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

Danny Liu, Deborah Richards, Chris Froissard, Amara Atif

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2015 Americas Conference on Information Systems, AMCIS 2015
Place of PublicationPuerto Rico
PublisherAmericas Conference on Information Systems
Pages1-10
Number of pages10
ISBN (Electronic)9780996683104
Publication statusPublished - 2015
Event21st Americas Conference on Information Systems, AMCIS 2015 - Fajardo, Puerto Rico
Duration: 13 Aug 201515 Aug 2015

Other

Other21st Americas Conference on Information Systems, AMCIS 2015
CountryPuerto Rico
CityFajardo
Period13/08/1515/08/15

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  • Cite this

    Liu, D., Richards, D., Froissard, C., & Atif, A. (2015). Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance. In 2015 Americas Conference on Information Systems, AMCIS 2015 (pp. 1-10). Puerto Rico: Americas Conference on Information Systems.