As part of EC-TEL 2017, the Twelfth European Conference On Technology Enhanced Learning with the theme of Data Driven Approaches in Digital Education, focusing on the new possibilities and challenges brought by the digital transformation of the education systems. We are thrilled to announce the first Multimodal Learning Analytics Across (Physical and Digital) Spaces (CrossMMLA) workshop. on Wednesday, September 13, 2017, in Tallinn.

See the detailed info, with the CfP, at http://crossmmla.org/ 


User Modeling and User-Adapted Interaction Special Issue on:

Multimodal Learning Analytics & Personalized Support Across Spaces


Learning often occurs in spaces and at moments that go beyond those shaped in formal educational settings. Students’ opportunities for significant learning are commonly not limited to the moments when they interact with a specific educational tool. By contrast, learning can be considered as a complex life-long journey which is socially, epistemically, physically and digitally situated. Increasing access to emerging communication technologies and the proliferation of mobile and pervasive devices have made it possible for students to have access to a wide range of educational (and non-educational) resources. Moreover, students commonly work outside the boundaries of the institutional learning system(s). They may interact face-to-face, use other educational tools or even use resources that were not specifically designed to serve in learning contexts. Instructors may also want students to not only use the tools offered by the institution, but also other tools that are more suitable for the context and the subject matter.

This proliferation of emerging communication technologies is creating new possibilities for providing automated, continued feedback and a more holistic view for supporting learning. Pervasive and mobile technologies can be used to allow learners to get remote access to educational resources from different physical spaces (e.g. ubiquitous/mobile learning) or to enrich their learning experiences in the classroom in ways that were not previously possible (e.g. face-to-face/blended learning).

However, to date most learning analytics and personalized support systems have been generally designed from a perspective which is agnostic of the physical space where learning happens or the various digital spaces through which learners interact. As a result, key challenges are emerging in regards of how systems can be adapted or how they can be used to provide support and/or feedback across different physical and digital spaces. Moreover, there are also technical challenges that need to be addressed to integrate and exploit learners’ data coming from heterogeneous, multimodal sources in order to provide such continued support or feedback.

In short, student’s learning activity happens where the learner is rather than within a specific educational system. Thus, there is an increasing interest in providing multimodal, personalized support or feedback to students across varied physical and digital spaces. For this special issue, we encourage submission of original papers that demonstrate ways to integrate and coordinate learning analytics and personalized feedback systems that provide continued support to learning across digital and/or physical spaces. Contributions can be made on any of the following themes:

  • Providing Personalized Support to Learners Interacting Across Multiple Digital Spaces: Studies of novel analytics approaches and systems providing personalized support or feedback across multiple digital learning tools. This includes data mining, learner modelling and/or visualization (or Open Learner Modelling) approaches applied to datasets that integrate logs from multiple learning tools;
  • Providing Personalized Support Bridging the Physical and Digital Realms: Design and study of learning situations that include collocated/face-to-face interaction and/or the use of online (remote access) tools (e.g. including ‘everyday’ settings, collocated collaboration situations, multi-device ecologies, ubiquitous learning technology or blended learning cases). This also includes classroom analytics, multimodal learning analytics (MMLA), and modelling collocated group interactions;
  • Generating Models of Learner’s Interaction Data from Heterogeneous Sources: Discussion of methodologies and theoretical approaches, and their technical solutions, to acquire learner models by integrating activity logs from multiple sources of student’s data. This includes technical approaches (such as conceptual models, formal representation of heterogeneous learner models or generic user modelling systems) but also non-technical issues (such as privacy and security of information for personalization, cultural adaptation, or data management).

 Paper Submission and Review Process  

Prospective authors must submit an extended abstract to the special issue editors via EasyChairIt must be at most 4 single-spaced pages long, not counting references, formatted with 12pt font and 1 inch margins. The special issue editors will screen all submitted abstracts. Abstracts that do not pass this initial screening (i.e., abstracts deemed not to have a reasonable chance of acceptance) will not be considered further. Authors of abstracts that pass the initial screening will be invited to submit a full version of the manuscript using the formatting guidelines and submission instructions of the journal, which can be consulted at http://www.umuai.org/paper_submission.html

Suggested Timeline

  • November 1, 2017: Submission of title and abstract
  • December 1, 2017: Notification of suitability of abstract
  • March 15, 2018: Submission of full papers
  • June 15, 2018: First round of review notifications
  • August 15, 2018: Revisions of papers due
  • November 1, 2018: Final notifications due
  • December 15, 2019: Camera ready papers due
  • February 15, 2019: Publication of special issue

Guest Editors

Roberto Martinez-Maldonado, University of Technology Sydney, NSW Australia Roberto.Martinez-Maldonado@uts.edu.au roberto.martinezmaldonado.net

Davinia Hernandez-Leo, Universitat Pompeu Fabra, Barcelona, Spain Davinia.Hernandez@upf.edu www.dtic.upf.edu/~daviniah/

Abelardo Pardo, The University of Sydney, NSW Australia, Abelardo.Pardo@sydney.edu.au http://sydney.edu.au/engineering/people/abelardo.pardo.php

I’m sharing here our slides of our paper about social learning spaces for MOOCs, with an analysis of a case in FutureLearn, presented in the Research track of #EMOOCs2017 Conference!

