*** 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:
• 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
• 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
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