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.

Studying language:English in action

This book introduces key ideas and current critical debates about how English functions within its social and cultural contexts, and provides practical examples and guidance on how to approach further work in these areas. It introduces core topics of language study; language variation, pragmatics, stylistics, critical discourse analysis, language and gender and language and education. Each chapter includes case studies providing worked analysis of sample texts, suggestions for further project work and an annotated further reading section.

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.

Intergroup relations in action: questions asked about lesbian, gay and bisexual issues in diversity training

This paper focuses on the questions which heterosexual trainees ask about lesbian, gay and bisexual (LGB) experience within diversity training about LGB issues. Drawing on a data corpus of 162 questions asked by trainees in 13 tape-recorded training sessions, questions were coded into six categories: (1) general understanding questions; (2) questions about the trainer's life, experience and practices; (3) professional practice questions; (4) questions about lesbian and gay related legislation, policies and procedures; (5) questions about specific people and projects and (6) questions about the meanings, derivations and correct use of terms and symbols. Real questions are compared with the decontexualized questions (and answers to them) that are provided in training manuals and it is demonstrated that these questions differ markedly from how questions actually get asked and how they actually get answered. Recommendations are provided for improving training and the argument made for turning towards analyses of the real world in action, especially when considering intergroup relations. Copyright © 2008 John Wiley & Sons, Ltd.

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.

Globally optimal parameters for on-line learning in multilayer neural networks

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.

Dynamics of on-line learning in radial basis function networks

On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations.

On-line learning in radial basis functions networks

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives insight into decreasing the time required for training. The realizable and over-realizable cases are studied in detail; the phase of learning in which the hidden units are unspecialized (symmetric phase) and the phase in which asymptotic convergence occurs are analyzed, and their typical properties found. Finally, simulations are performed which strongly confirm the analytic results.

On-line learning in neural networks

On-line learning is one of the most powerful and commonly used techniques for training large layered networks and has been used successfully in many real-world applications. Traditional analytical methods have been recently complemented by ones from statistical physics and Bayesian statistics. This powerful combination of analytical methods provides more insight and deeper understanding of existing algorithms and leads to novel and principled proposals for their improvement. This book presents a coherent picture of the state-of-the-art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable non-experts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, whether in industry or academia.