Learning to Teach and its Implications for the Continuum of Teacher Education: A Nine Country Cross-national Study.

The purpose of this Report is to inform discussions, policy formulation, and strategic planning on teacher education in Ireland. The research gives priority to initial teacher education (ITE) and induction, their interface, and implications for the continuum of teacher education, including continuing professional development (CPD). The study involved a two-pronged approach: a narrative review of recent and relevant literature and a cross-national review of teacher education policies in nine countries, namely, Ireland, Northern Ireland, Scotland, England, Finland, USA, Poland, Singapore and New Zealand. Adopting a broad, balanced and comprehensive understanding of the role of the contemporary teacher, it provides a framework for developing quality teacher education in Ireland. The Report incorporates exemplars of good practice and notes their implementation challenges for the Irish context.  Chapter One provides a framework for conceptualising quality teacher education and the continuum. Key features that emerge from the literature are discussed: teachers¿ practice, quality teaching, the professional life-cycle, teacher learning and relationships. With more specific reference to the continuum, Chapter Two overviews initial teacher education, induction, learning outcomes and accreditation in the selected countries, including Ireland. Key features of policy in the various countries are summarised. Individual country profiles, incorporating descriptions of socio-political, teaching and teacher education contexts, are further detailed in Appendix A. Chapter Three analyses relevant literature on initial teacher education, induction, learning outcomes/professional standards and accreditation. Along with previous chapters it provides the basis for recommendations for teacher education that are presented in Chapter Four. Chapter Four draws together the findings emerging from the cross-national review in terms of the contemporary context of teacher education in Ireland and identifies key challenges and possible lines of policy development as well as recommendations for the Teaching Council and other teacher education stakeholders. Each generation has an opportunity to provide the vision and resources for renewing teacher education in light of ambitious social, economic and educational aspirations to meet perceived societal and education challenges (as occurred in the 1970s). Despite the publication of two key reviews of initial teacher education a number of years ago, there is considerable scope for further reform of teacher education. However, significant changes have occurred to teacher education course provision and content over the last 100 years. In this report, we have stressed the need for, and called for investment in, greater system and programme coherence, mentoring to support assisted practice, knowledge integration, critical reflective practice, inquiry and the development of vibrant partnerships between higher education institutions and schools as the basis for teacher education reform across the continuum. This Executive Summary presents the Report¿s context, key findings and recommendations emerging from the analysis.  

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.