Page 8 - Campus Technology, January/February 2019

INDUSTRY TRENDS
access data for common customers. “When we have a Canvas customer who is also a customer of Civitas, they will give Civitas permission to receive their Canvas data, and Civitas leverages some machine learning techniques to
deliver their products and services,” Stein explained.
The company has a similar relationship with a vendor called Zoomi, which uses algorithms and analytics to predict learning outcomes and guide the creation of personalized
learning programs.
In addition to making data available, Instructure is engaging
in its own research and development activities that leverage machine learning. “We believe that data analytics is going to best serve teachers and students when it is sort of invisible — when it informs features or the ways the system interacts with you instead of being a dashboard or visualization,” Stein said. To that end, Instructure is piloting a tool called “Nudge.” The idea is that some students would benefit from nudges to engage with their course materials or classes. “In the current iteration, Nudge uses machine learning techniques to prompt students to log into Canvas or turn in assignments if we think they are likely to not do those things,” he said, adding that it is crucial that the message be personalized. “We don’t want to send nudges to the A-plus student who is already going to turn in the assignment. That is just going to be annoying.”
Supporting Recruitment and Retention
Andrew King is a machine learning developer on the applied research team at Ellucian, which develops enterprise resource management, student information system and
customer relationship management solutions for higher education. He said Ellucian is working on proofs of concept of machine learning approaches to retention and recruitment.
“We are right at the beginning of understanding of what machine learning can accomplish. It is going to find its way in just about every process,” King said. “On the recruitment side, it can be difficult to find candidates right for a company or in this case right for an institution. We will start to see a lot of work being done in that area to support admissions teams to make determinations about their applicants. Ultimately, we want these decisions to be made by humans, but supported and enhanced with AI.”
“Machine learning can be used to do identify patterns in the data,” he noted. “For instance, perhaps students who get some financial aid are more likely to stay at an institution. Another use is to try to identify students at risk of leaving the institution and flag them for contact by advisers.” Still another use might be to empower students with their own analytics, he added.
One of the complicating factors, King said, is that institutions are collecting data but it may reside in many different places, so being able to aggregate it in one place so a data scientist can do these analyses can be a burdensome process.
Rate of Change Increasing Exponentially
Ed tech giant Pearson deploys machine learning in several solutions. One product called Revel can provide students
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CAMPUS TECHNOLOGY | January/February 2019