Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.

Reading and research skills

University of Southampton, Dyslexia Services have developed a range of academic study skills resources available to download. This resource supports reading and research skills.

Educating an iPod generation: undergraduate attitudes, experiences and understanding of vodcast and podcast use

ognyline) There is an increasing pressure on university staff to provide ever more information and resources to students. This study investigated student opinions on (audio) podcasts and (video) vodcasts and how well they met requirements and aided learning processes. Two experiments within the Aston University looked at student opinion on, and usage of, podcasts and vodcasts for a selection of their psychology lectures.

Learning Objects, Interoperability and Standards

An introduction/overview to the whole issue of learning objects, interoperability and IMS specifications in Learning Technology

Research plan discussion: The socio-technical construction of MOOCs and educator practices in HE

In this seminar slot, we will discuss Steve's research aims and plan. Massive open online courses (MOOCs) have received substantial coverage in mainstream sources, academic media, and scholarly journals, both negative and positive. Numerous articles have addressed their potential impact on Higher Education systems in general, and some have highlighted problems with the instructional quality of MOOCs, and the lack of attention to research from online learning and distance education literature in MOOC design. However, few studies have looked at the relationship between social change and the construction of MOOCs within higher education, particularly in terms of educator and learning designer practices. This study aims to use the analytical strategy of Socio-Technical Interaction Networks (STIN) to explore the extent to which MOOCs are socially shaped and their relationship to educator and learning designer practices. The study involves a multi-site case study of 3 UK MOOC-producing universities and aims to capture an empirically based, nuanced understanding of the extent to which MOOCs are socially constructed in particular contexts, and the social implications of MOOCs, especially among educators and learning designers.