Learning Analytics: Identifying Points of Intervention for Student Support
Ken Mbuki (University of Leeds, UK)
Many Institutions in higher learning have been making extensive use of big data and analytics (Ferguson, 2012) in performing analytics on datasets created from tracking student learning activity (Wong, 2017) and other general student behaviours (Katrien Verbert et al, 2013) throughout different campus locations. Great insights like ‘highly-used-resources’ and ‘underutilized-resources’ have been discovered and therefore enhancing efficient resource allocation in the institutions. This data sets however, have not been fully utilized for academic purposes (Dietz-Uhler & Hurn, 2013). In this review we seek to answer the question, what data points can be used to identify potential cases of academic intervention (Wise, 2014.) using an individual student data in order to increase a student chance of academic success (Arnold & Pistilli, 2012)? We will then describe the lessons learned, from analysing a student data set collected from the University of Leeds entailing information from students track of lectures attendance, participation in online class discussions, access the class materials and library services. Finally, we shall provide a general guidance on nudging techniques that can be applied on the above identified data points to help improve an individual students academic performance in a university, through scripted or manual intervention strategies.