Students at America’s high schools, colleges, and universities are well into their first semesters. But while they plow through their assigned readings and write essays, administrators are turning their grades and their professors’ evaluations into millions upon millions of tiny data points. Much like every other field in the world, education is embracing big data–only, this time, they’re using it to determine who will thrive in college, who will fail, and who will need some extra help.
David Wright is Wichita State University‘s (WSU) Associate Vice President for Academic Affairs. In his position, Wright is responsible for overseeing the vast amounts of data WSU uses to track student and faculty performance. Like a growing number of American educational institutions, Wichita State uses predictive analysis tools to optimize their offerings and steer help to students who need it.
“We know our data better than an outside agency. We know the business practices in our system better, which outside vendors don’t do, and this allows us to do more with the data than them,” Wright tells Co.Exist. Using data points such as a student’s paper grades, the amount of hours he or she is enrolled during each semester, whether they’re working part-time or full-time or not at all, the amount of assistance from family and a host of other factors, WSU can predict which students are likely to encounter problems.
WSU has used predictive analytics software for the past several years. According to an IBM white paper, the university’s decision to implement a suite of IBM business analytics software in the school’s admissions department also helped predict the success rate for incoming students. The university decided to use the company’s analytics package instead of hiring external consultants to appraise incoming students. In a twist, it turns out the analytics software suite was better than the human consultants at predicting which students would succeed at Wichita State. According to IBM’s data, WSU’s recruitment model had 96% accuracy identifying “high-yield” application prospects compared to 82% by the external consultants.
But Wright added that integrating big data into the school’s practices took delicate work in terms of preserving institutional culture. “Implementing analytics requires getting in people’s business. Many units on campus that are overburdened or have low resources could see it as a burden. It means more requests for work, strangers showing up at their meetings, etc.,” he said. “But in terms of admissions, I saw they needed some information that they couldn’t get their hands on through external consultants, so I told them that if I worked with them I’d help them find benefits.”
Katharine Frase, IBM’s CTO of Global Public Sector, also notes that predictive analytics use by universities isn’t limited to admissions departments. She says that big data suites help schools “ask questions they didn’t know they needed to ask,” and shows use in everything from scheduling classes to identifying possible at-risk students in high schools. In one example of combining big data and predictive analytics, Frase noted that big data platforms can parse students interests and past academic performance, and recommend assistance for that particular student’s needs.
Another IBM executive, VP of Global Education Industry Michael King, said that, in education, big data and predictive analytics techniques are in their infancy. But he believes they will transform the industry as much as they have transformed health care. “We’re only just now starting to work through what opportunities are for leveraging big data in education, and health care is a model for this. We provide prescriptive solutions to help make recommendations to clinicians around potential treatment opportunities, how to intervene with certain patients, and we see parallels in education where we can use data to personalize the educational experience,” he says.
“The right set of information is everything. I think that, looking at lifelong learning and using data to help provide clearer pathways to students for a multi-institutional education plan, using tools similar to like Watson, is an important goal. We want to show how to put more tools in their hands for broad data. We can give prescriptive data to save time intervening for individual students.”
Predictive analytics tools from IBM and others are starting to become more commonplace in higher education–sooner or later, the tools used by schools like Wichita State will become the norm.