The design and evaluation of distributed virtual environment to support learning in global operations management

The primary goal of this research is to design and develop an education technology to support learning in global operations management. The research implements a series of studies to determine the right balance among user requirements, learning methods and applied technologies, on a view of student-centred learning. This research is multidisciplinary by nature, involving topics from various disciplines such as global operations management, curriculum and contemporary learning theory, and computer aided learning. Innovative learning models that emphasise on technological implementation are employed and discussed throughout this research.

Entrepreneurship and innovation through experience:student and employer perspectives of the value of work-based learning in promoting entrepreneurialism

The 'internationalisation' of Business and Management education, reflective of EU enlargement and the unprecedented globalisation of education, has resulted in growing numbers of overseas students adding a diversity and richness to the learning environment within many contemporary European Higher Educational Institutions (Green, 2006, Sliwa & Grandy, 2006). However, cross-national studies analyzing the impact that the internationalisation of business education has on the employability of business and management graduates are rare. Furthermore, there exists a notable gap in research aimed at identifying and conceptualising the generic business skills and competencies required by European employers of business and management graduates. By proposing a conceptual framework based upon a working model of business graduate employability, this goes some way to addressing this gap.

On-line learning in multilayer neural networks

We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.