Overlapping Stochastic Community Finding

The 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), Beijing, China, 17-20 August 2014Community finding in social network analysis is the task of identifying groups of people within a larger population who are more likely to connect to each other than connect to others in the population. Much existing research has focussed on non-overlapping clustering. However, communities in real world social networks do overlap. This paper introduces a new community finding method based on overlapping clustering. A Bayesian statistical model is presented, and a Markov Chain Monte Carlo (MCMC) algorithm is presented and evaluated in comparison with two existing overlapping community finding methods that are applicable to large networks. We evaluate our algorithm on networks with thousands of nodes and tens of thousands of edges.Science Foundation Irelan

Digital Storytelling and key skills: problems and opportunities

Abstract. This paper presents a pilot study conducted at the University Ca’ Foscari – Venice, in Italy, in which a group of pre-service secondary school teachers explored the use of digital storytelling through workshops. The aim of this study was to determine the key skills that teachers employ in the production of DS. To this end, the study investigated in detail: the stages of Digital Storytelling (DS) perceived as difficult; the key skills that teachers are able to develop in their use of DS; the obstacles that may prevent the use of DS in schools. Although teachers have recognized the positive value of DS on the pedagogical and educational levels, the sample shows some resistance to using it at school, not so much due to the lack of technical competence, but for institutional reasons such as time constraints, access to technical equipment and curriculum demands