For years, marketers have used sophisticated software to track consumers’ buying habits and web browsing activity, then crunch this information and—based on the data—make a series of intelligent predictions that allow them to target their sales messages much more effectively.
Now, this same technology is appearing in schools and colleges as well—and observers say it’s a development that could revolutionize education.
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Using predictive analytics software, teachers at Georgia’s Gwinnet County Public Schools soon will be able to see at a glance which students might need more help. Sinclaire Community College in Ohio has cut its student dropout rate in half. And the online American Public University System has watched its course completion rate steadily climb.
These breakthroughs come at a key time for U.S. education, which is under enormous pressure to innovate and provide better learning opportunities.
As school district officials struggle to meet the goal of having all students graduate from high school ready for college or a career, the challenges are significant: Operating costs are on the rise, while budgets for public institutions are shrinking. Infrastructures are aging and need costly updates. Changing demographics require that schools change, too, to meet the shifting needs of students. Performance is declining at the same time that expectations are rising. And “working harder” is simply not a sustainable option.
“When we talk with government policy makers and senior education leaders, there’s a recognition that education is the differentiator for national success. Everyone recognizes that education is critical, but [schools] still get their budgets cut all the time,” said Michael King, vice president of global education for IBM. “People in education really grapple with that problem.”
To overcome these challenges, the education field needs new and innovative approaches. And predictive analytics is one such promising solution.
A smarter approach
Predictive analytics encompasses a variety of statistical techniques for mining information gathered across a variety of sources—for example, student information systems, library automation systems, learning management systems, and back-office enterprise systems—and analyzing those current and historical data to make predictions about the future.
The implications of predictive analytics for education are nearly endless. For instance, schools are using analytics software from companies such as IBM or SAS to track student performance over time, looking at various data points—not just test or quiz scores, but other, more subtle signals as well, such as how frequently students are logging on to an LMS, or how often they’ve contributed to online discussions—to identify those who are at risk of failing or dropping out. Some schools are overlaying a system that can identify these early warning signs automatically and create a customized intervention plan for students who might need it. (See “Texas system provides early warnings to help stem dropouts [2].”)
“Say a student comes into ninth grade with a D in eighth grade algebra. Using statistics, you can see that someone who made a D in Algebra 1 is not ready for Algebra 2. But you might also see that if they’re not doing well in math, they’re probably not doing well in science,” said J. Alvin Willbanks, superintendent of Georgia’s Gwinnett County Public Schools. “Attendance patterns can also be shown: Maybe that student was out of school 20 percent of the time. This could indicate there’s a discipline problem. Using analytics can alert teachers to these things, so they can be proactive in putting interventions in place to get the student back on track.”
Working with IBM, Gwinnett County is creating what is calls its digital content learning assessment support system, or eCLASS. When it is completed, eCLASS will give teachers the ability to open a page and see their class roster in a dashboard format. At a glance, teachers will get important information about their students, with the ability to drill down to see individual student results and suggestions for addressing each student’s academic weaknesses.
In higher education, Sinclaire Community College used analytics to look at registered students who hadn’t paid yet, perhaps because their grants or loans had not yet come through. Those students were in danger of having their registration cancelled. By doing the analysis and intervening with these students before cancellation occurred, the college was able to halve its student registration dropout rate. That allowed the school to hold onto its state funding and ultimately increase its graduation rate.
The 70,000-student American Public University System, a fully online school, uses IBM’s SPSS Modeler to measure key student performance, participation, and attendance information to predict when students are in danger of dropping out, so those students can be given additional support. APUS also is part of a national initiative to study the factors influencing college dropout rates, so colleges and universities can deploy analytics technology more effectively in their student retention efforts. (See the side story “Analytics use boosts student retention.”)
While looking at a student’s prior grades to see how well he or she will do seems rather obvious, predictive analytics gives educators the ability to look more deeply into the data, said Karen Patch, senior technical architect for SAS. “What you’re able to find is hidden trends and patterns that you otherwise wouldn’t be aware of,” Patch said.
Predictive analysis has been used successfully in financial, insurance, retail, and other industries for years. “This is how you get targeted ads over the internet,” said Alex Kaplan, national practice leader for education at IBM’s Global Business Services division. “These firms are mining data and looking for patterns, such as how likely a person is to make a purchase. It’s a sophisticated use of data, and it can be directly applied to education.”
Sometimes the technology reveals key information that might come as a surprise, helping educators look at situations in a whole new light. (See “Analytics improves effectiveness of Minnesota schools [3].”)
“One really big issue is how engaged students are in school,” Kaplan said. You might think that the more involved in activities a student is, the more likely he or she is to fall behind academically. But Kaplan said schools are finding that the opposite is true. The more engaged students are in school—using social collaboration tools where they can chat with each other online, accessing websites that contain curriculum materials and lesson plans, spending time in other activities such as drama or sports—the more likely they are to succeed in their studies.
