Even before the COVID-19 pandemic put pressure on students and staff, there was focus on student withdrawal. Today, with one in ten undergraduate students in the UK dropping out of university before their second year, now, more than ever before, withdrawal from university is an important topic in higher education.
As the pandemic continues, the disparity in the education sector has widened. More young people have been learning remotely than ever before. Online teaching and learning has made it more difficult to identify those who are struggling, those who have a job outside of learning, or who may have other commitments and, those who don’t have the same access to devices – such as computers and laptops.
In addition, with an increase in the diversity of students entering higher education, we cannot assume that all students are able to cope equally with the challenges they face in education. With the risk of withdrawal highest amongst underrepresented groups, it’s important for universities to have a strategy in place to provide support and targeted interventions to improve retention, success, and experiences during their time at university.
“Withdrawal refers to the point at which scheduled learning; teaching and assessment activities; and other active and on-going engagement end in agreement with the provider.”
Students leave university for many different interconnected reasons. Usually, there isn’t one defining moment that prompts student withdrawal from university. The reasons for withdrawal can be grouped into academic (relating to academic progress, teaching, assessment, and feedback,) and social (relating to informal peer group relations, engagement in extra curricula activities and interaction with support mechanisms and academic staff). External factors such as critical life moments, bereavement, poor health, maternity, financial constraints, and domestic responsibilities can also prompt withdrawal.
Tinto’s model on student retention developed in the 1970s, supports the theory that “the more the student integrates and feels a part of the institution within the academic and social communities related to the university, the greater the likelihood of persistence.” This research indicates that a lower sense of belonging, and engagement also correlates with a higher dropout and withdrawal rate.
To reduce withdrawal rates, finding an effective way to understand earlier when a student is showing signs that they are likely to withdraw from their course is a useful place to begin.
While universities may already have a range of support mechanisms and strategies in place to help them support at risk students, how many are actually capturing students early enough that are showing the characteristics that they are likely to drop out? And also, as importantly, how many of those institutions can provide information about student’s engagement to academic or personal tutors that equips them to start a meaningful dialogue? And finally, how many early intervention teams and student-facing offices are tasked with determining definitively if a student has dropped out from the institution?
In recent years, universities have been investing in learning analytics projects with the promise that they will be able to track and monitor individual students’ learning interactions and build an evidence-based picture of which students might be disengaged, struggling or otherwise at risk of early exit.
However, there are a range of obstacles that universities must overcome to move forward at pace and with confidence to get there. Projects of this nature can be daunting and attempting to do too much and perfecting the data can stall progress.
Success depends on having strategic conversations that produce a shared definition of student engagement, underpinned by data that is currently available. By making those assumptions explicit at course or university level it becomes possible to explore how using the available data can give insight into whether students are, indeed, engaged.
Today, university best practice places the emphasis on tackling the problem of withdrawal early to identify any issues before they arise or develop further.
Research from the University of Essex analysed student engagement across different time periods. The findings concluded that it was too late to wait until the end of the academic year, as there wasn’t anything they could do at that point in time to help those students who displayed signs of dropping out.
They found that the six-week mark of the Autumn term, there was the greatest correlation between low engagement scores and non-progression of students. At week six, 88% of undergraduate students with very low engagement scores did not progress.
The University of the West of England (‘UWE’) have also explored many methods to make outreach to undergraduate students that show signs of low engagement. Used in combination with system alerts, text messages and phone calling campaigns, online forms have been designed to encourage students who have been identified in low engaging categories to contact the institution directly, The online form has enabled UWE to respond more quickly and prioritise those students who have proactively asked for help. In one semester alone, 544 forms were triggered with 299 students requesting they needed help.
Louise Carey, Student Advice Coordinator, at UWE explains, “The student is having issues, having concerns, and wants to make contact. For a number of students, they might feel more comfortable making a phone call, and we would support them, but other students don’t. This is an alternative way that students can contact us proactively, and it can only really be a benefit.”
As students and staff continue to adapt to new ways of learning, and as we look ahead to a post-coronavirus world, we believe that a strategy based around engagement data and the use of technology can help universities in:
Solutionpath’s StREAM technology can provide an early warning to drive systemic action to reach out and connect with the students who need it most – to improve student engagement and reduce overall student withdrawal.
Whether it is the student not logging in as often as they should, not attending a lecture, accessing library resources, or submitting coursework, StREAM can notice signs that a student is not engaging as they could/should be – signs that they could potentially withdraw from the course.
At the same time, academic or personal tutors can view visualisations of the student’s engagement, performance, and progress to date; to help them frame conversations with them around actual data points for non-biased and supportive discussions.
Finally, technology can flag up potential mental health problems or stresses outside of the course. When a student is less engaged with their studies, it can often be due to other pressures, and technology such as StREAM can call attention to this.
Solutionpath’s StREAM – student engagement platform uses a learning analytics system to give you a clear picture of student engagement across all aspects of academic learning to identify risk to progression and proactively support student retention initiatives.
Find out more about how StREAM can help understand and prevent student withdrawal by contacting us today or requesting a demo.
