The nationwide concern with student debt, coupled with increasing awareness of economic inequality, have led colleges and universities to redouble their efforts to promote student diversity and keep tuition affordable. In many cases, this means that institutions are shifting their approach to measuring student success:

• A deeper look at retention: Instead of focusing solely on program completion, institutions are examining retention trends across demographic and socioeconomic groups. Additionally, higher ed leaders are evaluating why specific programs have better retention rates and analyzing whether those retention trends lead to better student outcomes.
• Understanding student paths: Institutions are tracking how students move throughout the organization during their enrollment. Are some programs enrolling a more diverse population than others? Are students transferring in or out or some programs more than others and why? Is a subset of students completing a program more so than others and why?
• Responding proactively to changes in the labor market: The shift to remote work has affected regional supply and demand for skill sets. Since many employers are now drawing from a nationwide talent pool, what may have been an in-demand skill set in one region may now be a crowded market. Higher ed institutions are tracking these changes to ensure that their programs are preparing students for in-demand jobs.

Historically, only large, well-funded institutions have been able to analyze student success through the lens of equity and inclusion, but advances in data and analytics solutions are changing this. With the right data sharing and analytics approach, institutions of all sizes can unleash the value of their data.

Getting Started with Advanced Student Success Analytics

Higher education organizations can jumpstart their student success analytics efforts by prioritizing the following:

Effective Data Modeling and Integration

Colleges and universities have no shortage of data – in fact, the breadth of data collected can create challenges for measuring and monitoring student and program performance. A robust data warehouse is key to answering crucial business questions, finding patterns in current and historical trends across business areas, and monitoring the success of programs and services.

It’s also important to empower employees with the right tools to get value from the data – even if they are not an analyst. Self-service analytics data visualization tools help all users transform data into usable insights. Through drag-and-drop dashboards and easy-to-use web apps, users can explore their data, discover patterns and connections, and uncover answers to questions they didn’t even know they had.

Data Literacy

Institutions should partner with data literacy experts to avoid relying on a single KPI or a surface-level understanding of the data. Doing so can mean that some groups of students will be left behind. It’s important to look deeper and ask the right questions. What differences exist among students with different entry statuses (e.g., first-time or transfer), cohort memberships, socioeconomic statuses, or demographic profiles? What initiatives are having a positive impact on retention for certain groups, and how can we scale those?

Data Governance and Data Sharing

The foundation of success for an analytics solution is an understanding of the data available for analysis. This is the goal of data governance: Developing a shared understanding of your data and the processes behind using it. A data governance strategy provides clear definitions of roles, responsibilities, and processes and creates accountability for information quality.