Study behaviours in an increasingly digital world: Learning habits, top tips and 'study hacks' questionnaire survey

In response to recent changes in the higher education market, student performance and competitor activity, The Open University has developed strategic objectives around a shift to developing ‘digital by design’ modules and the development of new digital tools to improve student success rates. In order to design effective tools, this initial piece of research was designed to understand more about students’ current study behaviours. The survey was built on a framework with student success at the centre, and generated a great deal of rich, qualitative data about how current distance learning students approach their study. The data was analysed using a thematic analysis, and produced a number of interesting themes. These included a variety of digital personas; priorities when organising study sessions; note-taking methods and reasons for making notes; and boundaries. The practical applications of these findings are some embryonic concepts for new tools and digital spaces for students that encourage the development of successful study behaviours. These concepts are being developed in conjunction with a rigorous research plan

Regular Grids: An Irregular Approach to the 3D Modelling Pipeline

The 3D modelling pipeline covers the process by which a physical object is scanned to create a set of points that lay on its surface. These data are then cleaned to remove outliers or noise, and the points are reconstructed into a digital representation of the original object. The aim of this thesis is to present novel grid-based methods and provide several case studies of areas in the 3D modelling pipeline in which they may be effectively put to use. The first is a demonstration of how using a grid can allow a significant reduction in memory required to perform the reconstruction. The second is the detection of surface features (ridges, peaks, troughs, etc.) during the surface reconstruction process. The third contribution is the alignment of two meshes with zero prior knowledge. This is particularly suited to aligning two related, but not identical, models. The final contribution is the comparison of two similar meshes with support for both qualitative and quantitative outputs

Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self-Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA

Enquiry Pull Research: An Ethnomethodological Approach to Lean Construction Research or a Lean Approach to Ethnomethodological Research

This paper assembles some principals from three strands of thought: lean theory; ethnomethodology; and Wittgensteinian philosophy. These are considered with a view to their impact on research design and used here as a basis for an initial exploration of a candidate research topic, in order to illustrate this impact. Principles of Lean Theory are considered, along with those from Wittgensteinian social enquiry and Ethnomethodology, in order to suggest a strategy for Lean Research. These are applied to the intial consideration of a candidate research topic, in order to illustrate the argument

Remarks on the rolling tachyon BCFT

It is shown how the boundary correlators of the Euclidean theory corresponding to the rolling tachyon solution can be calculated directly from Sen's boundary state. The resulting formulae reproduce precisely the expected perturbative open string answer. We also determine the open string spectrum and comment on the implications of our results for the timelike theory.Comment: 20 pages, harvmac(b), no figure

Redshift distributions of extragalactic galaxy surveys

Measurements of the large-scale structure of the universe are key observables to study fundamental physics. This thesis focuses on the calibration of photometric redshift distributions of extragalactic galaxy surveys and their impact on the analysis of large-scale structure data. First, I examine the impact of redshift distribution uncertainties on the cosmological inference from weak lensing measurements. The weak gravitational lensing effect, known as cosmic shear, distorts the shape of galaxy images due to the distribution of gravitating matter along the line sight. Thus, it provides a probe of the matter distribution in the universe. However, modelling the observed cosmic shear signal requires knowledge about the distribution of observed galaxies along the line of sight, which is usually determined through photometric redshifts. I develop a method that accurately propagates residual redshift distribution uncertainties into the weak lensing likelihood and perform a self-calibration of the redshift distribution with cosmic shear data. Second, I develop a new method to assign photometrically observed galaxies to tomographic redshift bins. The goal is to obtain compact distributions and to reduce the overlap between redshift bins caused by catastrophic outliers in the photometric redshift estimation. This is achieved by combining a self-organising map with a simulated annealing algorithm which optimises the clustering cross-correlation signal between a photometric galaxy catalogue and a spectroscopically observed sample of reference galaxies. Finally, I perform consistency tests in cosmological analyses. These tests include a study of the consistency between the constraints on cosmological parameters probed by the five tomographic bins of the Kilo-Degree Survey. Furthermore, I study the internal consistency of the ΛCDM model by dividing the model into regimes: one that describes the evolution of the isotropic background of the universe and one describing matter density perturbations. This model is constrained by cosmic shear, galaxy clustering, and cosmic microwave background measurements

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets