Today’s universities and institutions of higher education have a wide array of predictive analytics tools at their disposal, meaning there are more eyes than ever on students that find themselves at the lower end of the grades. Research shows, that examining student data helps institutions better identify and support those who are struggling. This in turn improves not only learners’ outcomes, but also reduces the increasingly high cost of attrition being faced by today’s universities. First, let’s draw a distinction between the two types of predictive analytics most often at play in higher education settings: institutional analytics and learning analytics.
The goal of institutional analytics is to move the needle on some higher-order institutional metrics like attrition rates or year-to-year completion rates. In the world of higher education, institutional analytics are key, especially for universities struggling with funding streams. Attrition accounts for a large percentage of funding issues so big-picture data analysis is vital for higher education leaders seeking to understand the scope of the attrition issue.
Emphasis must also be placed on the power of learning analytics, or data that helps decrease student drop-out rates by providing both lecturers and learners with information about their progress. Learning analytics is focused at the level of the individual learner and on giving learners actionable information to make decisions about their studies within a given course or set of courses. Taken together, these two analytical approaches constitute “big data”. This big data represents a growing body of information on academic performance and student engagement that gives universities the insight they need to improve flagging student graduation rates, and ultimately save their bottom line.
While some big data critics point to its inherent privacy concerns, the level of insight and improvement this type of information collection and processing has the potential to provide cannot, and will not go unharnessed. A recent report states that “a little less than half of the nation’s students graduate in four years; given two more years to get the job done, the percentage rises to only about 60 percent.”
Nearly half of all university students leave higher education with no degree, saddled with large debts.
These staggeringly low graduation rates mean that almost half of our university students are leaving higher education with not only a big chunk of debt, but also no degree to show for it. The impact is indisputable: whether you’re considering the earning potential and quality of life for young people who don’t achieve a degree seeking meaningful, well-paying work, or the financial health of higher education institutions dealing with a dropout crisis, falling retention continues to be a hot topic of increasing concern that is in need of sizeable intervention.
University leaders have begun to use big data to analyse not only academic performance, but other metrics including student engagement and interactions that can go on to predict whether or not a student is more or less likely to drop out. The wealth of insight big data can bring is set to further equip and empower higher education students and administrators alike, hopefully, leading to a brighter and more successful future all round.