Investigating the use of Virtual Learning Environments by teachers in schools and colleges

Investigating the use of Virtual Learning Environments by teachers in schools and colleges

Institutional Personal Learning Environments – Paradise or Paradox? A Digital Literacies Perspective

This was my keynote presentation at Computer Supported Education (CSEDU) 2012, in Porto. It looks at the importance of digital literacies and how VLEs do not support their developmeng and looks at iPLEs as an alternative.

Values and Affordances of VLEs

This PowerPoint describes the growth of online learning from early hand-crafted solutions, through 'virtual learning environments' to today's 'managed learning environments'. It also looks at the emergence of the 'personal learning environment' concept.

University of Southampton Module Evaluation Form

This is the full Module Evaluation Form adopted by the University of Southampton. The latest editable file can be downloaded from the Learning and Teaching Enhancement Unit (LATEU) of the University. Included in this resource is the online version of the form for use in Blackboard, WebCT and other virtual learning environments. If you are using Blackboard, you are advised to use Internet Explorer version 6 or higher. Save the Blackboard zip archive to a local drive. Do not rename the file name. Go to the destination course area in Blackboard, open the "Control Panel" and then start the "Survey Manager" (in the "Assessment" group). Use the "Import" command to upload the zip archive. Once this is completed, rename the evaluation form which can then be added to any content area within the course using the dropdown "Add Survey" command.

Lecture on VLEs and MLEs

Powerpoint Lecture notes on Virtual Learning Environments and Managed Learning Environements

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.