Using Data in Higher Ed

Published May 1, 2017

Unizin is a very special collaborative effort. Our Consortium exists because of a vision shared by thought leaders at some of the nation’s most respected research universities. We are not a technology concern, but an organization committed to the improvement of teaching and learning. As part of that mission, we collect data on student engagement during the learning process, and we believe that data is the key to unlocking the next iteration of pedagogy and student success.

Imagine that a system of collecting and analyzing data was created by academics for academics. You own your data; you don’t need to purchase reports or subscribe to a service to get that information. It won’t be sold to outside vendors, it is standardized, and you can, with relative ease, access de-identified data from other member institutions within the Consortium for research purposes.

The Indiana University Advantage
There is a time value of data. For example, Indiana has four to five years of Engage data. They have insights into how students interact with their instructional materials across myriad courses, student types, pedagogies and can trace those to the students’ learning outcomes not only in that course but in that program and possibly beyond.

Short of a time machine, there is nothing anyone else can do to catch Indiana in terms of the historical aspect of their Engage data. That is your data poverty. Luckily, Indiana is part of the Unizin consortium, and we are going to have a research tier of data where Indiana’s data will, under the proper conditions, be de-identified and made available for researchers across this consortium. But they are data richer than you, and probably will be for the foreseeable future.

As you know, the world is becoming increasingly data driven. Whether or not the data collected will eventually mean something remains to be seen, but no matter how much you’re doing with data today, it’s not enough.

Unizin Learning Laboratory
All together, the information we collect will form what we are calling the Unizin Learning Laboratory, which will be separate from the management of our teaching and learning data. Research data doesn’t change frequently and is usually harvested when a major event occurs. This allows us to take the data, cleanse it, normalize it across the consortium, and with proper approvals (IRB or otherwise), provide it to researchers for the benefit of all of academia.

Brad Wheeler, Vice President for IT and Chief Information Officer, writes about data laundry, as “the legitimate process of transforming and repurposing abundant data into timely, insightful, and relevant information for another context,” the effort required is only surpassed by its value.” Doing it once in a university-controlled consortium such as Unizin is safer and more efficient than any alternative available to universities today. And to further assure security, the consortium model allows for a shared governance around various pieces of the analytics and the uses of the data.  

More than telling us which assignments have been completed and alerting faculty when a student begins to struggle – as important as those things are – the data collected by Engage can be leveraged for research at several levels. And while one university’s digital learning patterns are important for supporting student engagement and success, when we begin to consider the data collected across the Consortium, there is exponential impact.

By sharing that data within the consortium, you also have access to data from other member institutions. Considerations like student privacy, IRB review, and terminology are standardized at inception, so accessing and manipulating information for research purposes becomes significantly easier.

In this same vein, a common data model and infrastructure at the institutional level creates a common set of services and a common data model. This means the applications you build locally can easily be shared across the Consortium.

What we will do with all the data we are collecting today is unknown, but we have the mechanisms to capture, organize, and store that data so that when researchers identify the variables necessary for the ground-breaking insights of the future, the data is available to them.

Together, Consortium members will control and operate one of the academy’s largest, richest, and broadest collections of curated data across multiple universities to be the dataset for learning analytics. This Unizin Learning Laboratory will be the platform for risk managed data deliveries for teaching and learning research from a data pool contributed on a term-by-term basis by participating institutions.

And, here again, you’ll see that the power of collaboration is central to this massive shift in thinking and doing, teaching and learning. We will work together to define a new reference architecture. And as our Consortium grows, the insights and opportunities afforded by this wealth of data will inform our understanding of the learning process and improve learning outcomes for all students.