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Time Series Analysis of VLE Activity Data

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Author(s)
MÅ‚ynarska, Ewa 
Greene, Derek 
Cunningham, Pádraig 
Uri
http://hdl.handle.net/10197/7866
Date Issued
02 July 2016
Date Available
05T16:12:23Z September 2016
Abstract
Virtual Learning Environments (VLE), such as Moodle, are purpose-built platforms in which teachers and students interact to exchange, review, and submit learning material and information. In this paper, we examine a complex VLE dataset from a large Irish university in an attempt to characterize student behavior with respect to deadlines and grades. We demonstrate that, by clustering activity profiles represented as time series using Dynamic Time Warping, we can uncover meaningful clusters of students exhibiting similar behaviors even in a sparsely-populated system. We use these clusters to identify distinct activity patterns among students, such as Procrastinators, Strugglers, and Experts. These patterns can provide us with an insight into the behavior of students, and ultimately help institutions to exploit deployed learning platforms so as to better structure their courses.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Keywords
  • Machine learning

  • Statistics

  • Virtual learning envi...

  • Moodle

Web versions
http://www.educationaldatamining.org/EDM2016/
Language
English
Status of Item
Peer reviewed
Description
9th International Conference on Educational Data Mining: EDM 2016, Raleigh, North Carolina, 29 June - 2 July 2016 United States
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
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Feb 6, 2023
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