Learning analytics was one of the buzziest innovations in higher education before the pandemic, as educators sought to tap big data and machine learning to develop more personalized learning and to help students get to graduation.
Artificial intelligence later sucked up that oxygen and much more, essentially subsuming predictive and learning analytics. Yet the rapidly advancing technology also has given a boost to this work, which is continuing across higher education and a growing number of community colleges.
“Much like the MOOC explosion, AI has cornered the dialogue, imagination, angst, and fears of innovators and curmudgeons alike,” says Myk Garn, a consultant and ed-tech expert who served as the University System of Georgia’s assistant vice chancellor for new learning models.
AI helped spark the idea for the Learning Analytics Builders Coalition, an upstart peer network that has been incubated by the 1EdTech Consortium.
“The promise of what AI could do in combing through ponds of data might make the process more efficient and find insights that we may have not even looked for,” says Kevin Corcoran, assistant vice provost of the Center for Distributed Learning at the University of Central Florida. “It also presents a new challenge as we introduce interactive AI solutions.”
Part of LAB-C’s focus is how to “move from balkanized data swamps to accessible, usable data sources for AI,” Garn says. Learning analytics and competency-based specification of learning outcomes are essential structural elements for making that happen.
A solid data infrastructure needs to be in place for a college to make the most of Gen AI, says Suzanne Carbonaro, 1EdTech’s vice president of postsecondary and workforce education programs. “If you don’t have structured data for Claude to look at there’s going to be a lot of hallucinations. A lot of inaccuracies.”
Learning analytics tends to touch many people on a campus. But it’s rarely anyone’s primary responsibility. LAB-C is trying to help with this conundrum by creating a user community for learning analytics builders on campuses—a convener of conveners, as Garn calls it.
Part of the peer network’s goal is to help community colleges and universities without deep pockets to learn from leaders in the field, with an eye toward developing their own homegrown learning analytics systems. AI arguably is helpful here, Garn says, because it can support the “DIY ethos at resource constrained colleges.”
As it seeks to build awareness, LAB-C is mulling a national survey to better gauge where a college sits in the learning analytics spectrum. The network also is seeking seed funding to get to the next level.
Carbonaro says the project intersects with 1EdTech’s broader work, including the consortium’s efforts around AI, interoperability, and open standards for data on digital credentials. “What is learning analytics in the world of AI?” she says.