Learning Analytics Research: Using Meta-Review to Inform Meta-Synthesis

  • Xu Du
  • Juan Yang
  • Mingyan Zhang
  • Jui-Long Hung
  • Brett E. Shelton
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 880)


Research in learning analytics is proliferating as scholars continue to find better and more engaging ways to consider how data can help inform evidence-based decisions for learning and learning environments. With well over a thousand articles published in journals and conferences with respect to learning analytics, only a handful or articles exist that attempt to synthesize the research. Further, a meta-review of those articles reveals a lack of consistency in the scope of included studies, the confluence of educational data mining activities and “big data” as a parameter for inclusion, and the reporting of actual strategies and analytic methods used by the included studies. To fill these gaps within existing reviews of learning analytics research, this metasynthesis follows procedures outlined by Cooper to reveal developments of learning analytics research. The results include a number of metrics showing trends and types of learning analytic studies through 2017 that include which fields are publishing and to what extent, what methods and strategies are employed by these studies, and what domains remain largely yet unexplored.


Learning analytics Metasynthesis Educational data mining 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xu Du
    • 1
  • Juan Yang
    • 1
  • Mingyan Zhang
    • 1
  • Jui-Long Hung
    • 2
  • Brett E. Shelton
    • 2
  1. 1.National Engineering Research Center for E-LearningCentral China Normal UniversityWuhanChina
  2. 2.Boise State UniversityBoiseUSA

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