EdShare: collaboration in a University for learning and teaching

Poster for the School of Electronics and Computer Science, Learning Societies Lab Open Day, 27 February 2008 at the University of Southampton. Profile and presentation of the EdShare resource. The poster illustrates the philosophy of EdShare, how it relates to the Web 2.0 environment and its relationship to the education agenda in a University.

EdShare: collaboration in a University for learning and teaching

Poster for the Learning Societies Laboratory, School of Electronics and Computer Science, University of Southampton Open Day, Wednesday 27 February 2008.

EdShare: collaboration in a University for learning and teaching

Poster for the Learning Societies Laboratory, School of Electronics and Computer Science, University of Southampton Open Day, Wednesday 27 February 2008.

Scitable - Learn Science with Nature Education

Scitable is an open online teaching/learning portal combining high quality educational articles authored by editors at NPG with technology-based community features to fuel a global exchange of scientific insights, teaching practices, and study resources. Scitable currently contains articles in the field of genetics, and is intended for college undergraduate faculty and students. Future plans involve extension of Scitable to other fields within the life sciences, as well as to other audiences. Scitable brings together a library of scientific overviews with a worldwide community of scientists, researchers, teachers and students. Nature Education is a new division of Nature Publishing Group devoted to facilitating high quality, innovative, accessible science education in all countries of the world.

Session outline and learning outcomes - bed mobility

This resource is for Health Scientists

Session outline and learning outcomes - Hoists

This resource is for Health Scientist only

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

COMP6037 Seminar 23 10 13: Science, knowledge and technology

Set readings 1. Sismondo S. (2009). The Kuhnian revolution. In An introduction to science and technology studies. p12-22 2. Ben-David J, Sullivan T. (1975) Sociology of science. Annual Review of Sociology p203-21 3. Clarke A, Star SL. (2008) The social worlds framework: a theory/methods package. In Hackett EJ et al. The handbook of science and technology studies. Cambridge MA: MIT Press p113-137 Bonus paper (read if you have time) 4. Mitroff I. (1974). Norms and Counternorms in a Select Group of Apollo Moon Scientists. American Sociological Review 39:79-95 • Aim to ensure that you understand the core arguments of each paper • Look up/note any new terminology (and questions you want to ask) • Think about your critical appraisal of the paper (what are the merits/demerits of the argument, evidence etc) In the seminar we will spend about 5 minutes talking about each paper, and then - building on the two lectures - discuss how these ideas might be used to think about the Web and Web Science. At the end there will be some time for questions and a chance to note your key learning points.

MSc Web Science Programme Specification

Useful reference for learning outcomes

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.

Can you tell if they're learning?

The proliferation of Web-based learning objects makes finding and evaluating online resources problematic. While established Learning Analytics methods use Web interaction to evaluate learner engagement, there is uncertainty regarding the appropriateness of these measures. In this paper we propose a method for evaluating pedagogical activity in Web-based comments using a pedagogical framework, and present a preliminary study that assigns a Pedagogical Value (PV) to comments. This has value as it categorises discussion in terms of pedagogical activity rather than Web interaction. Results show that PV is distinct from typical interactional measures; there are negative or insignificant correlations with established Learning Analytics methods, but strong correlations with relevant linguistic indicators of learning, suggesting that the use of pedagogical frameworks may produce more accurate indicators than interaction analysis, and that linguistic rather than interaction analysis has the potential to automatically identify learning behaviour.

Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!