“To the Bat Car Robin!” The role of learning analytics in supporting, not just identifying students at risk

Abstract

This session explores the challenge of how we can use learning analytics to better manage services to support student transition into the first year of University. During the related session “Learning analytics to support students in the transition from secondary to higher education: which data to use and which feedback to give?” we will explore how institutions can use data to identify those students most at risk of early withdrawal or underperforming. This challenge workshop looks at the related topic: what happens once students at risk have been identified? For even if we can identify those students most at risk of failing months before they do so, if we are unable to improve our systems to support them, this new knowledge is of only limited use. There is some evidence that if students can be made aware that they are at risk of early withdrawal through learning analytics, they can change their behaviour to adopt more academically productive strategies. Both Arnold (2010) and Jayaprakash et al (2014) demonstrated that when students saw their engagement compared to their peers, they tended to raise their game. Arnold found that students in pilot studies using learning analytics tended to perform better in assessments than peers in control groups. However, the same knowledge also led other students choosing to withdraw early rather than risk failing an assessment. Studies into strategies for changing student behaviour have found that students can be highly resistant to encouragement and pressures upon them to change. Handley & Williams (2011) found the use of exemplars had little impact on student academic performance, as did Foster, McNeil & Lawther (2012) using early diagnostic testing and a variety of academic interventions and Hockings (2010) found resistance to change from a range of pedagogically sound interventions. However, we know from the experience of practitioners that we can have an impact on individuals and groups of students. We want to explore whether or not learning analytics can play a positive role in this process.status: publishe