How data could help universities support students’ happiness

The higher education sector is finding new ways of using tech to support students’ academic and emotional needs.

The number of young people accessing mental health services has surged in recent years. One in five more 18 to 25-year-olds sought help in 2021/22 than before the pandemic, according to the latest report from NHS England.

Universities are acutely aware of the growing need to support students both academically and emotionally.

As the cost-of-living crisis puts an increasingly tight squeeze on student finances, many young people will find the opportunities they have had to enjoy time away from studies or socialise with friends are either severely limited or dry up altogether. This will increase the potential for loneliness and put pressure on students’ mental health yet further.

What students want and need from their institutions is changing and as a result, universities are continually looking at new strategies for supporting students based on lessons learnt from teaching through the pandemic.

The legacy of Covid

Through the rapid shift of learning online due to Covid, there were some university students who undoubtedly thrived on the flexibility they suddenly had to study around work, family and other responsibilities. But this wasn’t universally the case.

Others quickly fell behind as they hadn’t yet developed the independent learning skills they needed to keep on top of their studies and felt isolated from their tutors and peers.

With growing recognition of how unique the experiences of students have been in recent years, the sector has started to explore how technology could help deliver higher education in a much more personalised way to support students, both academically and emotionally.

Tailored approach

Some institutions have already started along the pathway to deliver more personalised learning by running surveys designed to ascertain what aspects of learning students want to happen in person and which services they would rather access digitally. When no two students are the same, the results of such an exercise can be interesting.

A lecturer might find some students who enjoy the in-person experience and would not want to learn any other way. Equally, there may be individual students who would really benefit from being able to learn remotely on Mondays and Wednesdays when they have part-time work or caring responsibilities.

The challenge is finding efficient and cost-effective ways to manage the staffing and available facilities to meet everyone’s needs. It may not always be possible to find a balance that works but getting a better understanding of students’ preferences is one step closer to at least being able to find a happier medium.

Wellbeing focused

Universities are becoming more proactive at using data to identify the early indicators that a student might need extra support, whether that’s academically, socially, or emotionally. This requires a fresh approach to how student information has typically been managed.

Key data on a student’s academic and social experiences need to be brought together to create a single view of university life that staff can use to spot changing patterns in behaviour or alert student support services sooner if there are concerns.

Imagine a student, previously highly active in group discussions and projects, suddenly starts being absent from lectures or stops making contact with their peers on the WhatsApp study support group. By joining up data relating to their recent academic and social life, instructors and student services teams could quickly flag issues and put help in place straight away if the student needs it. This could make the difference between a student who thrives in university and achieves the qualifications and skills they set out to and one who is struggling with their mental health or at risk of dropping out altogether.

Innovative teaching and learning

When people and systems are unified in this way, information can be shared swiftly and securely across departments, without putting pressure on staff workloads, freeing them to identify potential issues sooner.

There is huge potential for AI and machine learning to take data analysis in the sector to another level too. Institutions could use these tools to spot minute anomalies in information or behavioural changes that no human could possibly be capable of identifying with the naked eye.

In a higher education context, this could transform the way universities support students’ wellbeing and academic progress. In the future, a lecturer might be able to use AI technology to identify that a student needs help very early on in their course and reach out straight away to provide it.

It might even be possible to flag that a student is capable of much more than they are achieving, or use the data on a student’s background and circumstances to highlight that they may be at greater risk of feeling isolated when they are accessing lectures remotely.

Although it may not be widely evident yet, the preparation for this more data-driven future for higher education has already begun.

About the Author

Iain Sloan was formerly student systems development manager at Oxford Brookes University and is currently senior solutions consultant at Ellucian. Inspired by the transformative impact of education, Ellucian develops solutions that power the essential work of colleges and universities. As the world’s leading provider of software and services designed for higher education, Ellucian works with more than 2,500 institutions in nearly 50 countries—enhancing operations and enriching the experience for over 18 million students.

Featured image: ©rh2010