|Role||Start date||End date||Funding source|
|Lead||01/Jan/2016||31/Dec/2018||EC (European Commission): FP(inc.Horizon2020, H2020, ERC)|
The goal of AFEL (Analytics for Everyday Learning) is to develop a multi-disciplinary approach to the analysis and understanding of informal/collective learning as it surfaces implicitly in online social environments. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours in order to improve their learning experience. There is an assumption in these approaches that data is available that explicitly relate to a learning activity. However, studies have for a long time demonstrated that learning activities happen, for a large part, outside of formal educational platforms. This includes informal, collective or incidental learning generally associated, as a side effect, to other (social) environments and activities. Supporting such a broader understanding of learning poses interesting technical and non-technical challenges. The aim of AFEL is to provide the technological grounding for such analyses to be achieved, as well as the cognitive models of learning and collaboration necessary to the understanding of such analyses of informal learning in social environments. This will be achieved through combining the variety of skills available in the consortium to a concrete set of online social environments, dedicated to informal learning or not. More precisely, we will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) social environments; 2) creating methods for the analysis of such informal learning data, based on combining visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it can be better supported.
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