Covid-19 and the opportunity to mitigate university student drop-outs through the adoption of learning analytics


It has always seemed to me that the meaning of the proverb “The best time to plant a tree was 20 years ago. The second best time is now” is too deep for a lot of people. But, for those that get it, it can be a life changer. For too many years, understandable legal, ethical and moral fears by university leadership have tended to paralyse progress in the deployment of learning analytics to support their students.

There are laudable exceptions, the UK Open University is a world leader in the successful deployment of effective learning analytics OU Analyse and the UK’s Jisc organisation continues to deliver first class leadership and support to universities in this field. These fears coupled with the inevitable institutional challenges and barriers to change and the understandable concerns of teaching staff that these analytics might be used to make judgements of their own performance, are legitimate, but can all be addressed. In fact, the widespread use of AI based learning analytics is inevitable and not too far away.

Why? As always, it will be money that will drive change. A UK student dropping out of their degree in the first term can represent as high as a £30,000 irreplaceable hit to the university budget. On average, over 6% of UK first year students drop out during their first year, representing a staggering UK wide total impact to university budgets of between £600M and £1Bn. In Australia and the US the statistics are far worse, with over 20% of students leave their studies by the end of the first year. Not the noble reason we’d all like, but nevertheless the driver needed to achieve the noble goals.

In response to the dreadful effects of Covid, Universities have responded magnificently to support students in their return to study, with a major component of that response focused upon remote learning wherever appropriate and feasible. The risk of course is that academic staff’s ability to recognise students at risk is diminished by the reduced face to face time and we may expect drop-out rates to increase. This risk had already been increasing as student attendance at lectures and tutorials has steadily reduced as on-line availability of teaching material has increased. Universities can expect to see the actual impacts in 2021, but past evidence of student drop-out rates suggests that those institutions who exploit learning analytics to predict potentially at risk students may reduce the potential damage to their budgets.

It’s worth noting that the evidence shows that the vast majority of reasons for a student struggling have nothing to do with their academic ability, and everything to do with outside factors, ranging from emotional health issues and financial commitments to lack of support. The later that academic staff identify that an issue exists the more likely it becomes that a student may be lost. Key however is that if academic staff are able to detect issues there can be ways to provide timely support to the student and help them to continue.

Now is the time for universities to implement AI led learning analytics – 20 years ago would have been ideal, but like planting a tree ….

(If you’d like to learn more, check out The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes), if you’d like to learn a lot more, and you are somewhere where falling asleep would not be hazardous to your health, then The Application of Data Mining Techniques to Learning Analytics and Its Implications for Interventions with Small Class Sizes).

By Dr Ed Wakelam

Keywords: Analytics, Edtech, COVID19

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