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Tools for Educational Data Mining

Author

Listed:
  • Stefan Slater

    (Columbia University)

  • Srećko Joksimović

    (Moray House School of Education The University of Edinburgh)

  • Vitomir Kovanovic

    (School of Informatics The University of Edinburgh)

  • Ryan S. Baker

    (Columbia University)

  • Dragan Gasevic

    (Moray House School of Education and School of Informatics The University of Edinburgh)

Abstract

In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.

Suggested Citation

  • Stefan Slater & Srećko Joksimović & Vitomir Kovanovic & Ryan S. Baker & Dragan Gasevic, 2017. "Tools for Educational Data Mining," Journal of Educational and Behavioral Statistics, , vol. 42(1), pages 85-106, February.
    • Handle: RePEc:sae:jedbes:v:42:y:2017:i:1:p:85-106
    DOI: 10.3102/1076998616666808
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    Cited by:

    1. Fernandes, Eduardo & Holanda, Maristela & Victorino, Marcio & Borges, Vinicius & Carvalho, Rommel & Erven, Gustavo Van, 2019. "Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil," Journal of Business Research, Elsevier, vol. 94(C), pages 335-343.

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