It’s been dubbed ‘the new oil’ and businesses around the globe are scrambling to exploit the repositories of data in their keeping, in a bid to extract actionable insights that can give them an edge on the competition.
But data technology is not the sole preserve of the big business world. Educational institutions around the globe are getting in on the act too.
Their embrace of data analytics and predictive modelling will have a significant impact on the way in which courses and institutions are marketed, curricula are structured and students are monitored and supported.
The technology toolkit
Recent advances in technology have made it possible for education providers to take advantage of their increasingly large data sets. They’re now able to extract insights which can be used to improve teaching and learning outcomes and develop sophisticated predictive models for the future.
A typical student has many ‘touch points’ with their educational institution – think library records, tutorial log-ins and sessions spent accessing virtual course-ware – and, over the past decade, the capability to monitor these touch points has increased exponentially.
Understanding patterns and trends in this data trail has enabled education specialists to develop learning analysis software which uses predictive modelling to generate student insights.
The term ‘learning analytics’ refers to the measurement, collection, analysis and reporting of data about learners’ progress and the context in which learning takes place. Learning analysis solutions can typically accept data from a myriad of sources and systems, including an institution’s core Student Management System.
The big data draw card
There are several reasons why being able to aggregate and exploit their vast reserves of student-related data appeals to educational institutions.
Growing student numbers and greater flexibility in the way they study have made it more difficult for educators to monitor students manually and provide support where warranted.
Australia’s user pays education model has created a client mentality among students and raised their expectations of receiving timely, personalised service when required.
As more businesses use data analytics to drive targeted and anticipatory marketing and customer service strategies, it’s likely students will come to expect a similar approach from educational institutions.
Profiles and predictions
The first-year attrition rate in Australian universities sits at 15 per cent, according to the federal Department of Education and Training’s most recent statistics. While some students who drop out return to their studies at a later date, the majority do not.
Harnessing the power of data analytics can give education providers greater insight into the factors that affect their students’ performance. Learning analysis technology can predict their likelihood of academic success and course completion, with a high degree of certainty.
The software can be used to create unique profiles for students, based on their demographic details, assessment results and data gleaned from their interactions with education services.
A predictive model can be used to identify their probable learning outcomes, based on the experience of students with similar profiles from previous academic years.
Making a difference when it matters
Providers are able to use this information to identify students who are struggling, or who will be likely to do so. They then have the option to stage targeted ‘interventions’ in time to make a difference, not months downstream of an issue.
A simple conversation with an ‘at risk’ student may be enough to keep them enrolled and on track. The action may result in the creation of a tailored intervention plan, or the student being guided towards learning support facilities.
Best of breed learning analysis solutions allow institutions to record and track these interventions and share details with key stakeholders, such as academic and support staff.
Institutions are also able to use the software to monitor the long-term effectiveness of different intervention initiatives. The insights obtained can be used to develop better learning support strategies for future students.
"As more businesses use data analytics to drive targeted and anticipatory marketing and customer service strategies, it’s likely students will come to expect a similar approach from educational institutions."
A better informed learning future
The data analytics discipline is still in its infancy. Organisations of all stripes are travelling through uncharted territory, towards a future where strategies and decisions are data driven, by default.
The forthcoming years will see the education sector identify a host of ways in which insights extracted from data collected across the learning lifecycle can be put to use.
They’ll include assisting providers to plan recruitment strategies and curricula and helping high school students identify courses and careers best suited to their interests and abilities.
For tertiary students, there’ll be more opportunities to assess the potential benefits of learning and lifestyle choices, such as taking a gap year or undertaking an internship, based on historical data for their course or cohort, rather than gut feel.
The potential applications are enormous, for education providers prepared to invest in making sense of the swathes of student data they collect every single day.
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