SLGridManathunga, K., Hernández-Leo, D., Sharples, M., (2017) A Social Learning Grid for MOOCs: Exploring a FutureLearn Case, Springer LNCS (vol. 10254) Proceedings of eMOOCs 2017, Madrid, Spain, 243-253. Open access: https://repositori.upf.edu/handle/10230/28273


Abstract. Collaborative and social engagement promote active learning through knowledge intensive interactions. Massive Open Online Courses (MOOCs) are dynamic and diversified learning spaces with varying factors like flexible time frames, student count, demographics requiring higher engagement and motivation to continue learning and for designers to implement novel pedagogies including collaborative learning activities.This paper looks into available and potential collaborative and social learning spaces within MOOCs and proposes a social learning space grid that can aid MOOC designers to implement such spaces, considering the relSLSG-examplesated requirements. Furthermore, it describes a MOOC case study incorporating three collaborative and social learning spaces and discusses challenges faced. Interesting lessons learned from the case give an insight on which spaces to be implemented and the scenarios and factors to be considered.

We also presented this work, link to slides, in the FLAN meeting (Barcelona, January 2017)

See information at: https://www.upf.edu/web/etic/phd-fellowship-in-educational-technologies

Special issue on
“Connecting Learning Design and Learning Analytics”

to be published at the
Interaction Design and Architecture(s) Journal (IxD&A)
CFP: http://ixdea.uniroma2.it/inevent/events/idea2010/index.php?s=102&link=call33
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 – extended till June 5, 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

Martinez-Maldonado, R., Goodyear, P., Carvalho, L., Thompson, K., Hernandez-Leo, D., Dimitriadis, Y., Prieto, L. P., and Wardak, D. (2017). Supporting Collaborative Design Activity in a Multi-User Digital Design Ecology. Computers in Human Behaviour, CHB, 71(June 2017), 327-342.
Open access: https://repositori.upf.edu/handle/10230/28165


Across a broad range of design professions, there has been extensive research on design practices and considerable progress in creating new computer-based systems that support design work. Our research is focused on educational/instructional design for students’ learning. In this sub-field, progress has been more limited. In particular, neither research nor systems development have paid much attention to the fact that design is becoming a more collaborative endeavor. This paper reports the latest research outcomes from R&D in the Educational Design Studio (EDS), a facility developed iteratively over four years to support and understand collaborative, real-time, co-present design work. The EDS serves to (i) enhance our scientific understanding of design processes and design cognition and (ii) provide insights into how designers’ work can be improved through appropriate technological support. In the study presented here, we introduced a complex, multi-user, digital design tool into the existing ecology of tools and resources available in the EDS. We analysed the activity of four pairs of ‘teacher-designers’ during a design task. We identified different behaviors – in reconfiguring the task, the working methods and toolset usage. Our data provide new insights about the affordances of different digital and analogue design surfaces used in the Studio.

We were delighted to host yesterday in UPF Barcelona the FutureLearn Academic Network meeting, with the theme “The Educator Experience”.  The meeting was co-organized by FutureLearn, the UPF Center for Learning Innovation & Knowledge (CLIK, directed by Manel Jiménez) and the Learning Technologies research team that I coordinate within the Interactive Technologies group at the UPF ICT Department.

flan-12The event started with an inspiring keynote by Professor Sir Timothy O’Shea, Principal & Vice-Chancellor, University of Edinburgh, who explained Edinburgh’s developing MOOC strategy, including producing 64 online Masters courses.

foto2.pngDr. Lisa Harris and Nic Fair explained how they are integrating MOOCs intoUniversity of Southampton practice from a perspective of education and research. foto3.png

Dr. Rebecca Ferguson, from The Open University, presented an very interesting analysis about what the research of FutureLearn’s UK partners tell us.


PhD Students from the Open University (ShiMing Chua,Tina Papathoma) and Universitat Pompeu Fabra (Kalpani Manathunga,Ishari Amarasinghe, Kostas Michos) presented their ongoing research around analyzing and enhancing social learning in MOOCs and how educators learn how to teach in MOOCs.

And Manel and I gave an overview of the MOOC research carried out at UPF essentially in the context of the RESET project and the DTIC Maria de Maetzu strategic program on Data-Driven Knowledge Extraction.

foto5There was also a Skype discussion with the participation of Ester Oliveras (UPF), Sarah Cornelius (University of Aberdeen), Sarah Speight (Nottingham), Pierre Binetruy (Paris Diderot) moderated by Mike Sharples about what have been the experiences of educators on FutureLearn courses, and how can these be improved.

All in all it was an enriching event, with interesting ideas and discussions about the role of MOOCs to achieve educational impact, to accelerate the educational technologies strategy within the institution, for educational research, and as research methodology. See #BarcelonaFLAN in twitter! And pictures in Flickr!