Here at Quantified, we spend a lot of time thinking and talking about how organizations can use big data to drive improvement—not only in leadership communication skills (though certainly in that regard) but in operations, back-office processes, customer relations, and, ultimately, the bottom line.
We are privileged to use our own big data expertise—AI-driven communication analytics—to support a wide range of leaders, from Fortune 100 companies and TED speakers to higher education institutions, and as part of that, we consider how the data we bring to the table can help these organizations on a macro level, beyond simply helping leaders (or students) become better communicators.
So I was thrilled to come across an article from Northeastern University that reminded me of one very important distinction between our corporate clients and the higher education institutions we work with: they use data very differently.
Education hasn’t had the historical use of big data that the business world has, but we’re also looking at the data differently. Businesses often use this data to affect their bottom line and maximize profit. While we want to attract students and promote programs, the main goal of learning analytics is to improve teaching and learning.
– Michael Dean, Learning Analytics, Northeastern University
Organizations and individuals across the globe are creating 2.5 quintillion bytes of data every day, and many industries have been leveraging that data for years to make decisions designed to increase their bottom lines. But in higher education, where data analytics is a newer adoption, the goals are different.
How Is Higher Ed Using Big Data?
In higher education, big data isn’t about moving the bottom line or improving valuations. And it’s not that they don’t have data—they’re collecting hundreds of thousands of data points per day from online assessments, learning management systems, open online courses, and more, according to the Northeastern article. It’s just that they’re using it in a different way. The purpose, in higher ed, is about attracting and retaining students.
So what does that look like?
If one of the key goals of a higher ed institution is to attract and retain students, it stands to reason that leaders in those institutions would leverage data to improve their programs. And that could start with current students’ performance. Are students dropping out of certain programs or majors? What kinds of trends in the data might indicate what’s causing them to drop out? Is it a particular class? A particular point in the semester? A particular professor? What do the students’ backgrounds look like? Is there a widespread gap in student knowledge that’s limiting their chances to succeed?
All of these details can be used to make targeted, educated changes to a program, overhauling the curriculum, rearranging the schedule, or finding more effective faculty for certain courses. And when the programs are tailored in this way to maximize students’ chances for success, retention numbers are likely to rise accordingly.
Like many industries grappling with tight margins and economic uncertainty, higher education is under pressure to do more with less. Here again, big data can help institutions dig into exactly how and where money is being spent, then make evidenced-based decisions as to how to budget more effectively.
A report from American Institutes for Research and Johnson County Community College, funded by the Bill and Melinda Gates Foundation, refers to one model of data-driven resource management as “Activity-based Costing.”
Although accounting systems are set up to broadly categorize costs, institutions rarely assign costs to activities that comprise the function or break down the function into smaller units within the institution to allow internal decision makers to easily see how resources are used. As an example, instructional spending is generally reported in the aggregate, yet instruction encompasses a number of different activities, including course development, individual tutoring, advising, and, of course, teaching.
The idea, according to the report, is that once an institution can break down costs into specific activities rather than broader functions, they can start to see exactly where resources may be overallocated and where they might be lacking. When we know more about the costs of instruction, student services and administrative efforts, we can learn how to maximize those services and provide better outcomes withoutbusting the budget.
Whether an institution uses traditional activity-based costing or any other method, big data (and the means to analyze that data) is a key player in successfully identifying exactly where and how money is being spent—and reallocating as necessary to drive measurable improvement for the institution and its students.
Of course, as Dean mentioned above, the main goal of analytics in higher education is to improve teaching and learning. And there’s real power behind big data’s ability to do just that, particularly in the area of personalized learning.
We’ve taken several deep dives into the power of analytics to personalize learning, and we invite you to read our latest one here. But in brief, data analytics empowers professors of large courses to get a clear understanding of their students’ existing capabilities, skill sets, and learning gaps, both on an individual level and as a group, and then tailor the curriculum to meet each class’s unique needs. And even further, blending those big data analytics with AI-driven learning platforms, professors can offer supplemental materials designed to ensure that each and every student gets the support they need to get true, lasting value from the course.
Higher education may not have the same goals or history with data analytics that the corporate world does, but its potential to drive transformation and improvement is no less powerful.