Title: Can online student performance be forecasted by learning analytics?
Author: Kenneth David Strang
Address: School of Business and Economics, State University of New York, Plattsburgh, 640 Bay Road, Regional Higher Education Center, Queensbury, NY 12804, USA
Abstract: The paper focuses on utilising learning analytics to assess student performance in an online business. The value of this paper is in the literature review and the process used for analysis since it provides a way of how research in Moodle learning analytics or other similar systems could be leveraged to track student progress and for retention. The engagement analytics module is a relatively new component of Moodle but it has not yet been investigated with respect to student performance or in online business courses. The purpose of this study was to examine the predictive relationships between student academic performance and Moodle engagement analytics indicators along with student online activity data from the system logs. Unfortunately none of the hypothesised learning analytics factors were positively related to, nor could they predict, student academic performance. However, several interesting deductions from the learning analytics data gave rise to ideas for further research. Sense making of puzzling statistics suggested a mediating pattern of students' poor self-regulation skills because more focus was put on the assignment requirements but less on interacting with the lesson materials needed to complete the assignment and thereby resulting in lower grades.
Keywords: academic performance; big data analytics; ICT; higher education; Moodle engagement analytics; online learning; e-learning; electronic learning; business education; student performance; student progress; student retention; self-regulation skills; technology enhanced learning.
Int. J. of Technology Enhanced Learning, 2016 Vol.8, No.1, pp.26 - 47
Submission date: 13 Jun 2015
Date of acceptance: 02 Sep 2015
Available online: 18 Apr 2016