In this series, we’re taking a closer look at the ways technology is transforming the higher education sector. From Artificial Intelligence to automation and big data, we explore the innovative products that institutions are using right now for ground-breaking results.
Wherever we go in the digital world, we leave footprints. From Google to Facebook to our learning management systems, we’re dropping data with every touchpoint. Some experts argue that access to user data is now the most valuable commodity on Earth.
The big question is, now we’ve collected all this data, what can we do with it? How can it help us learn better, teach better and create more successful education systems?
This is the question that universities across the world have been asking themselves. Big Data products, services and solutions are still in their infancy, but they’re already beginning to define a new era of learning that will have a big impact on both institutions and students.
In this article, we explore some brief case studies that highlight how universities are using big data solutions to create more effective learning experiences. Let’s take a look:
Predicting GPA scores with behavioural data
There’s a lot more to getting good grades than just being smart. Good study habits, routines and even sleep schedules can affect how well a student performs. Researchers at Dartmouth College believe that exam scores can even be predicted based on these simple data points.
By installing a simple app to collect passive data on 30 student’s phones, Professor Andrew Campbell was able to build a template of behavioural parameters that would result in higher
“Our SmartGPA results show there are a number of important study and social behaviours automatically inferred from smartphone sensing data that significantly correlate with term and cumulative GPA.”
These kinds of behavioural data technologies could have an enormous impact on the way students manage their time. It’s one thing to feel underprepared for a test – it’s another to know you’re underprepared based on the data. By using big data, your institution could offer a clearer path to academic success, which means better outcomes and a higher standard of graduate for your institution.
Reducing dropout rates with engagement data
Behavioural prediction is one of the key capabilities of data-driven technologies. Data can help us to identify when things are going right – or wrong – in the education process and provide us with opportunities for course correction.
That’s exactly what data company Persistence Plus aims to do with their latest product. By tracking students’ engagement with digital tools such as forums and learning materials, they claim to be able to ‘nudge’ them in the right study direction. Goal-setting technologies mixed with behavioural tracking and gentle study reminders can help keep up progress, resulting in fewer course dropouts.
In a sense, Persistence Plus is merely automating the job that academic tutors and student support have been doing for decades. Whether it replaces that personalised connection with tutors, or merely enhances it, is up for your leadership team to decide.
Better learning experiences with mass digitisation
Making learning more accessible should be a goal of every university. However, not every institution takes it as seriously as The University of Melbourne. Their dedicated University Digitisation Centre is implementing a range of platform and paper-to-data solutions that make key learning materials available to as many people as possible.
This kind of investment in digitisation is a game-changer, not just for students, but for the institution as a whole. Not only does digitisation provide location-agnostic access to important materials but also provides a platform for new, data-driven insights into study behaviours, research approaches and more. With the right data-collection tools in place, your institution can make new discoveries and systems that could transform your results.
For example, digitisation means rapid asset retrieval that can boost research time for PHD projects. And with the right systems in place, citations can be tracked back from paper to paper, dramatically speeding up the review process.
Big data can provide all these unique opportunities and more. But it all starts with an understanding of where your institution is right now with regards to data. Any innovation projects should start with a thorough audit of your existing assets to inform your path forward. You can learn more about technology auditing in our latest podcast. Take a look now for more useful content.