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AFEL – Analytics for Everyday Learning

Working groupKnowledge Construction Lab
Duration12/2015 - 11/2018
FundingEU research and innovation programme 'Horizon 2020' (687916)
Project description

The research project AFEL (Analytics for Everyday Learning) is funded by the EU research and innovation programme 'Horizon 2020' and aims at investigating the processes of learning and knowledge construction on the internet. Psychologists, psycholinguists, data scientists, and software engineers work together to develop tools to support these online learning processes.

The goal of the project is to develop methods and tools to understand informal/collective learning as it surfaces implicitly in online social environments. While Learning Analytics and Educational Data Mining traditionally rely on data from formal learning environments, studies have for a long time demonstrated that learning activities happen for a large part online, in a variety of other platforms. The aim of AFEL is therefore to devise the tools for exploiting learning analytics on such learning activities, in relation to cognitive models of learning and collaboration that are necessary to the understanding of loosely defined learning processes in online social environments.
To achieve this, AFEL gathers a range of skills in a consortium funded by the EU Horizon 2020 programme including experts in data analytics, interaction with data, cognitive models of learning and collaboration, as well as the developers of online social platforms. Concretely, the objectives of this consortium are to 1) develop the tools necessary to capture information about learning activities from online social environments; 2) create methods for the analysis of such informal learning data, based on combining visual analytics with cognitive models of learning and collaboration; and 3) demonstrate the potential of the approach in improving the understanding of informal learning, and the way it can be better supported. For example, recommender systems can be used to recommend usable learning materials for online-learners. Dynamic network visualizations can provide them with feedback on their learning contents and can help them to avoid acquiring biased knowledge.


Knowledge Media Institute KMI, The Open University in Milton Keynes (UK)

L3S, Leibniz University Hannover (DE)


KNOW Center, TU Graz (AT)


Project Website: AFEL


Yenikent, S., Holtz, P., Thalmann, S., d'Aquin, M., & Kimmerle, J. (in press). Evaluating the AFEL learning tools: Didactalia users’ experiences with personalized recommendations and interactive visualizations. 13th European Conference on Technology Enhanced Learning Heidelberg, Dordrecht, London, New York: Springer.

Holtz, P. Fetahu, B., & Kimmerle, J. (2018). Effects of contributor experience on the quality of health-related Wikipedia articles. Journal of Medical Internet Research, 20:e171. https://dx.doi.org/10.2196/jmir.9683

Yenikent, S., Buttliere, B., Fetahu, B., & Kimmerle, J. (2018). Wikipedia article measures in relation to content characteristics of lead sections. In S. Dietze, M. d'Aquin, D. Gasevic, E. Herder, & J. Kimmerle (Eds.), 7th International Workshop on Learning and Education with Web Data (#LILE2018) in conjunction with the 10th ACM Conference on Web Science (WebSci18) (pp. 5-8). Amsterdam, The Netherlands: Association for Computing Machinery.

Yenikent, S., Holtz, P., & Kimmerle, J. (2017). The impact of topic characteristics and threat on willingness to engage with Wikipedia articles: Insights from laboratory experiments. Frontiers in Psychology, 8:1960. https://dx.doi.org/10.3389/fpsyg.2017.01960