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Semantic Technologies for the Enhancement of Case Based Learning

Case Based Learning

date Date: Apr 23rd, 2008

Case based learning is the pedagogy of choice when knowledge domains are complex, unpredictable, politically or ethically contentious, or so rapidly changing and fluid that a curriculum defined in terms of knowledge or competences alone is inadequate as the basis of developing expertise.

Engagement in case based learning and the reflective processes that accompany it allows learners to achieve the higher levels of understanding and capability that characterise the ‘expert’ or the ‘virtuoso’, especially in domains where dealing with complexity is seen as indicative of this expert performance. As such, it is a desirable learning outcome for advanced undergraduate and postgraduate courses and programmes of professional learning.

The project will engage with groups of teachers and learners in two distinctive higher education sites (City University and the University of Cambridge) and disciplinary settings (three in each site, representing advanced undergraduate, M-level postgraduate and professional development courses). In all of these, learning from and with cases is an integral and assessed element. The research will address similarities and differences in the conceptualisation, construction and application of cases and the role of case building as an individual and group activity, and ways in which semantic web technologies support, enhance and transform these learning activities through the provision of robust, easy-to-use and flexible software tools and interfaces.

Teachers and learners will take part in ‘case-building’ activities in which semantic web tools and digital repositories are used to support engagement with rich case data, differently structured and represented and in which alternative constructions of cases are possible. This develops ideas originally developed by Lawrence Stenhouse in which the role of appropriately structured and mediated data sources provide a basis for multiple and cross-case analyses and application in a wide range of teaching, learning and engagement activities. The learning outcomes of these activities will be complex and discipline-specific, but they will be theorised both in terms of individual learners’ outcomes and their contribution to the shared resources of academic and professional knowledge-constructing communities.

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