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UNIVERSITIES USING DATA TO DRIVE GRADUATION RATES

Monday 2 December 2019 Data Insight & Analytics

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By Nader Koder

State of the nation – what’s happening in UK Universities?

Earlier this year universities were given a stern warning from the government that they must do more to reduce the dropout rates currently undermining access to higher education. While the Education Secretary praised the progress made in the record numbers of students enrolling in higher education from disadvantaged and underrepresented groups, he also advised that this progress was diminished when a larger number of students subsequently drop out of their courses.

These statements have been followed by news and reports that poorer students and students with unconditional offers were more likely to drop out, with HESA figures showing 8.8% of disadvantaged students in 2017 did not appear as second year students, compared with 6% of students from more advantaged backgrounds.

With these figures and the demand for improvements, the Department for Education has started taking a number of actions intended to mitigate drop-out rates. This has included the introduction of the Teaching Excellence and Student Outcomes Framework (TEF), designed to assess teaching in universities in connection with the student outcomes, such as graduate-level employment or further study.

But what can universities do at an institutional level to help decrease their drop-out rates? The answer it seems, may be found in how universities use their data.

Institutions in the UK are increasingly introducing data analytics and predictive analytics tools to support faculties in steering students in a direction that will improve graduation rates.

International Success – where data is working

Over the past few years, US universities have seen positive results in improving dropout rates, particularly amongst less selective universities with students who are struggling academically.

By taking the vast historical data held, machine learning techniques are implemented via data solutions to identify possible future outcomes and enable faculties to better support students at risk of dropping out.

One prominent example to come out of the US has been Georgia State’s use of predictive analytics and a particular student success story was Keenan Robinson. A student who wanted to major in nursing and who during his first year of university gained a B average grade. While his performance may sound positive, the University’s use of analytics was able to identify the fact that Robinson was unlikely to meet the requirements for the nursing program he had planned to apply to.

At this stage, without intervention, the risk of the student dropping out is significantly high. However, because Georgia State’s data intelligence solution was able to flag this early enough, it meant that in this case Robinson’s advisor could help steer him into another healthcare program that would accept a lower grade.

This not only mitigates the risk of the student dropping out, but also produces a new career path for them. This becomes even more beneficial for the student when we take into consideration that fees for higher education have increased over the past few years, meaning those who don’t graduate can end up worse off than people who never went to university.

The benefits universities and their students are seeing mean these data solutions are finding their way into a rising number of institutions.

Positive Change – Building better student outcomes

While universities will always encounter students dropping out for a variety of reasons, the introduction of data solutions provides a pathway to help them take actions where the dropout is avoidable.

Overall, this not only drives graduation rates up and improves the student experience while in higher education, but it also means universities are empowered to achieve their purpose; providing better opportunities for students post education.

For the UK as a whole, the introduction of TEF has been a part of improving student retention where recent figures show the year on year increase continuing in an upward trend from 5.7% in 2011-2012 to 6.4% in 2015-2016. But when we look beyond this average into the results for individual universities, some fare much worse than this, reaching non-continuation rates of up to 19.5%. For universities facing these higher rates, the introduction of analytics becomes critically important if they are to make a dent in these numbers.

With an election looming, university fees are hot on political agendas, and a change in government along with Brexit are just a few things that could rock the boat once again for Universities.

In this uncertain time, with increasing challenges and targets to meet, more and more universities are seeing the value of the data sitting in their institution and vast improvements that come from its use. For institutions at this point of the journey, the implementation of a solution to create actionable change is the natural next step. We should expect to see a lot more universities incorporating data strategies and solutions into their projects and infrastructure to support achieving their upcoming goals.

CACI works with higher and further educational institutions across the UK to deliver solutions tailored to improving student outcomes.

With new UK wide initiatives to drive down dropout rates in higher education, what can be done at the institutional level to make further improvements?

UNIVERSITIES USING DATA TO DRIVE GRADUATION RATES