A new 14+: vocational diplomas and the future of schools, colleges and universities. An educational and campaigning pamphlet

Written in an accessible and campaigning style, this pamphlet affords a valuable context to the introduction of the first group of specialist diplomas for 14 year olds in September 2008. The diplomas are the latest in a line of failed initiatives that have sought to provide vocational ‘alternatives’ for those young people staying in full-time education and not considered ‘academic’. Rather than developing any useful employment skills, Allen and Ainley argue that their introduction reflects the changing significance of education in the division and social control of learners that now extends from school to college and on to university. Those who are opposed to the current post-14 agenda, must not only put forward radical alternatives to the current curriculum offer but also, the authors argue, address issues of democracy and accountability. To do this, teacher trade unionists must make new types of alliances with local communities and also with their students.

University students with disabilities in Universities. A study of their inclusion

Access to higher education has increased among students with disabilities, and universities are adopting different alternatives which must be assessed. The purpose of this study was to identify the situation of a sample of students with disabilities (n=91) who attend a university in Spain, through the design and validation of the “CUNIDIS-d” scale, with satisfactory psychometric properties. The results show the importance of making reasoned curriculum adaptations, adapting teacher training, improving accessibility and involving all the university community. Different proposals were provided which support the social dimension of the EHEA.

Bimodal biometric verification based on face and lips

In this paper, we present a novel approach to person verification by fusing face and lip features. Specifically, the face is modeled by the discriminative common vector and the discrete wavelet transform. Our lip features are simple geometric features based on a lip contour, which can be interpreted as multiple spatial widths and heights from a center of mass. In order to combine these features, we consider two simple fusion strategies: data fusion before training and score fusion after training, working with two different face databases. Fusing them together boosts the performance to achieve an equal error rate as low as 0.4% and 0.28%, respectively, confirming that our approach of fusing lips and face is effective and promising.