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Time Series Analysis of VLE Activity Data

Author(s)
Młynarska, Ewa  
Greene, Derek  
Cunningham, Pádraig  
Uri
http://hdl.handle.net/10197/7866
Date Issued
2016-07-02
Date Available
2016-09-05T16:12:23Z
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
Subjects

Machine learning

Statistics

Virtual learning envi...

Moodle

Web versions
http://www.educationaldatamining.org/EDM2016/
Language
English
Status of Item
Peer reviewed
Conference Details
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/
File(s)
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Name

insight_publication.pdf

Size

157.28 KB

Format

Adobe PDF

Checksum (MD5)

768434cf72c00a4155e316bf5fbaab95

Owning collection
Insight Research Collection
Mapped collections
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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