231,173 research outputs found
Lancaster Postgraduate Statistics Centre – creating enterprise and innovation in teaching statistics across disciplines.
No full textThe Lancaster Postgraduate Statistics Centre (PSC) encompasses all aspects of Postgraduate Teaching and Learning within the Mathematics and Statistics department. It is the only UK HEFCE-funded Centre for Excellence in Teaching and Learning that uniquely specialises in postgraduate statistics, and rewards the research and teaching excellence of the Statistics Group. The award-winning purpose-built PSC building opened in February 2008, and features many modern state of the art facilities. Our popular MSc courses and short course programme provide excellent training for those wishing to further their knowledge of statistics. We hold regular Teaching and Learning Seminars that focus on innovative teaching methods and technologies, and offer a visiting fellow scheme as well as specialist training at all levels through master classes and workshops run by experts in the field. This article describes the work of the PSC as we proceed past the third year of grant funding. For more information about activities in the Postgraduate Statistics Centre please see our website at http://www.maths.lancs.ac.uk/psc
SME leaders’ learning in networked learning : an actor-network theory and communities of practice theory informed analysis
No full textThis thesis brings a Communities of Practice perspective together with Actor-network theory to provide a rich understanding of the social learning processes of SME leaders within a networked learning programme. Networked learning as an educational approach is a growing area in higher education. The networked learning programme under investigation forms part of the knowledge exchange initiatives at Lancaster university management school. The research explores the learning process through a qualitative, inductive approach underpinned by an (online and offline) ethnography and is supported by qualitative interviews, the researcher‟s own reflections and other secondary data. The study focuses on three main issues. Firstly, it provides an in-depth understanding of the way a learning community comes together. Secondly, it shows how delegates learn through co-constructing knowledge and the practices within the learning community. It is proposed that the learning community constructs, learns and challenges the situated curriculum. This takes place through the process of legitimate peripheral participation. Gaining fuller participation leads to an increased identification with that of „leader‟. Thirdly, the study theorises four conceptual learning spaces to show where the delegates learn. They are conceived of as an effect of the delegates‟ engagement with the integrated learning model underpinning the networked learning programme. The thesis concludes with a discussion presenting a set of learning and design principles. These can be used to inform the design and thinking around networked learning and knowledge exchange. Combining the theoretical frameworks of Actor-network theory and Communities of Practice theory is unique in the context of exploring the learning processes within networked learning. This combination stretches aspects of the main tenets of each theory and offers contributions to all three theoretical frameworks
Photographs from the 1972 Commencement of Lancaster Theological Seminary.
No full textDigitized copies of 4 black and white photographs from the 1972 Commencement exercises. 4 TIFF.A list of 1972 graduates. 1 PDFInformation from the back of photographs:
Recipient of S.T.M. degree James D. Corbett 1972
Rev. Robert Aregood, Sec. and the Rev. Dorothy M. Book, pres of Alumni Counci
Introducing the Lancaster Postgraduate Statistics Centre – a Centre of Excellence in Teaching and Learning (CETL).
No full textIn the spring of 2005, the University of Lancaster was successful in winning a £4.85 million bid to fund a Centre of Excellence in Teaching and Learning (CETL). In common with other CETLs, the Lancaster CETL has the core aim of achieving excellence in teaching, however our specific focus on the development of postgraduate statistics taught both within the discipline of statistics and more broadly in other disciplines is more unique. The award is partially funding a £3.3 million building, the Postgraduate Statistics Centre (PSC), to expand the postgraduate activities of the department and will provide state of the art new teaching spaces for teaching statistics courses. In addition to this, funding has provided the department with several new members of staff and will allow a range of new resources to be developed within the centre. This article will give a general overview of the PSC and will discuss the main aims and objectives of the project followed by a brief summary of our achievements to date
Lancaster - James M. Lancaster
No full text1861Notes in Pencil "James M. Lancaster, Ky."7 1/4 x 5 1/4 albumen prin
Photographs of the 1991 Commencement at Lancaster Theological Seminary and a list of the graduates.
