The Legacy and the Future of Research Networks in Technology-Enhanced Learning

These are the slides of my presentation at the Orphée Alpine Rendevous in Font Romeu on February 1, 2017. I was asked to participate in the meeting as an European expert in the field of technology enhanced learning. The participants worked on grand challenges in TEL the other days. In his presentation, Mike Sharples put the grand challenges in an European and international context. He presented a lot of reports, mainly from US or UK institutions about the challenges in TEL. Fridolin Wild, an expert in the fabrication of grand challenges from the STELLAR days reflected on the challenges. His presentation was about our joint WEKIT project on augmented reality and wearable technology industrial training. The WEKIT project has reached its second year and we are starting to develop our technology platform to support technology developers and training providers in AR and WELL (wearable enhanced learning).
After the presentations we had two panels. The first was to give feedback on the grand challenges and the second was to explore future funding opportunities. Here is a challenge, too, since the according EU unit has announced that there will be no TEL specific H2020 call in 2017.


Special Issue on "Connecting Learning Design and Learning Analytics" – IxD&A Journal

Special issue on
“Connecting Learning Design and Learning Analytics”

to be published at the
Interaction Design and Architecture(s) Journal (IxD&A)
(ISSN 1826-9745, eISSN 2283-2998)

*** Since 2012 also in Scopus ***
*** Since 2015 also in Emerging Sources Citation Index and Web of Science ***
IxD&A implements the Gold Open Access (OA) road to its contents
with no charge to the authors (submission & paper processing)

If you wish to help us in improving the quality of the journal, please donate:
Guest Editors:
• Davinia Hernández-Leo, Universitat Pompeu Fabra Barcelona
• María Jesús Rodríguez-Triana, École Polytechnique Fédérale of Lausanne
• Yishay Mor, independent consultant
• Paul Salvador Inventado, Carnegie Mellon University

Important dates:
• Deadline: May 20, 2017
• Notification to the authors: June 30, 2017
• Camera ready paper: July 30, 2017
• Publication of the special issue: end of September, 2017

Learning Design (LD) and Learning Analytics (LA) are both domains of research and action that aim to improve learning effectiveness.

Learning Design or, Design for Learning, is an emerging field of educational research and practice. Its practitioners are interested in understanding how the intuitive processes undertaken by teachers and trainers can be made visible, shared, exposed to scrutiny, and consequently made more effective and efficient. Arguably, most of the work in the field of LD has focused on the creative processes, on practices, tools and representations to support it, and on mechanisms for sharing its outputs between practitioners. Very little has been done in terms of the practices, tools and representations used for evaluating the effects of the designs. Several approaches emphasise top-down quality enhancement, which help designers to base their work on sound pedagogical principles. What is missing is the trajectory that would complete the feedback loop: the built-in evaluation of designs to see whether they achieved the expected outcomes.

Learning Analytics are about collecting and reporting data about learners and their contexts, for purposes of understanding and optimising learning environments. LA typically employ large datasets to provide real-time or retrospective insights about the effect and effectiveness of various elements and features of learning environments. Learning analytics are rooted in data science, artificial intelligence, and practices of recommender systems, online marketing and business intelligence. The tools and techniques developed in these domains make it possible to identify trends and patterns, and then benchmark individuals or groups against these trends. LA can help to identify at-risk learners and provide interventions, transform pedagogical approaches, and help students gain insight into their own learning.

How Learning Design may help Learning Analytics? According to situational approaches, one of the prerequisites to obtain relevant outputs is not to isolate the analysis of educational data from the context in which it is embedded. This tandem between LD and LA offers the opportunity to better understand student behaviour and provide pedagogical recommendations when deviations from the original pedagogical intention emerge addressing one of the challenges posed by LA.

How Learning Analytics may support Learning Design? Reciprocally, well-formulated learning analytics can be helpful to inform teachers on the success and outcomes of their learning designs. Learning analytics can provide evidences of the impact of a design in one or several learning situations in aspects such as engagement patterns in the activities proposed by the learning design, learning paths followed by the students, time consumed to complete the activities, etc.

To sum up, LD offers LA a domain vocabulary, representing the elements of a learning system to which analytics can be applied. LA in turn, offers LD a higher degree of rigor by validating or refuting assumptions about the effects of various designs in diverse contexts. There is a natural and synergistic relationship between both domains, which has led to a growing interest and some initial effort in bringing them together. However, making these links operational and coherent is still an open challenge.

Topics of Interest
This special issue solicits original research papers framing connecting learning design with learning analytics.
The main topics of interest are:

● Practical examples of synergies between LD and LA.
● Methods and tools for developing data-enriched learning design and / or design-aware learning analytics.
● Application domains for integrated LD-LA approaches, such as teacher inquiry, learning at scale, and self-determined learning.
● Theoretical and conceptual foundations, opportunities and challenges for synergies between LD and LA.
● Meta-models and mediating frameworks for connecting and correlating LD and LA.
● Utilising Design Patterns as such meta-models, and as boundary objects for all of the above.

