One of the most obvious advantages of big data is how it can process substantial quantities of data at once and uncover patterns.
And, the higher education sector has data from countless sources to process. Here are six ways universities use big data to make strides.
1. To find rising athletics stars
When college sports recruiters seek athletics superstars, they could impact the success of the university over a long-term basis. For example, if a college team has a successful season, it might attract the attention of more donors than usual. And, research shows if a university does well in the March Madness basketball tournament, it experiences spikes in college applications afterward.
HomeCourt is an artificial intelligence app used by campuses including Duke University and the University of Florida. It’s made for basketball players and can track statistics such as shots made and missed, and the location of each of those shots. Video footage of players is available, too, allowing users to slow down or re-watch their shots.
Then, an analytics feature inside the app crunches the numbers. It could help highlight athletes that are flying under the radar. A sharing feature lets players distribute data to outside parties — such as recruiters — too.
2. To improve student retention rates
Troubling statistics show although there’s a rising rate of high school students going to college, the retention rate has increased by only 2.6 percent since 2011. Other data indicates certain factors may make students more likely than their peers to quit college before earning their degrees.
For example, black students graduate at lower rates than other groups, and people who are the first in their families to go to college are also at an exceptionally high risk of dropping out. Some colleges look into the problem more specifically and ask why students left before finishing. The data collected could help universities make changes to boost retention.
There are other cases where universities use software to alert student advisors if individuals miss classes or assignment deadlines. Then, those professionals can intervene and find out more about the factors causing students to slip.
3. To discover the factors that help students succeed
Data can also show the characteristics that make some students thrive in the face of challenges while others fail. At Southern Connecticut State University, data aims to link demographics and traits with student achievements. For example, the data collected by the institution found that a sense of belonging was one of the two main factors affecting persistence.
Such data could not only increase retention but give guidance for developing programs to help students that otherwise may unnecessarily struggle. Research shows that 69 percent of first-year students feel homesick. If the emotions related to that issue are severe enough, academic performance could suffer, but colleges may offer programs to help people feel more involved.
4. To target prospective students
Specialty companies work with colleges to build strategies for attracting people who are looking at potential higher-learning facilities. They use data differently depending on how far along a person is in the application process. And, the information gets distributed via email and physical mail, plus social media.
Much of the data comes from SAT-prep sites or other places students frequent while preparing for college. Mining through data in strategic ways allows colleges to make their marketing increasingly relevant. If a university has an excellent nursing program and an individual indicated nursing as a career choice, marketing materials about such educational options may arrive.
However, it’s crucial for colleges to market to students carefully. If prospects get overwhelmed by too much correspondence, they may take a college off their shortlists. So, universities might also use a big data platform to track what happens as a result of marketing efforts. If a student calls a college after receiving something in the mail about a program, that institution might take that as a cue to send other materials.
5. To better understand student insights
At the end of a semester, students typically have to provide feedback about the classes they took and the professors who taught them. Such data is helpful for future lesson planning or to improve managerial efforts to highlight poor performers within a college department.
A big data platform might show that more than half of the students taking a certain class thought the professor spoke too fast. Further analysis might conclude that the problem has been brought up for years. Then, the people involved in supervising the faculty could intervene and request that the instructor slow down the pacing of speech.
An interesting study of anonymous feedback on RateMyProfessor.com found students are more likely to use words such as “brilliant” and “genius” to describe male professors than female ones. Knowing that could help people account for possible biases when poring over the things students say while giving their thoughts.
6. To show when and how often students use particular services
Most colleges require students to use identification cards when they eat meals at dining halls, order tickets for campus events, use college health services or sign into university Wi-Fi networks, among other things. If institutions connect those activities with big data platforms, they could understand the most popular or necessary services.
Then, if necessary, campuses can make adjustments based on what a big data analysis finds, such as increasing the number of workers in a dining hall during the busiest times or altering the opening hours of a library so that it operates one hour later than usual on Mondays. Such changes could increase student satisfaction.
Big data could make big differences at universities
As this list shows, the institutions of higher learning that use big data could find themselves exceptionally able to stay competitive by fixing issues, addressing the most pressing needs and uncovering formerly hidden insights.
Those that choose not to investigate big data for research purposes could find it difficult to maintain prominence.
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