AFEL - Analytics for Everyday Learning

Mathieu D'Aquin, Dominik Kowald, Angela Fessl, Elisabeth Lex, Stefan Thalmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The 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.

Original languageEnglish
Title of host publicationThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PublisherAssociation of Computing Machinery
Pages439-440
Number of pages2
ISBN (Electronic)9781450356404
DOIs
Publication statusPublished - 23 Apr 2018
Externally publishedYes
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: 23 Apr 201827 Apr 2018

Publication series

NameThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
CountryFrance
CityLyon
Period23/04/1827/04/18

Keywords

  • afel
  • everyday learning
  • h2020
  • learning analytics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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