Using data analytics in higher education to support students and guarantee retention

Imagine that you are a university student, struggling to keep up with your studies. Your grades are beginning to suffer, and you are considering dropping out. For many students in this situation, support may not be sought out or readily available – causing them to drop out of higher education. 

Data analytics have the capacity to change this reality. The academic use of data analytics can be understood as the process of evaluating and analysing organisational data, received from university systems, for reporting and decision-making reasons.  

Through the analysis of student data, collected over extended periods of time, university staff can build a picture of student behaviours. Once the average or ‘standard’ student behaviour types have been identified, it becomes easier to notice those that do not follow these norms. These are the students that could be struggling.  

The word ‘struggling’ could refer to any number of things. Tracking a students’ grades or attendance records over time could reveal a consistent decline. This, in turn, could be caused by a multitude of factors such as mental health issues, monetary struggles or disabilities, all of which can have an impact on student wellbeing and performance.  

Unfortunately, students do not always ask for help themselves. Surveys have shown that over half of students do not feel comfortable asking for support from academics and university staff, leaving institutions with one option to ensure that those who are struggling don’t slip under the radar: proactive student services.  

The proactive use of data analytics in higher education can allow institutions to identify students that are at risk. Combatting students’ issues before they have caused irreversible damage is a great development and has the potential to significantly improve not only student welfare, but also student retention numbers for universities. 

So, what kind of support can universities offer through the use of data analytics? Brunel University London, who have been using WISEflow since 2015/16, tracked exam engagement daily during the pandemic, flagging particular patterns by different student groups. Combining this with wider student information, they compared those who took exams with those who didn't and asked whether they were unable to access assessments, and if they needed to be proactive in reaching out to support them. 

Digital on-site examinations being sat at Brunel University London

Through this analysis of student and institutional data, Brunel found that disabled students, mature students and those from deprived areas were less likely to sit exams in the upcoming Spring term. They then proactively reached out to these students, which helped inform their additional support put in place for the August 2020 (and subsequent) exam periods such as bookable quiet spaces, increases to hardship funds and more loanable laptops.’ 

In this example, Brunel used analytics in the form of exam engagement data to identify those who might be struggling and offered support accordingly. Combining exam data with student information, such as listed disabilities and average household incomes, allows institutions to fill in the gaps and proactively offer help to those that are struggling.  

Data analytics can be used to offer students more support – but how does this extend to student retention? This can be understood in two ways: targeted interventions to ensure students actually complete their studies, and reducing the overall number of students that are struggling with any issues while at university. 

According to a survey from the Chronicle of Higher Education, almost half of institutions that use data analytics have developed successful interventions for struggling students. This might be contacting students earlier about a missing assignment, or reaching out to those who show signs of being at risk, in an effort ensure students are more likely to pass their exams and graduate. Such studies have shown that analytics can improve efforts to keep students from dropping out, ensuring they make it to the end of their studies successfully.  

These direct interventions are a clear step towards improving retention, as they work to eliminate the issues students might directly have with their studies – but this is not the only reason that students drop out.  

Notably, financial problems are a huge factor in causing students to leave higher education. To highlight this, the recent Student Academic Experience Survey from Advance HE and HEPI revealed that only 6% of all students surveyed believed their education had not been affected at all by the ongoing cost-of-living crisis. Aside from an inability to afford rent and bills, students may also be unable to access the technology they need to complete their studies. 

Beyond financial hardships, factors such as mental health issues and under-supported disabilities can have further adverse effects on students, and if left unchecked can often lead to incomplete degrees. Data analytics can be used in this regard. Using student information in tandem with examination data, attendance logs and information on assignment hand-ins can help institutions to identify those that need help and give them the support they need.

If you would like to learn more about WISEflow and how assessment data can help support student retention and wellbeing initiatives, contact us at hello@wiseflow.eu