"You are not going to serve a student population well unless you do your data work,"
"I don't think we could've scaled some of the strategies we've done unless we did the plumbing work upfront."
"How many institutions are creating their own, bespoke AI policy when they can do [it] as a group or borrow from Educause? There are so many ways to work together to go farther, to go faster."
"I do think more data beyond the walls-employer data, labor market data, employment outcomes-would be really helpful."
Institutions must invest in foundational data work—governance, data mapping, a data warehouse, and integration across learning management and student information systems—to support student populations effectively. Establishing these processes and best practices enables scaling of strategies and expanded capacity. Institutions should collaborate to share resources and policies, such as AI guidelines, rather than each creating bespoke solutions, to accelerate progress. Incorporating external data sources—employer records, labor-market indicators, and employment outcomes—can clarify students' learning journeys and postgraduation trajectories. Institutions should adapt to varied modern student needs by using data to understand who students are and how to tailor support.
Read at Inside Higher Ed | Higher Education News, Events and Jobs
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