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Improving access to learning in the workplace using technology in an accredited course

By Kathleen Munro and Susi Peacock

Abstract

This article gives an account of a case study which seeks to explore the potential for using technology to deliver learning in the workplace: a syringe driver course for nurses. We provide a brief overview of workplace learning, continuing professional development and learning technology in the health sciences. The paper then draws upon a three-year project that involved the transition of a traditionally taught, institution-based face-to-face course to work-based learning using technology. Through the evaluation and discussion of the case study we address key issues that have emerged, such as, marketing of the product; in our case it was decided that the most cost-effective way to provide the course and recuperate some costs was to accredit the course by the Institution. Registered practitioners in the workplace assess learning and are linked to the quality assurance mechanisms of the Institution. We also consider some of the major barriers to implementation, highlighting critical areas for consideration for those undertaking a similar project. These include the lack of technical knowledge in the Group, which resulted in a steep learning curve for all members. This and numerous iterations of materials (including video and animations) lengthened the project considerably whilst technological advances meant other more sophisticated technological solutions that became available during the production process were incorporated. A cost benefit analysis would show that the product has been delivered across Scotland and production costs covered and that there have been unquantifiable gains, including improving the external profile of the academic institution and the NHS Trust, developing the technical skills of the Group and providing invaluable experience of working in a cross-disciplinary collaborative working environment

Topics: LC
Publisher: Elsevier Science B.V. Amsterdam
Year: 2005
OAI identifier: oai:eresearch.qmu.ac.uk:347
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