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The effectiveness of learning analytics for identifying at risk students in higher education

Nottingham Trent University uses learning analytics data via the student engagement platform StREAM to identify at-risk students.

Non-engagement alerts are used to notify staff of any student who has not actively participated in their learning sources for 14 consecutive days. Ed Foster and Rebecca Siddle from the Centre for Student & Community Engagement at the university investigated the relationship between these alerts and student outcomes and presented the findings in the article The effectiveness of learning analytics for identifying at-risk students in higher education.

During the study, they compared the use of alerts to identify students at risk of poorer outcomes and found ‘no-engagement’ alerts to be more efficient at spotting students not progressing and not attaining than demographic data alone. In fact, by further exploring outcomes from disadvantaged groups they found that the chance of students with a widening participation status generating an alert is on average 43% higher than their more advantaged peers.

As the education sector faces a greater challenge in reducing disparities of attainment between socially disadvantaged students, we see one of the key challenges facing all institutions now being how to effectively target support or interventions to those that require it most?

The findings presented by Foster & Siddle (2019) highlight the effectiveness of learning analytics to help support without targeting students based purely on their background, providing an important breakthrough in helping to provide effective strategies for improving student outcomes.