Abstract Digital images have a rich potential as learning and teaching resources and arecurrently under-utilised in the support of pedagogical activities. The FILTER(Focusing Images for Learning and Teaching – an Enriched Resource) projectis addressing this under-use and encouraging uptake of visual resources bymapping different types of images and defining the ways in which those imagescan be used to enhance the learning and teaching process. These categorisationsform the basis of the main project deliverables: a generic image dataset, a set ofsubject-specific image datasets and supporting documentation such as “how-to”guides and case studies. This paper describes the aims, objectives and method-ologies of the FILTER project. The project is based at the Institute for Learningand Research Technology, the University of Bristol, and started in May 2001. Keywords images, digital images, learning, teaching, cross-disciplinary, collaboration Introduction It is known that a high level of expertise in image use already exists in certain subject areas, butpossibly using a limited range of image types. However, as Williams, Lock, Crisp and Longstaffe(1995) have noted, there is currently no extensive body of information exploring the use of imagesin computer-based learning and teaching.As more and more images become available over networks, it is crucial to take a collaborativeapproach to developing common standards for the creation and use of digital images (Hastings,1999). The FILTER project (Focusing Images for Learning and Teaching – an Enriched Resource)is working closely with a number of Learning and Teaching Support Network (LTSN) subjectcentres to investigate aspects of image use in learning and teaching. The LTSN is a network of 24subject centres based in higher education (HE) institutions throughout the UK. It aims to promotehigh quality learning and teaching through the development and transfer of good practices in allsubject disciplines. The LTSN subject centres have an established direct route of communicationand dissemination to practitioners, enabling FILTER to enter into dialogue with subject specialistsin order to inform research processes and outcomes directly. Each LTSN has varying levels of experience of image use; FILTER aims to enable a cross-fertilisationof these varying knowledge levels across subject disciplines, so that there are multiple learningopportunities for all involved. Transferral of expertise has a major role in the development of themain project deliverables: a generic image dataset and a set of subject-specific datasets containing:images, examples of image-based learning and teaching materials, supplementary thematic how-toguides and supporting metadata. This collaborative approach permits the identification and explorationof the factors of image use that are common to a number of subjects. Synthesis of this cross-domain~ 189 ~
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