AFEL - Analytics for Everyday Learning

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.

References

1. Mathieu d'Aquin, Alessandro Adamou, Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju, Ilire Hasani-Mavriqi, Peter Holtz, Joachim Kimmerle, Dominik Kowald, and Elisabeth Lex. 2017. AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning. In Proceedings of Proceedings of the 7th Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL) in conjunction with the 12th European Conference on Technology Enhanced Learning: Adaptive and Adaptable Learning (EC-TEL 2017).
2. Dominik Kowald, Simone Kopeinik, and Elisabeth Lex. 2017 a. The TagRec Framework As a Toolkit for the Development of Tag-Based Recommender Systems. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP '17). ACM, New York, NY, USA, 23--28.
3. Dominik Kowald and Elisabeth Lex. 2015. Evaluating tag recommender algorithms in real-world folksonomies: A comparative study Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 265--268.
4. Dominik Kowald, Subhash Chandra Pujari, and Elisabeth Lex. 2017 b. Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1401--1410.
5. Emanuel Lacic, Matthias Traub, Dominik Kowald, and Elisabeth Lex. 2017. ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture. In Proceedings of the Workshop on Large Scale Recommender Systems (LSRS'2015) co-located with the 9th ACM Conference on Recommender Systems (RecSys'2015).

Index Terms

• Information systems

  • Data management systems