Submission guidelines and procedure
All submissions (abstracts and later final manuscripts) must be original and may not be under review by another publication.
The manuscripts should be submitted either in .doc or in .rtf format.
All papers will be blindly peer-reviewed by at least two reviewers.
Authors are invited to submit 8-20 pages paper (including authors’ information, abstract, all tables, figures, references, etc.).
The paper should be written according to the IxD&A authors’ guidelines

Authors’ guidelines

Link to the paper submission page:
(Please upload all submissions using the Submission page. When submitting the paper, please, choose Domain Subjects under:
“IxD&A special issue on: ‘Connecting Learning Design with Learning Analytics’)

More information on the submission procedure and on the characteristics
of the paper format can be found on the website of the IxD&A Journal
where information on the copyright policy and responsibility of authors,
publication ethics and malpractice are published.

For scientific advice and queries, please contact any of the guest-editors below and mark the subject as:
IxD&A special issue on: Connecting Learning Design with Learning Analytics.

• davinia [dot] hernandez [at] upf [dot] edu
• maria [dot] rodrigueztriana [at] epfl [dot] ch
• yishaym [at] gmail [dot] com
• pinventado [at] cmu [dot] edu


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Special Track on Wearable-technology Enhanced Learning (WELL) @ iLRN 2017


Special Track on Wearable-technology Enhanced Learning (WELL)
at the Immersive Learning Research Network Conference iLRN 2017
June 26-29, 2017, Coimbra, Portugal

Topic of the Special Track

Wearable technologies – such as smart watches, smart glasses, smart objects, smart earbuds, or smart garments – are just starting to transform immersive user experience into formal education and learning at the workplace. These devices are body-worn, equipped with sensors and conveniently integrate into leisure and work-related activities including physical movements of their users.
Wearable-technology Enhanced Learning (WELL) is beginning to emerge as a new discipline in technology enhanced learning in combination with other relevant trends like the transformation of classrooms, new mobility concepts, and cyber-physical systems. Wearable devices play an integral role in the digital transformation of industrial and logistics processes in the Industry 4.0 and thus demand new learning and training concepts like experience capturing, re-enactment and smart human-computer interaction.
This proposal of a special track is the offspring of the SIG WELL ( in the context of the European Association for Technology Enhanced Learning (EATEL). It is a follow up proposal for the inaugural session we had at the iLrn 2015 in Prague. In the meantime, the SIG was successful in organizing a number of similar events at major research conferences and business oriented fairs like the EC-TEL, the I-KNOW and the Online Educa Berlin OEB. Moreover, the SIG has involved in securing substantial research funds through the H2020 project WEKIT ( The SIG would like to use the opportunity to present itself as a platform for scientific and industrial knowledge exchange. EATEL and major EU research projects and networks in the field support it. Moreover, we’ll seek to attach an IEEE standard association community meeting of the working group on Augmented Reality Learning Experience Models (IEEE ARLEM).

List of Topics
    Industry 4.0 and wearable enhanced learning
    Learning Analytics for Wearable technologies
    Wearable technologies for health and fitness
    Wearable technologies and affective computing
    TEL applications of smart glasses, watches, armbands
    Learning context and activity recognition for wearable enhanced learning
    Body-area learning networks with wearable technologies
    Data collection from wearables
    Feedback from wearables
    Learning designs with wearable technologies
    Augmented Reality Learning
    Ad hoc learning with wearables
    Micro learning with wearables
    Security and privacy for wearable technology enhanced learning
    Collaborative wearable technology enhanced learning


Paper submission: February 1, 2017
Notification of authors: March 15, 2017
Full paper submission: April 15, 2017
Date of the conference: June 26-29, 2017

Contributing papers have to undergo a peer review process and will be included
in the conference proceedings, depending on the overall quality and special tracks chairs’ decision, either as a long paper (10 – 12 pages) or as a short paper (6 -8 pages). Excellent papers will be deemed full papers (14 pages) and included in the Springer proceedings.  Authors of selected papers will also be invited to extend their contribution and to be published in a special issue of the JCR-indexed Journal of Universal Computer Science.

