Universities have always collected data on their students. But until now, much of it has not driven decision-making. What’s changed? The rapid evolution of technology is making it much easier to find answers to the questions universities have always asked.
Many of the fundamental questions that universities ask have remained the same for years. Advanced analytics provides a way to answer them more efficiently and cost-effectively than ever before. These questions cover all aspects of operations – from student-facing to back-office. For example, how can we improve payment timing and reduce questions by building student financial literacy? How does HR know where and how to post jobs to attract responses from the highest quality candidates? These are all examples where we have used analytics to create clear answers with a real difference to outcomes.
We’ve also used analytics to understand the impact of students’ experiences on their performance. How can seemingly small factors in a student’s first year create lasting impacts throughout their academic career? For instance, we were able to create an insightful picture of the impact that introductory teachers have on the subsequent academic performance of freshmen. Addressing questions like these has historically been a matter of assessing anecdotal evidence, such as interviews with students or teachers. Pairing this evidence with a data-driven framework changes the approach to student-teacher pairings and identifies academic staff who may need support with professional development.
We are also turning our attention to other issues that have dogged university leaders for years. For example, we’re leading a student success hackathon that aims to understand why students drop out of college. By combining data from a variety of sources, we aim to build new understanding on the drivers of student success. The results will help universities spot the early warning signs and make effective interventions to prevent the attrition that is so costly to both students and institutions.
Advanced analytics means we no longer have to guess at the drivers of student success. Machine learning can quickly identify the factors that determine a student may be at risk – and then make decisions and prioritize actions for university administrators to intervene.
Analytics can be a powerful new source of insights that can support more effective decision-making and strategic development. And it’s simple to get started. Concerns about the extent and quality of data are usually misplaced. You likely already have enough data to drive preliminary insights today and quickly identify opportunities for growth. Privacy concerns are also understandable. But anonymized, aggregated data is all you need to start making some groundbreaking discoveries. Since the technology is increasingly cloud-based and on-demand, you don’t need to build extensive system and data architectures before you can get started. The only real limits on the potential of analytics? The imagination to ask the most interesting and valuable questions.
To find out more, take a look at our education content hub. Or get in touch – I’d love to discuss how analytics could help your organization find fresh answers to tough old questions.