[EDUCATIONAL DATA] Understanding collective behavior of learning design communities
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
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Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
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Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
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UbicaLab |
Wireless Networking |
Educational Technologies |
[EDUCATIONAL DATA] Understanding collective behavior of learning design communities
http://doi.org/10.5281/zenodo.1207447
The following dataset has been used for the paper entitled "Understanding Collective Behavior of Learning Design Communities".
Michos, K., & Hernández-Leo, D. (2016). Understanding collective behavior of learning design communities. In Proceedings of the 11t European Conference on Technology Enhanced Learning, 614-617. https://doi.org/10.1007/978-3-319-45153-4_75
Abstract
Social computing enables collective actions and social interaction with rich exchange of information. In the context of educators’ networks where they create and share learning design artifacts, little is known about their collective behavior. Learning design tooling focuses on supporting educators (learning designers) in making explicit their design ideas and encourages the development of “learning design communities”. Building on social elements, this paper aims to identify the level of engagement and interactions in three communities using an Integrated Learning Design Environment (ILDE). The results show a relationship between the exploration of different artifacts and creation of content in all the three communities confirming that browsing influence the community's outcomes. Different patterns of interaction suggest specific impact of language and length of support for users.