Social inequalities in educational attainment are widely reported despite educational reforms aimed at providing equal educational opportunities for all. Variation in attainment between different socio-economic groups is apparent in the early stages of . education, at primary level, and continues through compulsory into further and higher education. Many research studies have investigated the effects of social factors at different points in the education system but there is less research into how such influences develop throughout the school career and into adult life. Much education research now focuses on investigating educational progress and the factors that have an impact on attainment and progression throughout the education system. The research presented here has two related and equally important aims. The first is to investigate appropriate statistical modelling techniques for the analysis of education data, in particular for examining educational attainment and progress. However, progress is a dynamic concept and can only be examined using longitudinal data. The increasing availability of large scale longitudinal data, on a national basis, provides new opportunities to explore the effects of social and other factors on educational progress. Hence the second main aim of this research is to investigate the scope of national longitudinabstudies for examining the changing and developing effects of influential factors, such'as social background and school, on educational attainment and progress. The statistical modelling techniques are applied to data from one such study, the British Cohort Study of 1970, and the analyses provide a case study to illustrate how education data from longitudinal studies can be investigated. The fmdings from the analyses are compared against current and existing research in order to evaluate the potential of data from national birth cohort studies for the investigation and monitoring of socio-economic trends in educational attainment and progression.

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Proteins play critical biochemical roles in all living organisms; in human beings, they are the targets of 50% of all drugs. Although the first protein structure was determined 60 years ago, experimental techniques are still time and cost consuming. Consequently, in silico protein structure prediction, which is considered a main challenge in computational biology, is fundamental to decipher conformations of protein targets. This thesis contributes to the state of the art of fragment-assembly protein structure prediction. This category has been widely and thoroughly studied due to its application to any type of targets. While the majority of research focuses on enhancing the functions that are used to score fragments by incorporating new terms and optimising their weights, another important issue is how to pick appropriate fragments from a large pool of candidate structures. Since prediction of the main structural classes, i.e. mainly-alpha, mainly-beta and alpha-beta, has recently reached quite a high level of accuracy, we have introduced a novel approach by decreasing the size of the pool of candidate structures to comprise only proteins that share the same structural class a target is likely to adopt. Picking fragments from this customised set of known structures not only has contributed in generating decoys with higher level of accuracy but also has eliminated irrelevant parts of the search space which makes the selection of first models a less complicated process, addressing the inaccuracies of energy functions. In addition to the challenge of adopting a unique template structure for all targets, another one arises whenever relying on the same amount of corrections and fine tunings; such a phase may be damaging to "easy' targets, i.e. those that comprise a relatively significant percentage of alpha helices. Owing to the sequence-structure correlation based on which fragment-based protein structure prediction was born, we have also proposed a customised phase of correction based on the structural class prediction of the target in question. After using secondary structure prediction as a "global feature" of a target, i.e. structural classes, we have also investigated its usage as a "local feature" to customise the number of candidate fragments, which is currently the same at all positions. Relying on the known facts regarding diversity of short fragments of helices, sheets and loops, the fragment insertion process has been adjusted to make "changes" relative to the expected complexity of each region. We have proved in this thesis the extent to which secondary structure features can be used implicitly or explicitly to enhance fragment assembly protein structure prediction.

Thesis (M.It.)(Informatics)--University of Pretoria, 2000. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

Understanding how to develop instructional materials for distance education students is a challenging problem, but it is exacerbated when a domain is complex to teach, such as computer science. Visual representations have a history of use in computing as a means to alleviate the difficulties of learning abstract concepts. However, it is not clear whether improvements observed are as a result of improvements in the visual representations used in instructional materials or due to individual differences in students. This research examines the two themes of individual differences and visual representation in order to investigate how they collectively impact on improving instructional materials for distance education students studying computer science. It investigates the impact of different representations on learning while additionally investigating the relationship between individual differences and student learning. The research in this thesis shows that visual representations are important in designing instructional materials. In particular, texts with visual representations have the power to cue students to perceive instructional materials as easier to process and more engaging. Investigation into the impact of concrete high-imagery versus abstract low-imagery visual representations illustrated that concrete visual representations incurred fewer cognitive overheads for computer science students and were able to ameliorate the challenges of learning computing. The research in this thesis into individual differences demonstrated that Imagers did benefit more from studying instructional materials containing text with visual components. However the research indicates that appropriate selection of individual difference tests is dependent upon the application, i.e., whether the results are to be used to assess generalised tendencies or episodes in learning and whether the tests examine underlying approaches to cognition or practices in education. An underlying question was whether students studying instructional materials containing low-imagery visual representations would cope as well as those studying high-imagery ones. Accomplished learners demonstrated that they could perform as well as with those receiving high-imagery visual representations. However, studying and recalling these materials did incur more cognitive processing. This thesis argues that improving instructional materials by including appropriate visual representations is a useful basis for improving learning for distance education computer science students.

