AFEL Workshop

AFEL 2018 – Call for Papers


To be held with the 13th European Conference on Technology Enhanced Learning (EC-TEL 2018), 3-6th September, Leeds, UK.


Workshop Date:

Tuesday, 4th September 2018

Key Dates

  • 1st July: Submission Deadline
  • 15th July: Notification of Acceptance
  • 19th August: Camera-Ready Papers
  • 24th August: Program Publication

Organizing Committee:

  • Mathieu d’Aquin – National University of Ireland Galway, Ireland
  • Stefan Dietze – L3S Research Center, Germany
  • Angela Fessl – Know-Center GmbH, Austria 
  • Peter Holtz – Leibnitz Insitut für Wissensmedien, Germany
  • Stefan Thalmann, – Graz University of Technology, Austria

Program Committee:

  • Alessandro Adamou – The Open University, Great Britain 
  • Carla Barreiros – Know-Center, Graz, Austria 
  • Dragan Gasevic – Monash University, Australia 
  • Jelena Jovanovic – University of Belgrade, Serbia 
  • Rene Kaiser – Know-Center, Graz, Austria 
  • Dominik Kowald – Know-Center, Graz, Austria 
  • Elisabeth Lex – Know-Center, Graz, Austria 
  • Tobias Ley – Tallinn University, Estland 
  • Viktoria Pammer-Schindler – Graz University of Technology, Austria
  • Allan Smeaton – Dublin College University, Ireland 
  • Ilaria Tiddi – The Open University, Great Britain 
  • Ran Yu – L3S Research Center, Germany 


Informal learning and workplace learning are established research fields in the domain of Technology enhanced learning (TEL) focusing primary on the business learning needs of employees. However, learning takes place everywhere and in everyday situations. Everyday learning as self-steered and curiosity-driven learning will become more and more important as part of life-long learning in future as knowledge and information develop rapidly. However, due to the rising complexity of available learning resources, support for self-organization and reflection about the own learning seems useful. In this regard learning analytics seems to offer promising approaches. This AFEL Workshop aims at bringing together researchers, practitioners, educational developers, entrepreneurs and policy makers from different backgrounds to provide a forum for discussion the multi-faceted area of analytics for everyday learning.

We are looking forward to contributions that feed the debate about learning analytics in the context of everyday learning on many levels. Thus, we are looking for contributions out of science, technology and practice to discuss learning analytics for everyday learning from different perspectives. Furthermore, participants are invited to submit innovative technologies that support learning analytics for everyday learning but also novel and advanced approaches based on artificial intelligence, augmented reality or ubiquitous computing technologies for learning. We are also highly appreciating papers on practices and different pedagogical approaches, types of learning settings, and application domains that can be used for everyday learning.

The workshop will include a paper session, a demo and prototype slam as well as an interactive session. The workshop aims at:

  • Providing a forum for presenting and discussing research on learning analytics for everyday learning.
  • Creating an interactive experience that connects participants’ research, current tools or latest prototypes and models with real end users’ learning experiences and requirements regarding analytics for everyday learning.
  • Creating an agenda for future everyday learning research and development.


Everyday learning becomes more and more important as learners, educators, knowledge workers, professionals etc. need to stay-up-to date for their daily learning and working activities. As technology evolves rapidly continuous everyday learning in fast changing environments will become a crucial part of the personal development. There exist different approaches on how this everyday learning can be supported. For example, learning analytics provides mechanisms for analyzing digital traces to support learners with regard to their learning goals, learning progress or learning strategies. Data-driven reflective learning is a learning strategy to re-evaluate past experiences with the goal to improve future behavior. Furthermore, there already exist manifold technologies and tools, that imitate everyday learning without recognizing it as „learning tool or technology“ like for example gamification approaches that motivate for learning like in language learning approaches (Duolingo), or tools that automatically give you an overview of your working or learning activities depending on the browser history, or tools that provide you guidance to improve your search behavior.

The main goal of this workshop is to illuminate everyday learning from different perspectives. Thus, the topics of interest include but are not limited to:

  • Theoretical discussion about everyday learning and related concepts (informal learning, workplace learning, reflective learning, collaborative learning, …) 
  • Conceptual discussion about learning analytics for everyday learning 
  • Methodologies to identify, study, and analyze everyday learning in different contexts (work, education, lifelong learning, continuing professional development, …) and to discuss the application areas for learning analytics
  • Empirical studies on analytics for everyday learning 
  • Technologies and tools for analytics for everyday learning 
  • Analytics for everyday learning in social context, knowledge, artefacts and processes and in specific contexts like higher education, work-place learning, learning organizations and networks 
  • Challenges, requirements and solutions for analytics for everyday learning in various contexts


  • Full papers: Description of novel theoretical, empirical or development work on alearning analytics in TEL, including a substantial contribution to the field (up to 15 pages). 
  • Work in progress: Ongoing research and current approaches on investigating the field, with initial insights for the community (up to 7 pages). 
  • Demos: Prototypes, design studies and tools for the support of learning analytics in TEL, which can be demoed and discussed (up to 3 pages). 

All contributions will be peer reviewed by at least two members of the programme committee evaluating their originality, significance, and rigour. The papers will be published in the CEUR workshop proceedings ( Submissions should use the Springer LNCS template


Please submit your paper via EasyChair: