Big data may have some big implications for graduating students on time at the University of Nevada, Las Vegas (UNLV).
More students could graduate in four years if the university analyzed the effective practices of successful students, said Cam Johnson, operations center manager in the Information Technology unit at UNLV. Those practices may include data points such as how many times students log into an online learning tool, how much they participated in discussions and how often they watched videos that instructors shared.
Johnson has pitched the idea to university administration. He claims that by looking at what successful students do, university staff could identify students who don't match those behaviors and figure out how to help them learn better.
While the distinction could be important for student success, it also may have an impact financially. Nevada is planning to give universities funding based on graduation rates rather than enrollment rates. If UNLV can find a way to help students graduate in a timely fashion, it will benefit both parties.
Johnson said in order for a big data approach to work, UNLV needs to be able to see a student's data from all of its systems in one place. That's where machine data will be helpful.
With a machine data system, student information from various platforms can all be brought into one bucket, no matter what form the data takes. Then university staff members can look at real-time data from different systems and get a sense for how students are doing.
UNLV is already using Splunk, a San Francisco-based big data company, to analyze data on the IT side of the house. Johnson said the next step is to allow the institution to integrate both IT and instructional data in one place, and make better use of the student data that already exists.
"The data's already there," Johnson said, "but just give people access to it and they'll find some good ways to make use of it."