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January 2016


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Vitomir Kovanovic <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Wed, 6 Jan 2016 01:05:24 +0000
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**Apologies for cross-postings***

Call for Papers (IEEE TLT)

IEEE Transactions on Learning Technologies,
Special issue on Learning Analytics

Guest editors: Dragan Gašević, Carolyn P. Rosé, and George Siemens

*** Submission deadline: February 1, 2016 ***

The availability of digital data affords unprecedented possibilities
for analysis of different aspects of human life. These possibilities
have mobilized researchers, practitioners, institutional leaders,
policy makers, and technology vendors to look at the ways these data
can be used to understand and enhance learning and teaching. This
intensive interest has given rise to the formation of the new field of
learning analytics. According to the Society for Learning Analytics
Research (SoLAR:, learning analytics is
defined as “the measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of understanding
and optimizing learning and the environments in which it occurs.” The
field of learning analytics builds on and links to numerous other
disciplines including learning sciences, social network analysis,
(educational) data mining, language technologies, machine learning,
information visualization, computational thinking and sense-making,
(educational, social, cognitive, organizational) psychology, and
educational theory.

Early results in the field of learning analytics offer much promise in
identification of learners at risk of failing or dropping out of a
course, understanding of information flow in social interactions, or
identification of different cognitive, metacognitive and affective
states in discourse and other behavior traces. These results have
attracted many institutions to invest in systemic implementation of
learning analytics, development of relevant institutional policies,
and creation of partnerships with organizations specializing in
learning analytics. Data about user interactions with learning
technologies are the main resources used in learning analytics. Given
the early days in the development of the field of learning analytics,
research is necessary to provide more effective methods in order to
understand the factors that influence learning as well as to optimize
learning outcomes and processes across cognitive, metacognitive,
social, and affective dimensions. Moreover, there is a need for
theoretically sound and empirically validated frameworks for:

- presentation of results of learning analytics to a variety of
   stakeholders, including decision makers, administrators, instructors
   and students,

- development of novel learning technologies offering effective ways
   for personalization support and continuous improvement,

- systemic institution-wide deployment and implementation of learning
   analytics, and

- ethical and safe use of data and learning analytics that protects
   user privacy while maximizing wellbeing.

This special issue of IEEE Transactions on Learning Technologies calls
for papers that address the above gaps by reporting on a combination
of theoretical/conceptual and empirically validated findings. The
accepted papers will contribute to the existing body of research
knowledge in the field of learning analytics and will offer a sound
empirical base that can motivate and inform practice. The submissions
that build bridges of learning analytics with other related
disciplines to enhance the impact are especially welcome.

Important Dates

- Full manuscripts due: February 1, 2016
- Completion of first review round: June 1, 2016
- Revised manuscripts due: July 31, 2016
- Final decision notification: October 31, 2016
- Publication materials due: November 30, 2016
- Publication of special issue: early 2017 (possibly the Jan-Mar 2017
   issue, i.e., vol. 10, no. 1)

Submission and Review Process

Full manuscripts should be prepared in accordance with the IEEE
Transactions on Learning Technologies guidelines
( and submitted via the
journal's ScholarOne portal (,
making sure to select the relevant special issue name.

Manuscripts should not be published or currently submitted for
publication elsewhere. Only full papers intended for review, not
abstracts, should be submitted via the ScholarOne portal. Each full
manuscript will be subjected to peer review.

## Guest Editors' Details

- Dragan Gašević
   Professor and Chair in Learning Analytics and Informatics,
   University of Edinburgh
   President, Society for Learning Analytics Research

- Carolyn Penstein Rosé
   Associate Professor, Carnegie Mellon University
   President, International Society of the Learning Sciences

- George Siemens
   Professor and Executive Director, University of Texas at Arlington
   Founding President, Society for Learning Analytics Research

Contact: [log in to unmask]

The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.

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