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Using cluster analysis for data mining in educational technology research

Educational Technology Research and Development volume 60, pages 383–398 (2012)Cite this article

Abstract

Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through two examples of mining click-stream server-log data that reflects student use of online learning environments. Cluster analysis can be used to help researchers develop profiles that are grounded in learner activity—like sequence for accessing tasks and information, or time spent engaged in a given activity or examining resources—during a learning session. The examples in this paper illustrate the use of a hierarchical clustering method (Ward’s clustering) and a non-hierarchical clustering method (k-Means clustering) to analyze characteristics of learning behavior while learners engage in a problem-solving activity in an online learning environment. A discussion of advantages and limitations of using cluster analysis as a data mining technique in educational technology research concludes the article.

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Authors and Affiliations

  1. Oklahoma State University, 210 Willard Hall, Stillwater, OK, 74078, USA

    Pavlo D. Antonenko

  2. Children’s Mercy Hospital & University of Missouri, Kansas City, 2401 Gillham Road, Kansas City, MO, 64108, USA

    Serkan Toy

  3. Iowa State University, N155 Lagomarcino Hall, Ames, IA, 50011, USA

    Dale S. Niederhauser

Authors
  1. Pavlo D. Antonenko
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  2. Serkan Toy
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  3. Dale S. Niederhauser
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Correspondence to Pavlo D. Antonenko.

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Antonenko, P.D., Toy, S. & Niederhauser, D.S. Using cluster analysis for data mining in educational technology research. Education Tech Research Dev 60, 383–398 (2012). https://doi.org/10.1007/s11423-012-9235-8

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  • DOI: https://doi.org/10.1007/s11423-012-9235-8

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