The Language of Failure: Retrospect

This article originally appeared in the United States and is reprinted with the permission of Impact, the Journal of the New York State Association for Supervision and Curriculum Development .     

Resisting and Surviving: Asian girls and education

The Schooling and Identity of Asian Girls FARZANA SHAIN Stoke-on-Trent, Trentham Books, 2002 160 pp., £15.99 (pbk) ISBN 1-85856181-7 Asian girls are not often the subject of mainstream texts and research on issues such as youth, identities and schooling—so this book makes a welcome addition. Indeed, research on femininities continues to be somewhat eclipsed by the ongoing ‘boys in crisis’ debate, in which the primary focus of academic and policy attention has been directed at boys' ‘underachievement’ and associated social and behavioural problems. Equally, issues of ‘race’, religion and ethnicity have been historically sidelined within mainstream sociological and educational research and therefore The Schooling and Identities of Asian Girls makes for a refreshing change. Despite the relative academic silence regarding Asian identities, recent world events have propelled Asian (but particularly Muslim) identities, into the British media spotlight. Since the high-profile Salman Rushdie controversy of the early 1990s, through to the British and American governments' declaration of the ‘war on terrorism’ after September 11th 2001, Muslim identities and associated issues of citizenship, nationality, belonging and identity are now all hot topics for debate.     

Primary Teacher Trainees: identity formation in an age of anxiety

This paper considers primary teacher trainees' understandings about touching the children in their care. A sample of 182 graduating trainees in Auckland, New Zealand, was surveyed. The majority considered touching children to be a source of regulation and anxiety for them. The paper suggests that the trainees' insistence on ‘being aware’, taken together with their failure to name ‘sexual abuse’ as an explicit concern, is unlikely to produce a capacity to engage critically with what is at stake for teachers in an era of heightened anxiety about child vulnerability.     

Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface

In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.