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dc.contributor.authord’Aquin, Mathieu
dc.contributor.authorKowald, Dominik
dc.contributor.authorFessl, Angela
dc.contributor.authorLex, Elisabeth
dc.contributor.authorThalmann, Stefan
dc.identifier.citationd'Aquin, Mathieu, Kowald, Dominik, Fessl, Angela, Lex, Elisabeth, & Thalmann, Stefan. (2018). AFEL - Analytics for Everyday Learning. Paper presented at the WWW ’18 Companion, The Web Conference, Lyon, France, April 23-27.en_IE
dc.description.abstractThe goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL 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) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and 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 is better supported; 4) evaluate all the former items in real world large scale applications and platforms.en_IE
dc.description.sponsorshipThe authors would like to thank the rest of the AFEL consortium. This work was supported by the Know-Center Graz, the Science Foundation Ireland (SFI) Insight Centre for Data Analytics and the European-funded project AFEL (GA687916). The Know-Center Graz is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).en_IE
dc.relation.ispartofCompanion of the The Web Conference 2018 on The Web Conference 2018en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectLearning analyticsen_IE
dc.subjectEveryday learningen_IE
dc.titleAFEL-Analytics for Everyday Learningen_IE
dc.typeConference Paperen_IE
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderHorizon 2020en_IE
dc.contributor.funderÖsterreichische Forschungsförderungsgesellschaften_IE
dc.local.contactMathieu D'Aquin. Email:
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/687916/EU/AFEL - Analytics For Everyday Learning/AFELen_IE

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