Data Analytics in Higher Education

In a university setting, data analytics is a largely untapped source of value for students, professors, and companies looking to hire and train graduates. Given its immense potential, data analytics in higher education plays a central role in the vision of creating a student-centered learning environment at Maryville University.

The term “data analytics” is suddenly everywhere, but what does it mean? And why is it crucial for student-centered universities to embrace it?

Data analytics is an expertise dedicated to examining data to find trends and draw insights. It’s an emerging field capable of revolutionizing healthcare, business, and even higher education — if, that is, universities are bold enough to harness its potential.

Data analytics has the tools and methods to parse big data (massive amounts of information across multiple, interconnected tech platforms). When an organization has billions of data points to sift through, data analytics allows it to make evidence-based predictions, models, and data visualizations (easily digestible images that present data) — all of which make it easier to make informed evaluations and decisions.

For Dr. Mark Lombardi, president and leader of Maryville University, data analytics is a key feature in the revolutionary changes he believes that higher education is capable of achieving. Maryville University is “at the forefront, really, of the digital transformation of higher ed — and we move fast,” Lombardi says in a recent EdUp Experience podcast. “We seize initiatives, whether it’s artificial intelligence, data analytics, what have you, and we apply those to the higher-ed model.”

Higher education faces challenges adapting to a rapidly evolving digital landscape.

For one, most universities comprise different colleges — schools of arts and sciences, schools of nursing, schools of pharmacy, schools of business — yet experts from different disciplines rarely talk with one another. This feature of the traditional model of higher education creates information silos, secluded spaces where professors and advisors from specific departments lack the opportunity to collaborate and innovate together.

One way the traditional information silo model hurts students is with retention. If a student is struggling in a math course and a pre-nursing course halfway through the term, because these schools don’t communicate, they may not identify that the student needs across-the-board support.

Institutions are slowly starting to realize how higher education data analytics can be used to identify at-risk students early. “I can’t emphasize enough that it’s not gathering data about students to use it to sell them things,” Lombardi explains. “It’s about gathering data about their learning and empowering them with that knowledge.”

Lombardi believes that students who may have struggled in traditional classrooms can succeed in higher education when schools embrace data-driven learning. “It’s an amazing thing: When you have a student who thinks that they can’t learn certain things — and so they avoid them — to then help them understand that no, you can learn this! It’s just the instruction needs to change, an approach needs to change, and when it does and they’re empowered and they find success, it’s just wonderful to see.”

And while some schools are just starting to think about how they can use data analytics to support at-risk students, Lombardi envisions a university in which all students have access to powerful insights driven by big data.

Multiple factors have enabled Maryville University to embrace the technological revolution both on campus and in online program offerings.

At Maryville, leaders are excited to learn how students learn — then use those insights to create better class environments. By collecting and analyzing student data from online and in-person classes, Lombardi envisions a university experience that’s completely tailored to each student and their goals. The dream is to create personalized learning and advising experiences that enrich a student at all stages of their academic life.

Lombardi understands that experts should drive revolutionary technological change. That’s why he’s enlisted the support of university faculty from day one.

By elevating innovative faculty leaders willing to lend their expertise to transform the university at every level, professors with advanced expertise in data analytics, data infrastructure, and artificial intelligence are coming together to form a team that’s united in creating cutting-edge educational experiences for their students.

Rather than staying stuck in silos, professors at Maryville University are empowered to join forces and collaborate on designing and implementing data analytics initiatives.

Data processing and predictive analytics are transforming every major industry, including higher education. This trend is lowering the cost of the technology infrastructure, which in turn is creating affordable opportunities for using machine learning in higher education.

Machine learning in student assessment applications can help professors and instructors determine the best teaching practices for their students and improve learner outcomes.

Maryville University leaders recognize the promise and potential of artificial intelligence (AI) tools. With AI programs that can parse hundreds of thousands of data per day, leveraging cutting-edge technology can make massive amounts of information interpretable and actionable.

It turns out that universities produce massive amounts of untapped data — from student enrollment metrics to digital habits and learning practices.

Data analytics in higher education transforms mere information (raw metrics) into usable data (actionable insights). Here’s a brief description of how this works:

Within data analytics, descriptive and predictive analytics can be incredibly useful in improving the higher education experience for students.

Descriptive analytics presents a snapshot of the current moment based on data such as GPA, test scores, demographic information, and student intake information. Using descriptive analytics methods, data experts can provide insight into how a school is functioning, how well students are learning, and where administrators need to make changes.

Predictive analytics projects into the future. Grounded in large datasets, predictive modeling can provide leaders with the ability to forecast whether a particular university initiative has a high likelihood for success and what barriers may need to be removed or what structures must be installed for better prospects.

Data visualization refers to tools and methods of displaying complex information in a clear and organized format. With their data visualized, learners can gain insight from a school’s back-end data analysis work. That’s where dashboards and reports come in.

Dashboards are online spaces where learners can gain access to their information. Having access to well-designed dashboards and reports can help learners:

For university leaders, dashboards and reports can give insight into student needs and suggest ways to address them.

Universities should embrace digital transformation as they shape higher education for the future. Students who want to take the next step in their careers should consider enrolling in a university whose leaders, like Maryville University President Mark Lombardi, understand the value of innovative fields — including higher education data analytics.

Maryville University’s student-centered investment in data analytics is creating more personalized learning environments, empowering students to navigate higher education with greater ease and reach their career goals. In Maryville’s online degree programs, students learn from experts who are excited to teach the next generation of data analytics leaders. Blaze your career path at Maryville University.

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EdSurge Podcast, “Why One University Is Moving Toward a Subscription Model”

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