Assessment and Feedback for the CIP: learning technologies

This 11-minute video provides some guidance on the learning technologies available at Southampton that can be used to support assessment and feedback. It was produced using the Panopto lecture capture system. The first link is to the native Panopto podcast, which requires the Silverlight player to be installed. The second link is the MP4 video version of the podcast, which should play on all PCs, Macs and suitable mobile devices. This share also provides a link to the video's script.

Graph Theory and Social Networks Tutorial

For COMP60

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.