Enabling the enablers: Using 'big data' to identify ways of improving student performance and engagement in the Tertiary Enabling Program at Charles Darwin University

The rapid growth of online learning and widespread use of learning management systems such as Blackboard and Moodle has resulted in increasingly large amounts of data being captured about students' digital interactions. Making use of this "big data” is an emerging field known as learning analytics (Fergusan, 2012) which is commonly defined as "...the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and environments in which it occurs” (Long et al., 2011). Such analyses can be as simple as plotting data on a graph (e.g. MacNeill & Elis 2013) to complex statistical technigues run by custom developed software packages (e.g. Course Signals at Purdue University; Arnold & Pistil, 2012). This paper discusses two examples of how data captured during use of Blackboard (CDU's online learning management system) by students enrolled in the Tertiary Enabling Program (TEP) at Charles Darwin University (CDU) is being used to investigate student performance and engagement, particularly for students studying externally online.

In the first example, we investigate the relationship between the academic performance of TEP students and the time of their initial engagement with Blackboard within individual units. Using a series of simple graphs it is possible to easily visualise correlations between time of engagement and academic results for different student cohorts such as internal and external students. The results are providing insights into when intervention should be providad to encourage TEP students to engage with thelr studies online to help maximise thelr chances of successfully completing units they are enrolled in.

In the second, more complex example, a novel approach is taken to analyse student engagement. Two biodiversity measures used to calculate extinction and origination rates of species as a function of time were utlised to calculate the rates
at which students disengaged and engaged with thelr online studies each week during a standard 15 week semester. This analysis is enabling us to better identify any particular point(s) during the semester that TEP students engage with their online studies and perhaps more importantly, particular points when TEP students disengage from their online studies.