata schemes, we argue for a more flexible approach to both defining and associating metadata with learning objects. This approach, which we call the ecological approach, sees metadata as the process of reasoning over observed interactions of users with a learning object for a particular purpose. Central to this approach is the notion that Semantic Web enabled computational agents will both provide and consume pieces of actual usage data that have been collected about a learning object in determining the usefulness of this learning object for some new purpose. This is then an evolutionary approach to metadata creation as compared to move traditional prescriptive 'one size fits all' approaches.