Good practice guide in learning and teaching

Once again this publication is produced to celebrate and promote good teaching and learning support and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. We have again changed our approach for this 2006/07 edition (our fourth) of the Aston Business School Good Practice Guide. As before, some contributions were selected from those identifying interesting best practice on their Annual Module reflection forms in 2005/2006. Other contributors received HELM (Research Centre in Higher Education Learning and Management) small research grants in 2005/2006. Part of the conditions were for them to write an article for this publication. We have also been less tight on the length of the articles this year. Some contributions are, therefore, on the way to being journal articles. HELM will be working with these authors to help develop these for publication. The themes covered in this year?s articles are all central to the issues faced by those providing HE teaching and learning opportunities in the 21st Century. Specifically this is providing support and feedback to students in large classes, embracing new uses of technology to encourage active learning and addressing cultural issues in a diverse student population. Michael Grojean and Yves Guillaume used Blackboard™ to give a more interactive learning experience and improve feedback to students. It would be easy for other staff to adopt this approach. Patrick Tissington and Qin Zhou (HELM small research grant holders) were keen to improve the efficiency of student support, as does Roger McDermott. Celine Chew shares her action learning project, completed as part of the Aston University PG Certificate in Teaching and Learning. Her use of Blackboard™ puts emphasis on the learner having to do something to help them meet the learning outcomes. This is what learning should be like, but many of our students seem used to a more passive learning experience, so much needs to be done on changing expectations and cultures about learning. Regina Herzfeldt also looks at cultures. She was awarded a HELM small research grant and carried out some significant new research on cultural diversity in ABS and what it means for developing teaching methods. Her results fit in with what many of us are experiencing in practice. Gina leaves us with some challenges for the future. Her paper certainly needs to be published. This volume finishes with Stuart Cooper and Matt Davies reflecting on how to keep students busy in lectures and Pavel Albores working with students on podcasting. Pavel?s work, which was the result of another HELM small research grant, will also be prepared for publication as a journal article. The students learnt more from this work that any formal lecture and Pavel will be using the approach again this year. Some staff have been awarded HELM small research grants in 2006/07 and these will be published in the next Good Practice Guide. In the second volume we mentioned the launch of the School?s Research Centre in Higher Education Learning and Management (HELM). Since then HELM has stimulated a lot of activity across the School (and University) particularly linking research and teaching. A list of the HELM seminars for 2006/2007 is listed as Appendix 1 of this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk), who coordinates the HELM seminars. For 2006 and 2005 HELM listed, 20 refereed journal articles, 7 book chapters, 1 published conference papers, 20 conference presentations, two official reports, nine working papers and £71,535 of grant money produced in this research area across the School. I hope that this shows that reflection on learning is alive and well in ABS. We have also been working on a list of target journals to guide ABS staff who wish to publish in this area. These are included as Appendix 2 of this publication. May I thank the contributors for taking time out of their busy schedules to write the articles and to Julie Green, the Quality Manager, for putting the varying diverse approaches into a coherent and publishable form and for agreeing to fund the printing of this volume.

