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Data Science in Higher Education

Data science has come to higher education and is creating a huge, quiet revolution that will benefit students, faculty and the institution’s bottom line.

Data science is a profession growing as fast as our passion to generate and consume information. It evolved to meet the demand to collect, coordinate and analyze all the information being created individuals, groups, organizations and companies.

The birth of the data science profession was inevitable as the amount of information has exploded over the past few years.

In social media alone, these are generated every minute of the day:

  • more than 350,000 tweets
  • 400 hours of new Youtube videos
  • 3 million Facebook posts
  • 4 million text messages in the U.S. alone

(Source: Microfocus)

For years, tech pioneers like Google, Amazon, Netflix, and others have been using big data to help us find what we like or want — and to sell us things we didn’t know we wanted.

For decades it’s been standard marketing practice to cluster people in the same age groups or with the same interests and promote products that they might like based on these demographics. Today, the huge amount of data being collected every nano second enables companies to read you, the individual, like a book, a detailed book, with tons of salient footnotes.


Today, colleges and universities face increasingly sophisticated competition as well as increasing personnel and operating costs. More and more institutions are adopting data science to decrease their margin of error in doing business and helping predict the future.

Any institution offering on campus and online courses are competing in a global market. To survive and thrive, institutions will need to use big data to:

  • Find students: Better data will enable institutions to market their programs to the students suited to each academic degree programs
  • Attract students: It will enable institutions to deliver the right talking points to those students
  • Retain students: With the right kind of data, institutions can allow students to personalize their educational experience, thereby helping retain the students through graduation.

This is a big and important point. For most institutions, recruiting and retention are the two biggest challenges in the higher education business.
Data science is providing new ways to conduct business. At a growing number of institutions, admissions officers are making faster and more precise decisions on who to admit based on big data. They’re using key performance indicators such as academic history, demographics and geographical data to predict which students are more likely to enroll if they are accepted, thus cutting marketing and advertising costs.

There is also a growing trend of Admissions offices looking at a potential student’s use of social media to collect more information. (Remember the social media stats above?) This real-time information gleaned from a student’s content and photos can give admissions folks insights into a student’s character, habits, discipline and potential for success.

Once the student is accepted, big data analytics can monitor that student and intervene with help if he or she is having problems. This helps ensure a student’s success and helps the institution’s bottom line.

When a student drops out of college, several areas of the campus are affected, from dining to housing to sports. And, as admissions officers know, every student who drops out adds to the number of new students to be recruited. Better retention cuts an institution’s operating expenses in many ways.

Big data has also given rise to predictive analytics which combines and analyzes information from several sources to make predictions about the future and perhaps even shape the future.

Some institutions are using predictive analytics to:

  • Advise students to help them succeed academically, thus increasing retention
  • Identify and advise students who are at-risk academically, socially or financially
  • Build models to more accurately forecast enrollments

Institutions are also learning that letting first and second year students fend for themselves is also a bad business model. Tracking and analyzing student behavior and performance –and stepping in to help students in trouble — will benefit students personally and professionally and the institution’s business success.
Most higher education professionals are familiar with Blackboard Inc., an educational technology company known for its pioneering Blackboard Learn. The company’s researchers are continually conducting learning analytics research on a huge scale. One of the latest developments is the quantum jump of categorizing types of courses into five “archetypes.”

The ability to classify courses by these five archetypes enables faculty and departments to identify course patterns that provide the greatest impact for different populations of students. According to Timothy Hartfield in his April 2017 Blackboard Blog: “Instructional designers and instructors can work together to fit course patterns to learning goals, engaging in evidence-based conversations about teaching methods in a way that, until now, was all but impossible.”

Blackboard’s information that led to the development of archetypes was based on a sample that included data from 3,374,462 unique learners in 70,000 courses at 927 institutions. That type of sample would have been impossible a few years ago.

Unfocused marketing to a large and varied audience lumbering and expensive. It’s also becoming a technique of the past. It is being replaced by smaller, more sharply focused campaigns that result is a larger yield for a smaller investment. This gives the institution a big edge in an increasingly competitive field.

The steady decrease of high school populations and international students will sharpen the competition for students into something close to cutthroat. Institutions with slow, creaky bureaucracies and leaders cautious of change will find themselves shrinking or even ceasing to exist.

For the most part, higher education has existed the last few centuries using the lecture model. The professor stood at the front of the class and imparted knowledge as students dutifully took notes. Yes, labs and discussion groups came on board in the mid-20th century, but central to everything was the professor lecturing, giving tests and handing out grades. If a student couldn’t cut it, he or she could leave. In recent years the professor has faced a classroom in which every student is carrying a mobile device– the product of and contributor to data science.

That was the death knell of the lecture as a universal educational delivery model. The institutions who incorporate data science into their business models are much more likely to succeed in an environment that demands more efficiency in all aspects of higher education business.

Developing ways through data science to attract students, retain them and help them succeed is vital to the institution’s bottom line. And in the last couple decades, institutions have discovered what corporations have always known: the bottom line is everything.


Resource: 30 Best Online Master’s in Data Science Degree Programs