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Abstract

In this paper we report on the development and use of CoFIND (Collaborative Filter In N Dimensions), a web-based collaborative bookmark engine, designed as part of a self-organising learning environment to generate a list of useful and relevant learning resources. Users of CoFfND can add pointers to resources and rate them according to two types of category, 'topics' and 'qualities'. Along with the links and descriptions of the resources themselves, both topics and qualities are also entered by users, thus generating a resource-base and collective categorisation scheme based on the needs and wishes of its participants. A topic is analogous to a traditional category whereby any object can be considered to be in the set or out of it. Examples of topics might include 'animals', 'computing', 'travel' and so on. Qualities, on the other hand are the things that users value in a resource, and most of them are (in English at any rate) adjectives or adjectival descriptive phrases. It is always possible to say of a quality that a given resource is more or less so. Examples of qualities might include 'good for beginners', 'amusing', 'colourful', 'turgid' and so on. It is the qualities that provide the nth dimension of CoFIND, allowing much subtler ratings than typical collaborative filtering systems, which tend to rate resources according to a simple good/bad or useful/useless scale. CoFIND thus dynamically accommodates changing needs in learners, essential because the essence of learning is change. In use, the user enters a number of qualities and/or topics that interest them. Resources are returned in a list ordered according to the closeness of match to the required topics and qualities, weighted by the number of users who have categorised or rated a particular resource. The more a topic or quality is used to categorise different resources, the more prominent its position in the list of selectable topics or categories. Not only do less popular qualities sink to the bottom of this list, they can also fall off it altogether, in a process analogous to a Darwinian concept of evolution, where species of quality or topic fight each other for votes and space on the list and topics and qualities are honed so that only the most useful survive. The system is designed to teeter on the 'edge of chaos', thus allowing clear species to develop without falling into chaotic disorder or stagnant order. The paper reports on some ongoing experiments using the CoFIND system to support a number of learning environments within the University of Brighton. In particular, we report on a cut-down form used to help teach a course on Human-Computer Interaction, whereby students not only rate screen designs but collaboratively create the qualities used to rate those resources. Mention is made of plans to use the system to establish metadata schema for courseware component design, a picture database and to help facilitate small group research. The paper concludes by analysing early results, indicating that the approach provides a promising way to automatically elicit consensus on issues of categorisation and rating, allowing evolution instead of the 'experts' to decide classification criteria. However, several problems need to be overcome, including difficulties encouraging use of the system (especially when the resource base is not highly populated) and problems tuning the rate of evolution in order to maintain a balance between stability and disorder