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Strategic Directions | Feature

Big Data: An Evolution in Higher Education's Technology Landscape

Big data has arrived in higher education, but it certainly is not leveraged to the extent that it could be or may soon be. What should we expect in the future? CT spoke with John Ittelson, Professor Emeritus, CSU Monterey Bay, who is currently a Senior Fellow and the Acting Associate Executive Director of the Online Education Initiative of the California Community Colleges, Chancellor's Office.

Mary Grush: What are the potential benefits that may come to higher education, from leveraging big data? Will changes related to big data come fast to institutions?

John Ittelson: The level of discussion around big data within the broad K-20 education arena is getting quite elevated right now. In particular, within higher education, much of the notion of disruption is attributed to the application of big data analytics to promote student success.

Big data is going to have a big impact and influence major changes within higher education, including student success rates. But, it's not so much disruptive as it is evolutionary. It's going to take some time for big data and analytics to transform the student experience enough to have the effect on student success that people are looking for.

Grush: What's the special nature of big data in the higher education context?

Ittelson: For many people, it can be difficult to understand, or to visualize big data. As simple as it sounds, all big data starts with little data. I use the analogy of a snowflake. There are lots of snowflakes — data points out there right now. But this doesn't become big data — a blizzard — until it's piled on heavy and deep, if you will.

We're slowly learning to recognize big data and understand its importance. We're finding that the digital footprints our students constantly generate provide a fantastic opportunity to collect and leverage information about our students in new ways — usually using data that's really already out there.

Eventually, you'll recognize this as big data. But until you learn to analyze it and take control of what you want this information to do for you, it's not really having the effect of big data. And it certainly isn't going to be seen as a big data revolution.

Grush: Where is all this big data at this point? How do you collect or identify it and what can you do with it?

Ittelson: Higher education has always been involved in data and data analysis. We've had to deal with it in the form of enrollment management, the processing of applications, in the student information system, in the course management system, in transcripts and academic records… But besides being for the most part siloed, this data has been somewhat limited by design — by the specific function for which it was originally intended.

I think what's happening now is, we are able to generate data that's much more granular. And we're breaking through those siloes. This is happening to the point that we can now look at the data in terms of the individual student and focus our attention on them, to create better service and support. Using big data analytics, we now have the ability — at least the potential — to ask and answer many more relevant questions, both in aggregate and about a particular student.

To be able to meet the needs of a given student, you have to know their preferences, strengths, weaknesses, their goals and desires — an elaborate array of facts that can be brought together as never before, with big data analytics. Some of this data may be collected directly from the student; other data may be taken from observable behavior and student choices. Either way, we are using big data analytics to provide better service to the individual student.

When we collect this big data and analyze it across cohorts and broader audiences, we can then start applying what we learn to improve our education institutions.

And the more data we have about more people, the more we can improve services to individual students. We can begin to offer more customized, personalized choices to help them meet their educational objectives.

Grush: Who owns the data?

Ittelson: Back in early 2000, I started talking about students managing their own eDentity. Part of that would be their transcripts, their records. But these kinds of discussions about "who owns the data" are not only taking place in the education community, but also in business and industry, government, politics, defense, and national security.

My strong personal preference would be for students to be able to control how their identifiable data is used and where or to whom it is presented. Still, we should consider the ability for the institution to use anonymized data on behalf of improving student learning or to support other valid research studies in a protected mode.

On an institutional level, with so much of this data being held, manipulated, and transmitted electronically, it's a much larger problem protecting both data integrity and the addressing privacy/access issues. There are many campuses that not only have an individual that has the responsibility for keeping data safe, but whole departments to make sure that student data is handled properly and securely, and protected.

So while I don't have answers to the ownership questions we will all face, I've seen glimpses of the kinds of discussions we'll be having as we venture further into the realm of big data in higher education.

Grush: Beyond institutional and student data, what about big data for instruction and for research? Are institutions going to be ready to handle, routinely, big data for academic and research purposes?

Ittelson: Institutional and academic data are separate issues, but related. Computers are becoming more powerful, data storage is improving in general, and larger datasets for research and academic use are becoming more commonplace. Students and researchers also have access to better data manipulation tools. Yet, similar issues surface on the academic side of big data to those we've seen with institutional and student data. You still have to protect the data, and there are often complex questions about data access and availability — along with concerns about the compute resources needed to manipulate them. Again, these are conversations we can expect to have on campus with the inevitable growth of big data.

Grush: Moving ahead with big data, what's the biggest blunder higher education should avoid?

Ittelson: Other than compromising large data sets — especially containing personally identifiable data — the biggest blunder would be not being transparent about what we're doing. And it's really more than transparency; it's also a need to educate. Most people don't, at this point, understand many aspects of big data, what its power is, and what its potential risks are. We need to inform the various stakeholders about what we're doing with their data and about the potential values to the people whose data we're collecting.

Grush: Do we need a new "big data" literacy to add to our technological awareness?

Ittelson: Yes, I think so! Since our early beginnings with computers we've had different "literacies" proposed: computer literacy, where you learn to operate and basically understand how to use a personal computer and work in a technology-enabled environment; digital literacy, the ability to analyze and use the information that's so readily accessible on the Web and through social media; and now we need people to understand big data. These are the sets of literacies we need to gain, about our technologies, their capabilities, and how to interact with them in our daily lives.

It's important that people understand big data, to know how to compare their individual data with that larger, 'big data' dataset and make informed decisions.

About the Author

Mary Grush is Editor and Conference Program Director, Campus Technology.

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