Learning Analytics: From Big Data to Meaningful Data

Authors

  • Agathe Merceron Beuth University of Applied Sciences Berlin, Germany
  • Paulo Blikstein Stanford University
  • George Siemens University of Texas at Arlington, USA

DOI:

https://doi.org/10.18608/jla.2015.23.2

Abstract

This article introduces the special issue from the 2015 Learning Analytics and Knowledge conference. We describe the current state of the field, and identify some of the trends in recent research. As the field continues to expand,it seems that there are at least three directions of vigorous growth: the inclusion of multimodal data (gesture, eye-tracking, biosensors, etc.), the diversification of learning environments (MOOCs, classrooms, and hands-on learning environments.), and new types of research questions as researchers begin to consider a broader set of learning-related constructs (moving away, for example, from the focus on student retention.)

Author Biography

Paulo Blikstein, Stanford University

Graduate School of Education

Assistant Professor

References

Calvo, R., & D'Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. Affective Computing, IEEE Transactions on, 1(1), 18-37.

Kizilcec, R., Piech, C. & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. Proceedings of Third International Learning Analytics & Knowledge Conference: LAK13 (8-12 April), Leuven, Belgium, 170-179.

Skinner, B. (1968) The Technology of Teaching, New York: Appleton-Century-Crofts

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Published

2016-02-18

How to Cite

Merceron, A., Blikstein, P., & Siemens, G. (2016). Learning Analytics: From Big Data to Meaningful Data. Journal of Learning Analytics, 2(3), 4-8. https://doi.org/10.18608/jla.2015.23.2

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