Track chairs

Ilona Buchem, Beuth University of Applied Sciences Berlin, Germany
Ralf Klamma, RWTH Aachen University, Germany,
István Koren, RWTH Aachen University, Germany
Fridolin Wild, Oxford Brookes University, UK
Alla Vovk, Oxford Brookes University, UK

Tentative Program Committee (t.b.c.)
Mario Aehnelt, Fraunhofer IGD Rostock, Germany
Davinia Hernández-Leo, Universitat Pompeu Fabra, Spain
Carlos Delgado Kloos, UC3M, Spain
Elisabetta Parodi, Lattanzio Learning Spa, Italy
Carlo Vizzi, Altec, Italy
Mar Perez Sangustin, Pontificia Universidad Católica de Chile, Chile
Isa Jahnke, University of Missouri-Columbia, USA
Mikhail Fominykh, Europlan, UK
Puneet Sharma, University of Tromso, Norway
Yishay Mor, Levinsky College of Education, Israel
Tobias Ley, Tallinn University, Estonia
Peter Scott, Sydney University of Technology, Australia
Victor Alvarez, Murdoch University, Australia
Agnes Kukulska-Hulme, The Open University, UK
Carl Smith, Ravensbourne University, UK
Victoria Pammer-Schindler, Graz University of Technology &Know-Center Graz, Austria
Christoph Igel, CeLTech, Germany
Peter Mörtel, Virtual Vehicle, Austria
Brenda Bannan, George Mason University, USA
Christine Perey, Perey Consulting, Switzerland
Kaj Helin, VTT, Finland
Jana Pejoska, Aalto, Finland
Jaakko Karjalainen, VTT, Finland
Joris Klerxx, KU Leuven, Belgium
Marcus Specht, Open University, Netherlands
Roland Klemke, Open University, Netherlands
Will Guest, Oxford Brookes University, UK


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Joint Special Issue of IEEE TETC and TLT on "Innovation in Technologies for Educational Computing"

*** Call for Papers ***
Joint Special Issue of IEEE TETC and TLT on “Innovation in Technologies for
Educational Computing”

IEEE Transactions on Emerging Topics in Computing (TETC) IEEE Transactions
on Learning Technologies (TLT)


*** Guest Editors ***

Fabrizio Lamberti, Politecnico di Torino, Italy Gwo-Jen Hwang, National
Taiwan University of Science and Technology, Taiwan Baltasar Fernández,
Universidad Complutense de Madrid, Spain Wenping Wang, The University of
Hong Kong, Hong Kong

*** Topics of interest ***

The goal of this joint Special Issue is to provide an overview of most
recent emerging and “fringe” learning technologies. Sample topics of
interest include:

1. Virtual, augmented and mixed reality: game-based learning, edutainment,
gamification, intrinsic integration of game mechanics, virtual worlds and
communities for education and training, digital humans and avatars in
education, work-based and augmented learning

2. Learning at Scale: service-oriented architectures for learning, MOOCs,
interoperability of learning systems, standards for knowledge sharing, open
educational resources, linked open data, ontologies and reasoning

3. Ubiquitous and wearable computing: educational applications of
sensor-based learning, smart watches and activity trackers, body sensor
networks, ego-vision devices and life-logging equipment, Internet of Things,
smart environments, context-aware services and tools

4. Social computing: educational data mining and filtering on social
networks, social media for e-learning and e-assessment, social learning at
scale, informal learning in social communities, peer review and assessment,
trust and reputation in social communities

5. Big Data and data analytics: modeling of learners and learning processes,
learning analytics, educational data mining, student profiling, behavioral
and emotional analytics, learning data visualization, quantified self

6. Intelligent systems: adaptive learning, recommender systems, tools for
smart tutoring and training, pedagogical agents and assistants, course and
material personalization

7. Learning in the making: 3D printers and computer-controlled fabrication
devices, open micro-controller, sensor and actuator technologies, smart
programming environments, robotics

8. Human-computer interaction: natural and multi-modal interfaces,
conversational agents, affective computing, interactive tabletops and
surfaces, innovative interaction devices and techniques

*** Submission instructions ***

This partial list is not exclusive and does not cover all novel learning
technologies. As a part of the submission letter the authors are requested
to argue why the technology presented in the submission should be counted as
“emergent” and “cutting edge”.

Submitted papers must describe original research which is not published nor
currently under review by other journals or conferences. Authors are
responsible for understanding and adhering to submission guidelines
published on the IEEE Computer Society website (

Authors are invited to submit manuscripts focused on odd labeled topics
directly to Transactions on Emerging Topics in Computing (TETC) at and papers focused on even labeled
topics directly to Transactions on Learning Technologies (TLT) at

In special cases, authors are welcome to submit to the journal of their
choice. Although with the above choice the authors are indicating which
Transaction represents the primary target of their submission, they should
be aware that papers may be published in TETC or TLT depending on the
availability of space with the final allocation at the discretion of the
Editor-in-Chief of the respective Transactions.

Correspondence should be addressed to:

*** Important dates ***

– Submission Deadline: December 1, 2016
– Reviews Completed: March 1, 2017
– Major Revisions Due (if Needed): April 1, 2017
– Reviews of Revisions Completed (if Needed): May 1, 2017
– Minor Revisions Due (if Needed): June 1, 2017
– Notification of Final Acceptance: July 1, 2017
– Publication Materials for Final Manuscripts Due: August 1, 2017
– Publication date: October-December Issues of 2017


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