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Data-driven unsupervised clustering of online learner behaviour.

Robert L Peach1,2, Sophia N Yaliraki3, David Lefevre2

  • 11Department of Mathematics, Imperial College London, London, SW7 2AZ UK.

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|September 12, 2019
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Summary
This summary is machine-generated.

This study introduces a new mathematical framework to analyze online learner engagement patterns. The method identifies distinct learner groups, revealing that low-performing students often exhibit massed learning behaviors.

Keywords:
Education

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Area of Science:

  • Educational Technology
  • Data Science
  • Learning Analytics

Background:

  • Online learning adoption presents opportunities for analyzing learner behavior.
  • Optimizing web-based learning requires understanding temporal engagement patterns.

Purpose of the Study:

  • To introduce a mathematical framework for analyzing time-series of online learner engagement.
  • To identify clusters of learners with similar temporal behavior without predefined reference patterns.

Main Methods:

  • Utilized dynamic time warping kernel for pairwise time-series similarity.
  • Employed unsupervised multiscale graph clustering to group learners by temporal behavior.
  • Analyzed task completion data from an online postgraduate degree program.

Main Results:

  • Identified statistically distinct learner engagement clusters (distributed to massed learning).
  • Revealed outlier learners with sporadic engagement patterns.
  • Demonstrated that low performers were concentrated in the massed learning cluster and identified more accurately than traditional methods.

Conclusions:

  • The framework effectively clusters learners based on temporal engagement patterns.
  • Unsupervised clustering of online learner behavior can identify at-risk students.
  • The method is applicable across different datasets and institutions.