Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance

Liu, D; Richards, D; Froissard, C; Atif, A

Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance

Liu, D Richards, D Froissard, C Atif, A

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Publisher:: ASSOC INFORMATION SYSTEMS
Publication Type:: Conference Proceeding
Citation:: 2015 Americas Conference on Information Systems, AMCIS 2015, 2015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Liu, D
dc.contributor.author	Richards, D
dc.contributor.author	Froissard, C
dc.contributor.author	Atif, A https://orcid.org/0000-0003-0381-0343
dc.date	2015-08-13
dc.date.accessioned	2024-08-15T02:00:36Z
dc.date.available	2024-08-15T02:00:36Z
dc.date.issued	2015-01-01
dc.identifier.citation	2015 Americas Conference on Information Systems, AMCIS 2015, 2015
dc.identifier.uri	http://hdl.handle.net/10453/180417
dc.description.abstract	Given the focus on boosting retention rates and the potential benefits of pro-active and early identification of students who may require support, higher education institutions are looking at the data already captured in university systems to determine if they can be used to identify such students. This paper uses historical student data to validate an existing learning analytics tool, the Moodle Engagement Analytics Plugin (MEAP). We present data on the utility of the MEAP to identify students 'at risk' based on proxy measurements of online activity for three courses/units in three different disciplines. Our results suggest that there are real differences in the predictive power of the MEAP between different courses due to differences in the extent and structure of the learning activities captured in the learning management system.
dc.language	en
dc.publisher	ASSOC INFORMATION SYSTEMS
dc.relation.ispartof	2015 Americas Conference on Information Systems, AMCIS 2015
dc.relation.ispartof	21st Americas Conference on Information Systems (AMCIS)
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance
dc.type	Conference Proceeding
utslib.location.activity	PR, Fajardo
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-15T02:00:35Z
pubs.finish-date	2015-08-15
pubs.publication-status	Published
pubs.start-date	2015-08-13

Abstract:

Given the focus on boosting retention rates and the potential benefits of pro-active and early identification of students who may require support, higher education institutions are looking at the data already captured in university systems to determine if they can be used to identify such students. This paper uses historical student data to validate an existing learning analytics tool, the Moodle Engagement Analytics Plugin (MEAP). We present data on the utility of the MEAP to identify students 'at risk' based on proxy measurements of online activity for three courses/units in three different disciplines. Our results suggest that there are real differences in the predictive power of the MEAP between different courses due to differences in the extent and structure of the learning activities captured in the learning management system.

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http://hdl.handle.net/10453/180417