5 Ways Data Science Is Improving Higher EducationKaylaMatthewsBlockedUnblockFollowFollowingJun 6Data science uses precise methods and platforms to extract insights from massive collections of information.
Universities collect data from students, faculty and staff members.
Here are five examples of real-world enhancements brought to higher education courtesy of data science.
1.
More Ways to Train Future Data ScientistsWhen people assess colleges and degree programs, they typically look for options that will give them advantages like job security and fulfilling careers.
It’s upsetting for a person to work hard for four years or more to earn a diploma and then discover their newly acquired skills don’t translate well to the job market.
Data science roles are in high demand, and earning a degree in the field could help students get maximally equipped to excel.
Numerous universities increased their data science investments through degree programs, research efforts and more.
If a learning institution earns a reputation for being a place that’s well-equipped to get aspiring data scientists ready for the job market, it could become easier for that organization to attract students.
So, data science investments improve higher education by making the sector increasingly relevant to current and future needs.
2.
Using Data to Spot PlagiarismEntire campuses and individual professors work hard to foster cultures of honesty among students.
At some colleges, incoming learners sign honor codes to pledge that they won’t cheat on tests or plagiarize when writing papers.
Getting a reputation as a place where plagiarism is common hurts universities because it gives the impression they don’t enforce rules to provide all students with a level playing field by weeding out the dishonest people.
Today’s intentional plagiarists use advanced techniques that go far beyond copying a passage of text from somewhere and tweaking it slightly.
But data science and machine learning algorithms can make comparisons through a process called text mining.
This approach allows educators to spot likely instances of plagiarism even when pieces of content seem unique at the surface level.
Then, it becomes easier for colleges to take decisive action against cheaters and maintain a campus environment that does not tolerate dishonesty for better grades.
As a result, the higher education sector can spend more time teaching people who want to learn without attempting under-the-radar tactics.
3.
Tracking the Flu on CampusFlu outbreaks are significant issues in any case.
They can spread exceptionally rapidly on college campuses due to the number of people an ill student could interact with during a given day, the dozens of surfaces they could touch in a given hour, and so on.
Many people discuss the phenomenon of the “college bubble,” where students rarely participate in things outside of campus.
That trend facilitates the spread of the flu because it keeps sick students in a relatively contained area and gives them ample opportunities to get others sick.
Researchers at two North Carolina universities developed an analytics model that tracks flu data with help from a mobile app.
They said it could theoretically determine how the flu spreads from one person to another.
In a trial, researchers asked students to report their symptoms.
Then, data distributed through a smartphone app gave each user a personalized forecast that advised them to stay home or go to class depending on whether the symptoms indicated they have a cold or the flu.
It did so by calculating the odds that a student would contract or spread the flu on a given day.
This is a fascinating application of data science.
But considering the sensitive nature of health data, an example like this makes it essential for colleges to uphold best practices for data security.
That means doing things such as training users and staff about how to handle data and understanding the kind of data a university has, in order to make appropriate decisions about protecting it.
4.
Assessing How Students Interact With PeersEducation experts know that if students begin feeling isolated at college and lack support systems, those factors could negatively affect retention rates.
Researchers from the University of Iowa discovered a forward-thinking way that could detect how well students fit in with others.
They examined data used at almost every university that has eating establishments: dining hall swipes.
The team revealed that measuring the amount of time between a person’s dining hall swipe and the person behind them using their dining hall privileges could help determine whether people ate meals with groups of friends or alone.
Moreover, the researchers got useful data as early as the second semester of a person’s first year at college.
This suggests that looking at peer interactions early on and connecting them to future graduation likelihood is possible and more necessary than previously thought.
The data collected through studies like this one could also help college representatives be more proactive in encouraging first-year students to get involved and interact with friend groups, whether by meeting people who live in their residence halls or elsewhere.
5.
Digging Into Data for Better Alumni EngagementCommunicating with university alumni to encourage them to donate is an essential practice for most universities.
Representatives know that financial support is instrumental in helping the campuses stay appealing to current and prospective students.
But the people tasked with reaching out for donations could quickly get off track by spending too much time talking to individuals who are not likely to give.
Applying data science to fundraising efforts could help universities succeed by figuring out the factors associated with the most generous alumni donors.
Having that information should make it easier for universities to plan more enticing events for the alumni, as well as aid in crafting more appropriate messages in donation request materials.
Diversity in Data Science Applications for Higher EdAs this list shows, there is no shortage of exciting ways to apply data science to the higher education sector.
People can expect more uses to emerge as experimentation continues to occur.
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