Portraits of teachers in landscapes of change: exploring the role of teachers in school improvement

This thesis focuses on an investigation which aimed to explore how teachers interpret their roles and construct their professional identities in relation to school improvement and how they can be supported in their contributions in this respect. The initial research questions were set within a conceptual framework linking teacher professionalism and school improvement, in particular the symbiotic and reciprocal relationships between individuals and organisations which were illuminated by the concepts of agency and structuration. Research aims, questions and conceptual development were reflexively and iteratively modified, to encompass the significance of school context in influencing professional identity and agency and to explore intractable dilemmas arising in interpreting external and internal policy requirements in relation to personal values. The implications of narrative enquiry for validity were acknowledged, focusing on distilling the ‘essence’ of situated professional selves and identities through portraiture to explore these substantive themes. The professional values, priorities and aspirations of six teachers were investigated through semi-structured interviews incorporating elicitation tools, and presented as a form of nested case study where individual portraits were set within the organisational landscapes of their two contrasting schools, based on evidence representing a range of perspectives. This involved navigating the methodological territory between narratives and portraits. Analysis is presented as an ‘exhibition’, with three ‘galleries’ exploring different themes emerging from the empirical evidence. This enabled comparisons to be made between the stance that teachers choose to take in relation to internally or externally driven change and their own motivations, aspirations and actions to achieve outcomes according with their personal values and concerns. The research contributes new understandings in relation to how, within ‘imposed’, ‘selected’ and ‘constructed’ organisational environments (Bandura, 2001), teachers’ professional identities are, to a greater or lesser extent, imposed or constructed. This in turn affects their agency in influencing their professional environments aligned with their personal professional values and aspirations. The empirical evidence therefore shows the significance of organisational cultures, leadership and individual agency, in influencing how professional environments and identities are constructed or imposed. A new model is derived from the empirical evidence and parallel conceptual development, contrasting complementary epistemological, ontological and agentic perspectives for schooling. This provides a framework for developing professional identity and professionality, in which individual agency is considered a vital dimension. Since teachers have a predominantly narrative understanding of reality, it is argued that narrative and visual approaches are key to such school improvement work. Making the agentic perspective visible and developmental supports key components of agency - intentionality, forethought, self-reactiveness and self-reflectiveness (ibid.). The resulting levels of engagement give grounds for optimism in supporting teachers’ more powerful individual and collective agency, including working critically and strategically with systemic reform, contributing proactively to local initiatives for change and pursuing personal change agendas

The Parent Champion Programme: independent evaluation

The Parent Champion Programme is a personal development course which was piloted in two Medway Children's Centres by 'Every Family Matters' from April 2011 - April 2013, sponsored by Big Lottery funding. This evaluation of the programme examines evidence including parents' testimonies, questionnaire data and reports from professionals including Children's Centre Managers. The programme has resulted in some profound improvements for parents and vulnerable families including a 'stepping down' of Social Services provision for a significant number of families. Approaches are based on coaching in the development of positive, connected, emotionally intelligent approaches that affirm parents and children as people and empower them as agents of change to make choices that improve their life chances and wellbeing. 12 participants have progressed to OCN Level 3 accreditation which includes developing capacity to champion the programme's approaches within the community

Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions

We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher linear discriminant classifiers, focusing on the casewhen there are fewer training observations than data dimensions. The specific form and simplicity of this ensemble permits a direct and much more detailed analysis than existing generic tools in previous works. In particular, we are able to derive the exact form of the generalization error of our ensemble, conditional on the training set, and based on this we give theoretical guarantees which directly link the performance of the ensemble to that of the corresponding linear discriminant learned in the full data space. To the best of our knowledge these are the first theoretical results to prove such an explicit link for any classifier and classifier ensemble pair. Furthermore we show that the randomly projected ensemble is equivalent to implementing a sophisticated regularization scheme to the linear discriminant learned in the original data space and this prevents overfitting in conditions of small sample size where pseudo-inverse FLD learned in the data space is provably poor. Our ensemble is learned from a set of randomly projected representations of the original high dimensional data and therefore for this approach data can be collected, stored and processed in such a compressed form. We confirm our theoretical findings with experiments, and demonstrate the utility of our approach on several datasets from the bioinformatics domain and one very high dimensional dataset from the drug discovery domain, both settings in which fewer observations than dimensions are the norm

