The Standing Conference on Teacher Education North-South (SCoTENS) Citizenship Website

This site been designed primarily to support students, staff and other professionals involved in Initial Teacher Education North or South. However, it should also be of interest to others within the formal and non-formal education sectors.Those involved with, or interested in, Education for Citizenship.

Religious segregation and teacher education in Northern Ireland

This paper surveys the extent of religious segregation in teacher education in Northern Ireland and notes that there are elements of separation within a general context of (increasing) common teacher education. With reference to liberal and communitarian theories the case for separate teacher education is considered. It is acknowledged that a case can be made for forms of separate teacher education in a liberal society but that certain limits or expectations should apply. A common teacher education is found to be desirable but it is suggested that in order to justify its dominant status in a plural environment it must be accommodating of religion, encourage dialogical engagement around concepts of shared fate and cultivate a sense of community. © 2010 Taylor & Francis.

Exploring diversity through ethos in initial teacher education

This paper considers whether there is value in introducing student teachers to schools of different ethos as part of their initial teacher education. A 2-year study of undergraduate post-primary student teachers at a university college in Northern Ireland reveals that encounters with schools of different ethos can help student teachers to understand differences between schools and their visions of education, as well as correcting misunderstandings and challenging stereotypes. It is argued that as a result of experiencing diverse examples of ethos, student teachers may also be helped to understand the complexity of schools as organisations and to position themselves and their professional practice within wider debates about the aims of education and schools as communities of practice. © 2008 Elsevier Ltd. All rights reserved.

Changes in quality and safety attributes throughout long term storage of tomato juice processed by high pressure.

Tomato is the second most widely grown vegetable crop across the globe and it is one of widely cultivated crops in Sri Lanka. However, tomato industry in Sri Lanka facing a problem of high postharvest loss (54%) during the glut coupled with heavy revenue loss to the country by importing processed products. The aim of this work is to develop shelf-stable tomato product with maximum quality characteristics using high pressure processing (HPP). Tomato juice with altered and unaltered pH was processed using HPP at 600 MPa for 1 min after blanching (90 oC/2 min). As a control tomato juice was subjected to thermal processing (TP) at 95 oC /20 min. Processed samples were stored under 20oC and 28oC for 9 month period and analysed for total viable count (TVC) and instrumental colour (L, a, b) value at 0,1,2 3, and 4 week and 2, 3, 6 and 9 months interval. The raw juice sample had initial 6.69 log10 CFU/ml and both TP and HPP caused a more than 4.69 log10 reduction in the TVC of juice and microbial numbers remained low throughout the storage period even at 3 months after storage irrespective of the storage temperature. Both TP and HPP treated samples had the redness ⤘a value’ of 14.44-17.15 just after processing and showed non-significant reduction with storage in all the treatments after 3 months. The storage study results and discussed in relation to the end goal and compared with the literature.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

A Transatlantic Experience - Student and Teacher Perspective