No full text12 Digitized copies of photographs. One PDF. 12 TIFFS One PDF list of graduatesIndividual identified on the back of _image00001:
Edmund M. MillerIndividuals identified on the back of _image00002:
United Methodists: Hayser, Greiner, Faux, George, House, BrennerIndividuals identified on the back of _image00003:
Jeff Zimmerman, Linda LaurmanIndividuals identified on the back of _image00004:
Zimmerman, Jordan, LaurmanIndividuals identified on the back of _image00005:
Knappenberger, Dender, Esslinger, HeiderreichIndividuals identified on the back of _image00007:
Martha Ann BaumerIndividuals identified on the back of _image00008:
Rev. Martha Baumer, Speaker 166th Commencement, Paul Westcoat Penn West Conference MinisterIndividuals identified on the back of _image00009:
Members of Class of 1941Individuals identified on the back of _image00010:
Sandra Thomas D.Min 1991 with husband and childrenIndividuals identified on the back of _image00012:
Paula Oerderbrink, Ann Graves, Gary Daniels, Nerva Cole, Ann Thompson, Jolene Bright, Susan Marti
Intelligent student systems : an application of viewpoints to intelligent learning environments
No full textIntelligent Student Systems are a class of Intelligent Learning Environments that place the learner in the role of a tutor rather than a student. In an analogy with the educational practice of peer tutoring users learn by teaching the computer -- inverting the predominant `computer as tutor' metaphor. Intelligent Student Systems emphasize the learner's viewpoint in educational interactions in preference to the system's conception of the domain. These systems are considered to be less complex than Intelligent Tutoring Systems and to have the potential to generate novel human-computer educational interactions. Viewpoints also have an integral part in knowledge representation in Intelligent Learning Environments and they are utilised in the design and implementation of an Intelligent Student System in economics. Testing of the system produced insights into the future application of Intelligent Student Systems
Recommendations for changes in UK National Recovery Guidance (NRG) and associated guidance from the perspective of Lancaster University's Hull Flood Studies
No full textThis report was commissioned by the Civil Contingencies Secretariat (CCS) following the publication of Lancaster University‟s Hull Flood Project and Hull Children‟s Flood Project. Its principal purpose is to identify how findings made as a result of the two research projects could be integrated into the Cabinet Office‟s National Recovery Guidance (NRG), as a means to improve affected communities‟ ability to recover from emergency events.
The report, in effect, details a desktop analysis of UK Civil Protection (CP) guidance, from a bottom-up perspective (i.e. using as its critical lens, the lived experiences of members of the public who were tested by the Hull flooding of 2007 and its aftermath)
Place of death in the Morecambe Bay area : patterns and preferences for place of final care and death among terminally ill cancer patients and their carers.
No full textEfficient Bayesian inference for partially observed stochastic epidemics and a new class of semi-parametric time series models
No full textThis thesis is divided in two distinct parts. In the First part we are concerned with developing new statistical methodology for drawing Bayesian inference for partially observed stochastic epidemic models. In the second part, we develop a novel methodology for constructing a wide class of semi-parametric time series models. First, we introduce a general framework for the heterogeneously mixing stochastic epidemic models (HMSE) and we also review some of the existing methods of statistical inference for epidemic models. The performance of a variety of centered Markov Chain Monte Carlo (MCMC) algorithms is studied. It is found that as the number of infected individuals increases, then the performance of these algorithms deteriorates. We then develop a variety of centered, non-centered and partially non-centered reparameterisations. We show that partially non-centered reparameterisations often offer more effcient MCMC algorithms than the centered ones. The methodology developed for drawing eciently Bayesian inference for HMSE is then applied to the 2001 UK Foot-and-Mouth disease outbreak in Cumbria. Unlike other existing modelling approaches, we model stochastically the infectious period of each farm assuming that the infection date of each farm is typically unknown. Due to the high dimensionality of the problem, standard MCMC algorithms are inefficient. Therefore, a partially non-centered algorithm is applied for the purpose of obtaining reliable estimates for the model's parameter of interest. In addition, we discuss similarities and differences of our fndings in comparison to other results in the literature. The main purpose of the second part of this thesis, is to develop a novel class of semi-parametric time series models. We are interested in constructing models for which we can specify in advance the marginal distribution of the observations and then build the dependence structure of the observations around them. First, we review current work concerning modelling time series with fixed non-Gaussian margins and various correlation structures. Then, we introduce a stochastic process which we term a latent branching tree (LBT). The LBT enables us to allow for a rich variety of correlation structures. Apart from discussing in detail the tree's properties, we also show how Bayesian inference can be carried out via MCMC methods. Various MCMC strategies are discussed including non-centered parameterisations. It is found that non-centered algorithms significantly improve the mixing of some of the algorithms based on centered reparameterisations. Finally, we present an application of this class of models to a real dataset on genome scheme data
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