Call For – Special Issue JLS

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: Questions about the special issue can be submitted to Taylor Martin ([email protected]) or Bruce Sherin ([email protected]). The final due date for submission is February 1, 2012. For more information, contact either of the guest editors.

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