AFEL-Analytics for Everyday Learning
d’Aquin, Mathieu Kowald, Dominik Fessl, Angela Lex, Elisabeth Thalmann, Stefan
d’Aquin, Mathieu
Kowald, Dominik
Fessl, Angela
Lex, Elisabeth
Thalmann, Stefan
Loading...
Repository DOI
Publication Date
2018-04-23
Keywords
Type
Conference Paper
Downloads
Citation
d'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.
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.
Publisher
ACM
Publisher DOI
10.1145/3184558.3186206
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland