Call for Papers Technological Advances in the Study of Learning:
Learning Analytics and Computational Techniques for Detecting and
Evaluating Patterns in Learning

A Special Issue of the Journal of the Learning Sciences
Guest editors: Taylor Martin, University of Texas at Austin
Bruce Sherin, Northwestern University

Throughout its history, the Journal of the Learning Sciences has focused
on the potential of technology to transform the educational experience
of students. New advances in technology now have the potential to
transform how we study learning. These advances are occurring along two
fronts. First, technological advances have dramatically altered the
types of learning data that is available. When students conduct work on
computers and online, they leave a trace of their learning behavior
that, at least in principle, can be mined. The extent and growth of this
data is without precedent. Second, technological advances now make it
possible for researchers to conduct new types of analysis. Individual
researchers have, on their desks, computers that only a decade or two
ago would have been called supercomputers. In addition, advances in
fields such as computational linguistics and computer science have led
to the development of entirely new methods for discovering and
describing patterns in data.

The editors of this special issue are seeking papers that focus on the
use of advanced computational techniques to examine learning in complex
learning environments in ways that were heretofore impossible. Some of
these techniques include network analysis, data mining, natural language
processing, and machine learning. These advanced computational
techniques can not only aid in the design of systems with better, more
immediate feedback, but are also a novel lens to investigate human
cognition itself, finding patterns in massive datasets that can often be
quite difficult to detect. We are seeking papers presenting empirical
work conducted with a variety of age groups and in a range of content
areas (including those not in STEM education). Together, the papers will
illustrate how advanced computational techniques allow us to make
headway in understanding learning in multiple contexts.

All papers should be written for a sophisticated audience with expertise
in research on learning. However, because the focus of the issue is on
advancing new methods, the novel methods that are used should be
described so that they can be understood by readers who only have
familiarity with more traditional methods.

Manuscripts should be submitted to the Journal of the Learning Sciences
submission site: http://www.tandfonline.com/hlns. Question about the
special issue can be submitted to Taylor Martin
(taylormartin@mail.utexas.edu) or Bruce Sherin
(bsherin@northwestern.edu). The final due date for submission is
February 1, 2012. For more information, contact either of the guest editors.