Abstract. In this paper we first investigate how eTwinning and national and local teachers' professional development schemes interact. eTwinning is the community for schools in Europe that promotes teacher and school collaboration through the use of Information and Communication Technologies (ICT) under the European Union's Lifelong Learning Programme. The eTwinning Portal hosts more than 137,000 teachers who interact with each other on the European-scale. Second, using this authentic data, we discuss how novel research methods such as Social Network Analysis, information visualisation techniques and future scenario forecasting are used to study eTwinning in the Tellnet-project aiming to sustain and support dynamic teacher networks as a platform for formal and informal teachers' professional development in the future.
Abstract-When Internet users search for information, surf on websites or discuss with others, their actions are driven by certain goals. Extraction of users' goals can enable higher effectiveness and accuracy of web services. Supporting users in based on their goals can be highly beneficial, especially supporting of learners in the preparation for an exam as a learning process, Different phases of learning are identified when users learn collaboratively. We scrutinize how goals are constructed and achieved within a community, examining not only social activities based on patterns of behavior, but also emotions and intents users express in their posts. As a result we elicit users' goals. We achieved good accuracy in defining emotions of users and recognizing their intents and social patterns in our case. Here we discuss how the obtained results contribute to mining of learning community goals.
Smart communities provide technologies for monitoring social behaviors inside communities. The technologies that support knowledge building should consider the cultural background of community members. The studies of the influence of the culture on knowledge building is limited. Just a few works consider digital traces of individuals that they explain using cultural values and beliefs. In this work, we analyze 13 Wikipedia instances where users with different cultural background build knowledge in different ways. We compare edits of users. Using social network analysis we build and analyze co-authorship networks and watch the networks evolution. We explain the differences we have found using Hofstede dimensions and Schwartz cultural values and discuss implications for the design of smart community technologies. Our findings provide insights in requirements for technologies used for smart communities in different cultures.
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