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A Framework for Interactive Exploratory Learning Analytics

  • Mohammad Javad MahzoonEmail author
  • Mary Lou Maher
  • Omar Eltayeby
  • Wenwen Dou
  • Kazjon Grace
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10925)

Abstract

Many analytic tools have been developed to discover knowledge from student data. However, the knowledge discovery process requires advanced analytical modelling skills, making it the province of data scientists. This impedes the ability of educational leaders, professors, and advisors to engage with the knowledge discovery process directly. As a result, it is challenging for analysis to take advantage of domain expertise, making its outcome often neither interesting nor useful. Usually the outcome produced from such analytic tools is static, preventing domain experts from exploring different hypotheses by changing data models or predictive models inside the tool. We have developed a framework for interactive and exploratory learning analytics which begins to address these challenges. We engaged in data exploration and hypotheses generation with our university domain experts by conducting two focus groups. We used the findings of these focus groups to validate our framework, arguing that it enables domain experts to explore the data, analysis and interpretation of student data to discover useful and interesting knowledge.

Keywords

Learning analytics Exploratory data analytics Educational data mining Learning analytics framework 

Notes

Acknowledgments

This research was supported by Charlotte Research Institute. We acknowledge Dr. Shannon Schlueter and Dr. Audrey Rorrer for their assistance in gaining access to the student data stored in the university databases. We had numerous discussions about our ideas for modelling student data with a broad range of faculty, of whom we especially thank Dr. Mohsen Dorodchi, Dr. Bojan Cukic, Dr. Xi (Sunshine) Niu, and Dr. Noseong Park.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Javad Mahzoon
    • 1
    Email author
  • Mary Lou Maher
    • 1
  • Omar Eltayeby
    • 1
  • Wenwen Dou
    • 1
  • Kazjon Grace
    • 2
  1. 1.University of North Carolina at CharlotteCharlotteUSA
  2. 2.The University of SydneySydneyAustralia

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