“You might have students who are performing poorly academically, but who are really engaged in school, so you know they’re excited about school but are maybe struggling in a certain topic,” Kaplan said. “Or, you might have someone who is scoring well but is really not engaged at all. That student might actually be at risk of dropping out and would normally fall through the cracks. We’re now in the position to give this information” to school leaders before it’s too late, he said.
Now, Kaplan said, instead of relying solely on their intuition and observation, educators and administrators can have quantitative data to help them make better, smarter decisions. “And it’s not just about the individual student; it’s also about how the school thinks about instruction,” he said. “Once [officials] identify patterns and trends, they might learn that everybody is struggling with the periodic table in chemistry, so it gives them a deeper insight into the instructional process.”
IBM has worked with industry leaders for years, helping businesses use predictive analytics as a data-driven system for managing risk and improving their return on investment. In fact, the company says, in one recent survey 90 percent of respondents said they had attained a positive ROI from their most successful deployment of predictive analytics—and more than half achieved a positive ROI from their least successful deployment. Now, IBM has developed what it is calling a “vision for smarter education,” creating an analytics framework for schools that builds upon its expertise in analytics, business processes, and technology integration.
IBM’s new framework combines predictive analytics with “intervention management” technology, which can trigger a specific intervention that is unique to each struggling student’s needs and then deliver this remedial or supplemental content directly to the student. The entire process occurs through a single dashboard interface, IBM says; the solution is in beta-testing now and will be commercially available for schools and colleges early next year.
Analytics features are even appearing in popular LMS programs. Instructure, whose Canvas LMS program is available as either an open-source version that schools manage themselves or a cloud-based model hosted by the company, plans to release a version in early 2012 that includes predictive capabilities.
On their course roster, instructors will see green, yellow, or red dots next to students’ names, indicating how at risk they are of failing or dropping the course. Clicking on a student’s name will take instructors to a page where they can see more detailed reports based on that student’s grades, class participation, assignment completion, and outcomes (whether he or she has mastered the content).
At the bottom of the course roster, instructors will see a list of students who are most at risk in the class. The software also will contain dashboards for administrators to view the same information for entire departments or schools. (See “Analytics use boosts online student retention [4].”)
Getting started with predictive analytics
For school district leaders interested in using predictive analytics, the first step is to understand the kinds of data you want to analyze. Then, you must create a data warehouse that pulls this information from all available sources, including student information systems, learning management systems, enterprise software, and other areas. And the data need to be both consistent and trustworthy. “Having this information all in one place gives schools an enormous amount of leverage,” IBM’s Kaplan said.
The next step is to think about the key performance indicators you want to be able to compare. Some commercial analytics software might have certain options built into the system, while others might give users the flexibility to design a nearly unlimited number of queries themselves. Whatever option you choose, you should recognize that your needs are likely to evolve as you dig deeper into using the system.
Education leaders also should consider what actions, if any, they want certain indicators to trigger, such as sending a message about tutoring options to a student who is considered at risk of failing. Again, some software programs might contain certain built-in intervention choices, while others might not.
In an April 2010 whitepaper called “7 Things You Should Know About Analytics,” the higher-education technology group EDUCAUSE noted that analytics technology is a powerful tool that can help schools “identify where and when certain investments will have the greatest benefits.” But the organization cautioned that even the best data algorithms can result in misclassifications of students, “in part because such programs are based on inferences about what different sorts of data might mean relative to student success.”
In other words, predictive analytics software is only as useful as the data it draws from—and the suppositions that can be made from how those data interrelate.
That’s why it’s important to have as complete a picture as possible of a student’s academic history. And that’s the motivation behind states’ efforts to create longitudinal data systems that can follow a student’s progress from pre-kindergarten through college graduation and on to the workforce—a development that IBM’s King finds encouraging.
“If you can track a student across multiple schools, you have a common view of the student that can help drive understanding,” he said.
Other countries are starting to look at how data systems can help their educational systems fuel economic growth, by identifying students coming through the pipeline as individuals with particular skill sets that can supply the country’s needs in various areas. By taking a “P80” view of students—that is, a single longitudinal view of each student from pre-kindergarten through age 80—policy makers and education leaders are able to look at the “supply chain” of people moving into the workforce and discern whether there are enough teachers or nurses, for example.
In order to compete globally, this is the direction the United States needs to take as well, King argues: “It’s important that our educational leaders and policy makers recognize that that’s where we’re heading. It’s a completely different competitive dynamic. We have to start thinking not just about our schools, but … how we transform our state systems by bringing together a P80 view of each student.”
Jennifer Nastu is a freelance writer who writes frequently about education and technology.