Tools for Educational Data Mining

Stefan Slater, Srećko Joksimović, Vitomir Kovanovic, Ryan S. Baker and Dragan Gasevic
Additional contact information
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

Journal of Educational and Behavioral Statistics, 2017, vol. 42, issue 1, 85-106

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.

Keywords: software; modeling; data management; text mining; data analysis; big data; data cleaning (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:42:y:2017:i:1:p:85-106

DOI: 10.3102/1076998616666808

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