Time Series Clustering of Moodle Activity Data

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Title: Time Series Clustering of Moodle Activity Data
Authors: Młynarska, Ewa
Greene, Derek
Cunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/8338
Date: 21-Sep-2016
Online since: 2017-02-13T15:17:58Z
Abstract: Modern computer systems generate large volumes of log data as a matter of course and the analysis of this log data is seen as one of the most promising opportunities in big data analytics. Moodle is a Virtual Learning Environment (VLEs) used extensively in third level education that captures a significant amount of log data on student activity. In this paper we present an analysis of Moodle data that reveals interesting differences in student work patterns. We demonstrate that, by clustering activity profiles represented as time series using Dynamic Time Warping, we can uncover meaningful clusters of students exhibiting similar behaviours. We use these clusters to identify distinct activity patterns among students, such as Procrastinators, Strugglers, and Experts. We see educators as the potential users of a tool that might result from this research and our preliminary analysis does identify scenarios where interventions should be made to help struggling students.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Keywords: Machine learningStatisticsVirtual learning environments (VLE)
Other versions: http://aics2016.ucd.ie/
Language: en
Status of Item: Peer reviewed
Conference Details: 24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), University College Dublin, Dublin, Ireland, 20-21 September 2016
Appears in Collections:Insight Research Collection

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