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.

Using online peer assessment tools to provide feedback and promote learning

A set of slides used for the RAP SIG event on 19 Jan 2017

Work less, teach better and get better feedback: why we tried innovative teaching methods

Speaker: Patrick McSweeney Organiser: Time: 15/10/2014 11:00-11:45 Location: B32/3077 Abstract Having started at Southampton in 2005 I have seen quite a few changes to the way courses are taught and studied. I will reflect on some of the interesting changes I have observed and suggest their causes. As a practical example I will talk about codestrom, a peer feedback tool for learning programming. We have found that this teaching method has improved the student experience and reduced the work load for the module team. Together we will discuss how this and other recent developments can enable other teaching innovations which benefit staff as well as students. Hopefully the new class of PhD students will be able to contribute from the point of view of having recently been undergraduate students here and else where.