Making sustainability ‘real’: using group-enquiry to promote education

Sustainable development is now widely held as a transcendental ideal of town and country planning, yet the way in which it is taught in planning schools remains problematic. This arises from a range of factors, including the all-persuasive nature of sustainability and the lack of solid examples of success through implementation. The issue of how best to promote learning for sustainable development in planning has arguably intensified in the last two years in the case of the Royal Town Planning Institute- sponsored ‘fast track’ one-year Masters, which has reduced the opportunities for students to engage in wider (and perhaps even deeper) concepts, including that of sustainable development. This paper explores this through discussion of a specific project developed at Queen’s University Belfast, facilitated by a grant from the UK Higher Education Academy. Working with a local community, this entailed a group of students working on their Masters thesis collectively addressing issues of sustainable regeneration in a small Irish market town. The design of the project draws heavily on the concepts of enquiry based learning, experiential learning and action competence, whichare seen as being central to improving education for sustainable development (ESD). The paper explores the benefits of such an approach and discusses the ways in which this experience can help enhance student’s experience of ESD.

SPHERE: Stormont Parliamentary Hansards: Embedded in Research and Education

SPHERE (Stormont Parliamentary Hansards: Embedded in Research and Education) was a JISC-funded project based at King’s College, London and Queen’s University, Belfast, working in Partnership with the Northern Ireland Assembly Library, and the NIA Official Report (Hansard). Its purpose was to assess the use, value and impact of The Stormont Papers digital resource, and to use the results of this assessment to make recommendations for a series of practical approaches to embed the resource within teaching, learning and research among the wider user community. The project began in November 2010 and was concluded in April 2010.

A series of formal reports on the project are published by JISC online at http://www.jisc.ac.uk/whatwedo/programmes/digitisation/impactembedding/sphere.aspx

SPHERE Impact analysis summary
Portable Document Format
SPHERE interviews report
SPHERE Outreach use case
SPHERE research use case
SPHERE teaching use_case
SPHERE web survey report
SPHERE web analysis

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

How using Technology Enhanced Learning could help modernise traditional large group teaching or Lecturing.

The efficiency of lecturing or large group teaching has been called into question for many years. An abundance of literature details the components of effective teaching which are not provided in the traditional lecture setting, with many alternative methods of teaching recommended. However, with continued constraints on resources large group teaching is here to stay and student’s expect and are familiar with this method.

Technology Enhanced Learning may be the way forward, to prevent educators from “throwing out the baby with the bath water”. TEL could help Educator’s especially in the area of life sciences which is often taught by lectures to engage and involve students in their learning, provide feedback and incorporate the “quality” of small group teaching, case studies and Enquiry Based Learning into the large group setting thus promoting effective and deep learning.