It seems that over the last two years almost every aspect of how we live has changed. As a society we have had to re-frame our views on so many things as we adapt to new ways of working. When it comes to student engagement, perhaps we can look backwards in order to help us move forwards.
COVID-19 has, if anything, brought to the forefront challenges that the higher education sector has been battling with for years. Blended and flipped learning pedagogies and how they fit into a constantly evolving digital landscape has been a source of discussion for years and the speed of digital transformation has varied widely across the sector.
The pandemic changed that. Suddenly we no longer had the luxury to think about how remote and hybrid learning could work, it just had to. This created its own difficulties, not least around measuring and assessing student engagement in the new normal. Where attending class was the gold standard for many years, we can’t really say the same now. So, what can we use, and how can we assess its effectiveness.
Data can seem scary with a myriad of considerations – who owns it, who manages it, how is it accessed and stored, data protection, servers, bandwidth, the list goes on. Often it can seem that by the time all the pieces are put together in the right order it’s too late to do anything that could change outcomes. Once a student has withdrawn its too late.
Then there is the question of what data to focus on. Demographics, socioeconomic backgrounds and school attainment can all give you an idea of who a student is, but it can be too easy to focus interventions on something that the student has no power to change. It is also somewhat reductive to base intervention strategies around a particular group due to their past circumstance rather than their academic journey right now. It is the do and not the who that matters most.
Next, think about who actually needs to be able to see the data. Data is often left to the analysts, and it is hard to access and use. Putting into the hands of the people who have the ability to effect change – personal tutors who can use the information to help structure support conversations and signpost help resources, and the student themselves to see how they are participating in their study, can prove the best way to make effective interventions.
With so many different learning resources, course formats (theory vs practical vs placements) it is increasingly difficult to get a true understanding of what engagement looks like at cohort, course, module and individual level. And, importantly, what is working.
The truth is that there is no single definition that we can rely on, because for each university the systems they use, the policies adhered to, and engagement metrics will be different. We can generally agree that when we are talking about engagement, we think of how student are participating in academically purposeful activities.
There are numerous studies that show that a higher student engagement is indicative of later success which is why university leaders invest in strategies to enhance it. One report from Aston University for example, found that students who obtained the highest end-of-year marks were more likely to be in a higher engagement quintile as early as the first 3–4 weeks.
This engagement could be quantified by how many times they are attending classes, using the VLE, accessing library services or meeting with their personal tutor. It is essential for the institution to be able to define what engagement means to them and then they can move onto how they can ensure it is happening.
Now we know what we are going to measure, the next question is how is it done? There are various data models and theories but perhaps the best way is to start by seeing what lessons can be learnt from the past.
There is the old saying that “those who do not learn from the past are doomed to repeat it”. Therefore, if you can retrospectively look at the engagement data of students who have since dropped out of class, it could inform where to concentrate future focus and intervention helping future students stay engaged.
This sort of invaluable data can help to develop strategies and benchmarks to assess the effectiveness in creating forward looking initiatives, making sure that the right support gets to the right person at the right time.
Teesside University for example has been on a major digital transformation journey in recent years, assessing how they use innovative technology solutions to improve the student experience. They aimed to improve retention, progression, and attainment by finding a way for personal tutors to better support students, faster and with better outcomes. But first, they would need to find a way to better understand what engagement looked like at Teesside currently.
Jonathan Eaton, Academic Registrar stated, “We realized that we needed a consistent approach to how we manage and record all our data in order to understand the complexity of the picture at Teesside and learning analytics was identified as our solution”.
Since 2013 Solutionpath has empowered universities with tools to measure and analyse engagement data. StREAM Solutionpath’s student engagement analytics platform provides both staff and students with a real-time engagement dashboard. This shows how a student is interacting with their studies, allowing for conversations to take place to help student who are struggling get back on track.
With the StREAM Data Foundry, we can produce retrospective analysis which takes historical engagement data from existing digital systems at the university and applies our engagement algorithm to create a model of engagement for your institution. We have seen that when a Foundry is run, students who went on to withdraw spend on average 80% – 90% of their time in a low or very low engagement category. This is substantially more than students who continued their studies and indicates an opportunity for early intervention.
Foundry gives you cohort, course, module and individual student engagement analytics data providing a granular view of a student’s academic participation. By comparing this against withdrawal data, how far back it could have been possible to put meaningful interventions in place, and perhaps prevent the student from dropping out of their study.
In this way the past really can inform the future. If you would like to see if running a Foundry could work for you book a discovery call with us today.
It seems that over the last two years almost every aspect of how we live has changed. As a society we have had to re-frame our views on so many things as we adapt to new ways of working. When it comes to student engagement, perhaps we can look backwards in order to help us move forwards. The pace of change COVID-19 has, if anything, brought […]
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