Student data matters most when it enables early intervention South African universities collect vast amounts of information about student performance, attendance, engagement and progression. Yet too often, that information tells institutions what has already happened rather than helping them influence what happens next. The opportunity is to use existing data to identify emerging academic risks […]
Student data matters most when it enables early intervention
South African universities collect vast amounts of information about student performance, attendance, engagement and progression. Yet too often, that information tells institutions what has already happened rather than helping them influence what happens next.
The opportunity is to use existing data to identify emerging academic risks earlier and connect students with the right support before difficulties become entrenched, says Taryn Migatchev, Head of Academic Insights and Reporting at The IIE and Advtech’s Academic Centre of Excellence.
“In practical terms, data-informed decision-making requires institutions to establish where each student is in their academic journey, where they need to be and what intervention is most likely to close the gap,” says Migatchev.
“A shared understanding of these questions gives lecturers, academic leaders and student-support teams a common view of student progress. It also establishes a clear path from evidence to action: identify the risk, determine the appropriate support, intervene and assess whether the response was effective.”
With student retention and completion continuing to place pressure on the higher education sector, the ability to recognise warning signs early is increasingly important.
Historically, institutions have relied heavily on retrospective reporting, analysing academic performance after results have been released. While this helps explain what happened, it offers limited opportunity to change the outcome for a student.
Bringing together current attendance, engagement and assessment data can provide a much earlier picture. Missed classes, declining engagement or weaker assessment performance, for instance, may signal that a student requires additional support.
“We have moved beyond using data for compliance or historical reporting purposes. It should be informing how institutions support students while there is still an opportunity to make a difference,” says Migatchev.
For students and their families, the stakes are significant. Higher education represents a substantial personal and financial investment, making timely support critical when academic difficulties emerge.
“When lecturers, academic leaders and student-support teams share a common picture of student progress, decisions stop being guesswork,” Migatchev says. “Students who need support can be identified earlier, resources can be directed where they are most needed and the effectiveness of interventions can be measured.”
But identifying risk is only useful if the insight reaches those who can act on it. Academic information needs to be consolidated and made available to deans, lecturers and student-support teams during the relevant semester, rather than remaining in central reports reviewed after the fact.
The value extends beyond individual interventions. Patterns in performance, progression and graduate outcomes can also reveal where programmes and institutions themselves need to improve. These insights can inform curriculum design, teaching development, quality assurance and the alignment of qualifications with evolving workplace needs.
“As higher education evolves, institutions need to learn about their own practice as rigorously as they teach,” says Migatchev.
“Data, used well, allows an institution to listen to itself. The advantage is not in having more information, but in recognising what it is telling you and acting early enough to improve outcomes.”
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