Good practice guide in learning and teaching

This is the second edition of our Aston Business School (ABS) Good Practice Guide and the enthusiasm of the contributors appears undiminished. I am again reminded that I work with a group of very committed, dedicated and professional colleagues. Once again this publication is produced to celebrate and promote good teaching across the School and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. Contributors to this Guide were not chosen because they are the best teachers in the School, although they are undoubtedly all amongst my colleagues who are exponents of enthusiastic and inspiring approaches to learning. The Quality Unit approached these individuals because they declared on their Annual Module Reflection Forms that they were doing something interesting and worthwhile which they thought others might find useful. Amongst those reading the Guide I am sure that there are many other individuals who are trying to operate similar examples of good practice in their teaching, learning and assessment methods. I hope that this publication will provoke these people into providing comments and articles of their own and that these will form the basis of next year’s Guide. It may also provoke some people to try these methods in their own teaching. The themes of the articles this year can be divided into two groups. The first theme is the quest to help students to help themselves to learn via student-run tutorials, surprise tests and mock examinations linked with individual tutorials. The second theme is making learning come to life in exciting practical ways by, for example, hands-on workshops and simulations, story telling, rhetorical questioning and discussion groups. A common theme is one of enthusiasm, reflection and commitment on behalf of the lecturers concerned. None of the approaches discussed in this publication are low effort activities on the part of the facilitator, but this effort is regarded as worthwhile as a means of creating greater student engagement. As Biggs (2003)[1] says, in his similarly inspiring way, students learn more the less passive they are in their learning. (Ref). The articles in this publication bear witness of this and much more. Since last year Aston Business School has launched its Research Centre in Higher Education Learning and Management (HELM) which is another initiative to promote excellent learning and teaching. Even before this institution has become fully operational, at least one of the articles in this publication has seen the light of day in the research arena and at least two others are ripe for dissemination to a wider audience via journal publication. More news of our successes in this activity will appear in next year’s edition. May I thank the contributors for taking time out of their busy schedules to write the articles this summer, and to Julie Green who runs the ABS Quality Unit, for putting our diverse approaches into a coherent and publishable form and for chasing us when we have needed it! I would also like to thank Ann Morton and her colleagues in the Centre for Staff Development who have supported this publication. During the last year the Centre has further stimulated the learning and teaching life of the School (and the wider University) via their Learning and Teaching Week and sponsorship of Teaching Quality Enhancement Fund (TQEF) projects. Pedagogic excellence is in better health at Aston than ever before – long may this be because this is what life in HE should be about.

Bayesian methods for neural networks

Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.

Bayesian methods for neural networks

Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.

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.

Noisy fitness evaluation in genetic algorithms and the dynamics of learning

A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stochastic fitness measure and correctly accounts for finite population effects. Although this model describes a number of selection schemes, we only consider Boltzmann selection in detail here as results for this form of selection are particularly transparent when fitness is corrupted by additive Gaussian noise. Finite population effects are shown to be of fundamental importance in this case, as the noise has no effect in the infinite population limit. In the limit of weak selection we show how the effects of any Gaussian noise can be removed by increasing the population size appropriately. The theory is tested on two closely related problems: the one-max problem corrupted by Gaussian noise and generalization in a perceptron with binary weights. The averaged dynamics can be accurately modelled for both problems using a formalism which describes the dynamics of the GA using methods from statistical mechanics. The second problem is a simple example of a learning problem and by considering this problem we show how the accurate characterization of noise in the fitness evaluation may be relevant in machine learning. The training error (negative fitness) is the number of misclassified training examples in a batch and can be considered as a noisy version of the generalization error if an independent batch is used for each evaluation. The noise is due to the finite batch size and in the limit of large problem size and weak selection we show how the effect of this noise can be removed by increasing the population size. This allows the optimal batch size to be determined, which minimizes computation time as well as the total number of training examples required.

Incorporating curvature information into on-line learning

We analyse the dynamics of a number of second order on-line learning algorithms training multi-layer neural networks, using the methods of statistical mechanics. We first consider on-line Newton's method, which is known to provide optimal asymptotic performance. We determine the asymptotic generalization error decay for a soft committee machine, which is shown to compare favourably with the result for standard gradient descent. Matrix momentum provides a practical approximation to this method by allowing an efficient inversion of the Hessian. We consider an idealized matrix momentum algorithm which requires access to the Hessian and find close correspondence with the dynamics of on-line Newton's method. In practice, the Hessian will not be known on-line and we therefore consider matrix momentum using a single example approximation to the Hessian. In this case good asymptotic performance may still be achieved, but the algorithm is now sensitive to parameter choice because of noise in the Hessian estimate. On-line Newton's method is not appropriate during the transient learning phase, since a suboptimal unstable fixed point of the gradient descent dynamics becomes stable for this algorithm. A principled alternative is to use Amari's natural gradient learning algorithm and we show how this method provides a significant reduction in learning time when compared to gradient descent, while retaining the asymptotic performance of on-line Newton's method.

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.