1. Student behavior will change based on the knowledge they gain from access to their own data.
“Because we engaged students with the data, we know their behavior changes,” Renick said. “Once we got the students their own information, we found, en masse, that they changed their behavior.”
2. Data tracking makes real-time data use and feedback a possibility.
At the University of Kentucky, the technical infrastructure brings all data from all student systems into a high-speed analytical platform. Working with Echo360 and Instructure’s Canvas, Kellen and his team access real-time streaming data from that platform.
“Now, we’re starting to think about how we can use that real-time data for in-class activity and feedback. How can we get deeper into the classroom and get intervention? We’re working with a handful of faculty on baby steps, but I think this is going to be the more common approach,” Kellen said.
3. Behavioral data is key. Real-time and in-depth data use is only just expanding in the higher-ed community.
“I think we’re in the first or second inning,” Singer said. “Many institutions are beginning to focus on behavioral data, beginning to build models about who a student is, what they’re doing, and based on that, you can have predictive data. We’re starting to see the move toward adding behavioral data. There’s a huge gap in behavioral data [right now].”
4. Clean, accessible data is a must for predictive analytics to have an impact.
Fitbit users are able to quickly access their daily steps and other data to assess if they’re on target to meet goals. Higher education needs a similar model, the panelists said.
“For these systems to work effectively, the data has to be in the right shape, and sadly, in postsecondary education, that’s rarely the case,” Renick said.
(Next page: Strategies 5-8)
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