Evidence of the Month: Brockenhurst College


Each month, we highlight one of the new additions to the LACE Evidence Hub, which brings together evidence about learning analytics. You are welcome to add to the Hub site, which you can visit via a tab at the top of this page.

The Evidence of the Month for November 2015 is concerned with the roll out of analytics at scale. Brockenhurst College implemented learning analytics in September 2015, with the aim of improving student recruitment and retention by 15% over the next five years.

The predictive analytics used by the further education college (catering mainly for students aged 16 to 19) are based on a regression model that employs five years of student data. When the model was tested on a previous year’s data, its predictions were correct in 87% of cases.

A start-of-term model identifies students who are likely to be most at risk of drop-out before they join the college. An in-term model looks at student behaviours once they join the college. The two models are compared to see if an individual student’s risk levels are rising or falling.

Using learning analytics has changed the way in which the college deals with interventions and how it supports students who are experiencing problems. Previously, tutors identified and reacted to problems. This could mean that it would take three or four weeks before an intervention strategy was in place – a long time in a 30-week programme.

Now, data from the predictive model is shared early on, allowing proactive as well as reactive interventions. This is not a black-box model – predictions are shared together with the factors that the model suggests are making a student high risk.

Analytics are shared not only with staff, but also with students. Visualisations show students whether their individual risk of drop-out is rising or falling, but do not compare their risk with that of other students. The intention is to spark conversations between students and tutors about the factors that influence drop-out and how these can be addressed.

In the video referenced here, Dom Chapman, Director of Learners at the college, talks to the 2015 conference of the Association of Learning Technology (ALT) about the initiative.

The analytics have only just been rolled out, so there is currently no evidence of their effect on teaching and learning, but the video does provide evidence that analytics are being rolled out at scale – in this case at a college with 8,000 learners.

Citation: Association for Learning Technology (2015), ‘2015 #altc: Invited Speaker – Dom Chapman’, YouTube video| Url: https://www.youtube.com/watch?v=7j7IBh2Hrik

About Author

Rebecca is a lecturer at The Open University in the UK, focused on educational futures, learning analytics, MOOCs, augmented learning and online social learning. She is a member of the steering committee of the Society for Learning Analytics Research (SoLAR) and was Workshops Chair of the second Learning Analytics and Knowledge conference (LAK 2012). She co-chaired the 1st and 2nd International Workshops on Discourse-Centric Learning Analytics, held in Belgium and the US, as well as the first UK SoLAR Flare (a national learning analytics event). Her most recent publication is the book ‘Augmented Education’, published by Palgrave in May 2014. Rebecca is working on the LACE work package relating to learning analytics in higher education.

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