Sharp generalization error bounds for randomly-projected classifiers

We derive sharp bounds on the generalization error of a generic linear classifier trained by empirical risk minimization on randomly projected data. We make no restrictive assumptions (such as sparsity or separability) on the data: Instead we use the fact that, in a classification setting, the question of interest is really ‘what is the effect of random projection on the predicted class labels?’ and we therefore derive the exact probability of ‘label flipping’ under Gaussian random projection in order to quantify this effect precisely in our bounds

Overlapping memory replay during sleep builds cognitive schemata

Sleep enhances integration across multiple stimuli, abstraction of general rules, insight into hidden solutions and false memory formation. Newly learned information is better assimilated if compatible with an existing cognitive framework or schema. This article proposes a mechanism by which the reactivation of newly learned memories during sleep could actively underpin both schema formation and the addition of new knowledge to existing schemata. Under this model, the overlapping replay of related memories selectively strengthens shared elements. Repeated reactivation of memories in different combinations progressively builds schematic representations of the relationships between stimuli. We argue that this selective strengthening forms the basis of cognitive abstraction, and explain how it facilitates insight and false memory formation

Towards large scale continuous EDA: a random matrix theory perspective

Estimation of distribution algorithms (EDA) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging and as a result existing EDAs lose their strengths in large scale problems. Large scale continuous global optimisation is key to many real world problems of modern days. Scaling up EAs to large scale problems has become one of the biggest challenges of the field. This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective EDA-type algorithms for large scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections of the set of fittest search points to low dimensions as a basis for developing a new and generic divide-and-conquer methodology. This is rooted in the theory of random projections developed in theoretical computer science, and will exploit recent advances of non-asymptotic random matrix theory

On the mechanism of response latencies in auditory nerve fibers

Despite the structural differences of the middle and inner ears, the latency pattern in auditory nerve fibers to an identical sound has been found similar across numerous species. Studies have shown the similarity in remarkable species with distinct cochleae or even without a basilar membrane. This stimulus-, neuron-, and species- independent similarity of latency cannot be simply explained by the concept of cochlear traveling waves that is generally accepted as the main cause of the neural latency pattern. An original concept of Fourier pattern is defined, intended to characterize a feature of temporal processing—specifically phase encoding—that is not readily apparent in more conventional analyses. The pattern is created by marking the first amplitude maximum for each sinusoid component of the stimulus, to encode phase information. The hypothesis is that the hearing organ serves as a running analyzer whose output reflects synchronization of auditory neural activity consistent with the Fourier pattern. A combined research of experimental, correlational and meta-analysis approaches is used to test the hypothesis. Manipulations included phase encoding and stimuli to test their effects on the predicted latency pattern. Animal studies in the literature using the same stimulus were then compared to determine the degree of relationship. The results show that each marking accounts for a large percentage of a corresponding peak latency in the peristimulus-time histogram. For each of the stimuli considered, the latency predicted by the Fourier pattern is highly correlated with the observed latency in the auditory nerve fiber of representative species. The results suggest that the hearing organ analyzes not only amplitude spectrum but also phase information in Fourier analysis, to distribute the specific spikes among auditory nerve fibers and within a single unit. This phase-encoding mechanism in Fourier analysis is proposed to be the common mechanism that, in the face of species differences in peripheral auditory hardware, accounts for the considerable similarities across species in their latency-by-frequency functions, in turn assuring optimal phase encoding across species. Also, the mechanism has the potential to improve phase encoding of cochlear implants

Effects of Finite Layer Thickness on the Differential Capacitance of Electron Bilayers

We have calculated the effects of finite thickness on electron or hole layers in double-quantum-well systems. In particular, we apply our model to calculate the Eisenstein ratio and the interlayer capacitance of a biased bilayer device; these are direct measures of the compressibility of the charge carriers in the layers. We show that our model agrees well with the experimental layer-occupancy data for a device of this type. We present results for the regime of negligible interlayer tunneling, zero applied magnetic field, and low layer densities, when the compressibility of one or both layers is negative