Good Practice in the use of PowerPoint

This presentation aims to encourage academic staff to make better use of PowerPoint by avoiding dependence on or abuse of bullet points. It highlights PowerPoint's emergent properties and presents clear guidelines for effective slides

New Brand PowerPoint template - good practice advice

This short video introduces the new teaching templates and provides advice about good slide design

Blackboard Good Practice Guide

This short (4 sides of A4) document provides advice to tutors about essential and recomended practices, organisational principles, blended learning, accessibility and copyright.

Faculty Good Practice - Event

Slides and link to Panopto recording about 'good practice' within from across the Faculty Adam Procter - Winchester School of Art. Liz Williams - Southampton Law School. Jean Leah - School of Management.

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.