This thesis presents a novel approach to the problem of behaviour modelling within computer vision. This technique is not based upon statistical measures of typicality, but upon building an understanding of the way people navigate towards a goal. Representing movement through the scene in terms of the known goals and obstacles and interpreting people's behaviour as representative of underlying intentions enables behaviour to be explained in terms of these previously defined goals. A family of related algorithms for performing this goal-directed analysis of behaviour are presented and evaluated, alongside a number of metrics for measuring how well the computed explanation matches the observed behaviour. These measurements can be interpreted as measurements of goal-directedness or intentionality. The system is evaluated using a novel methodology which involves comparing the algorithmic output with the performance of humans engaged in a visual surveillance task. An application of this technique is demonstrated within the visual surveillance domain, providing classification of behaviour patterns as explicable or inexplicable. The advantages of such an approach are multiple: it handles the presence of movable goals (for example, parked cars) with ease, and trajectories which have never before been presented to the system can be classified as explicable. The output of the system (for example �Agent n is heading towards goal m� with an associated score indicating how good this explanation is) are easily interpreted. The systems described in this thesis could also in principle be extended to handle richer varieties of scene, moving obstacles, and more complicated systems of goals.

Plants are of fundamental importance to life on Earth. The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to measure plant health, and even to model climate change. The current availability of botanists is increasingly failing to meet the growing demands for their expertise. These demands range from amateurs desiring help in identifying plants, to agricultural applications such as automated weeding systems, and to the cataloguing of biodiversity for conservational purposes. This thesis aims to help fill this gap, by exploring computational techniques for the automated analysis and classification of plants from images of their leaves. The main objective is to provide novel techniques and the required frame¬work for a robust, automated plant identification system. This involves firstly the accurate extraction of different features of the leaf and the generation of appropriate descriptors. One of the biggest challenges involved in working with plants is the high amounts of variation that may occur within a species, and high similarity that exists between some species. Algorithms are introduced which aim to allow accurate classification in spite of this. With many features of the leaf being available for use in classification, a suitable framework is required for combining them. An efficient method is proposed which selects on a leaf-by-leaf basis which of the leaf features are most likely to be of use. This decreases computational costs whilst increasing accuracy, by ignoring unsuitable features. Finally a study is carried out looking at how professional botanists view leaf images. Much can be learnt from the behaviour of experts which can be applied to the task at hand. Eye-tracking technology is used to establish the difference between how botanists and non-botanists view leaf images, and preliminary work is performed towards utilizing this information in an automated system.

E-commerce creates opportunities for companies to implement internal and external connections. This establishes new capabilities to meet market needs and changes the way business can be conducted through the redefinition of rules and roles in the extended enterprise. The literature suggests that the emergence of Internet / Web based communities of common interest accelerates this shift towards the concept of the extended enterprise by enabling enterprises to align within a series of 'value networks' against other groups of enterprises. A collaborative approach and shared benefits are among the crucial factors which bind these value networks of closely integrated business communities. This study investigates the cultural and organisational context of implementing e-commerce to support business to business collaborative relationships. Using primarily qualitative and exploratory methods, various interrelationships are explored through two in depth case studies. The research approach is structured by underpinning theories, predominantly transaction cost economics, and informed by key findings from the literature search of the topic domain. The research itself is based on the assumption that collaborative e-commerce cannot be considered in a vacuum and that a holistic perspective is required, particularly in considering the socio-political and relational aspects of such inter-organisational systems. The work undertaken therefore seeks to identify the interrelationships between the IT, organisational and business aspects of collaborative business to business relationships. The research concludes that a number of interdependent factors are key in implementing e-commerce to support collaborative relationships. Foremost among these are the redesign of processes and practices, common information interchange standards and support, a consultative approach based on mutual trust, the sharing of benefits and a willingness to learn and to adopt a 'network' perspective. The findings are encapsulated into an e-commerce/collaborative relationship framework developed by incorporating key findings from the case studies, as well as themes from the literature. This framework may be a useful tool for analysis in other case studies and could be refined and enhanced by further such research.