Data science is a growing and promising discipline that has impacted various domains, including higher education. Owing to its ability to use precise methods and platforms to extract insights from data, several academic institutions are incorporating data science into their operations and educational curriculum. This helps them engage students, improve educational outcomes and placement records, and boost faculty productivity and research opportunities.
Data science and advanced analytics are transforming higher education in more ways than one. Read on to know how!
1. Improves Completion Rates
Every educational institution aims at achieving a high completion rate and reducing the number of delayed graduation and dropouts. Predictive models in data science and advanced analytics can help colleges evaluate the high-risk students and take precautionary measures to reduce dropouts.
Each student has a distinct personality, learning ability, and socio-economic background. This information can help institutions identify students who are most likely to leave, enabling them to come up with the best strategy to reduce the risk of students dropping out. The University System of Maryland has started using data science and predictive analysis to boost graduation rates. Educators use student data to identify choke points (a difficult class or assignment), enabling them to intervene before failure becomes inevitable.
Lastly, statistical models can be used to forecast student performance. So, if a student is likely to score a poor grade, the model will warn the educator, allowing them to create a study plan to complete the course successfully.
Thus, data science can improve retention and graduation rates in higher-education institutions, thereby upholding the institution’s reputation, reducing college debt, and putting students on the path to success.
2. Helps Institutions Dig Deeper for Prospective Students
Educators are increasingly using data science to determine their target audience and develop marketing content that gets students to enroll in a particular course.
For instance, Saint Louis University, Missouri, uses data, such as the characteristics of the current year students to develop promotional campaigns that target pupils with similar characteristics. This strategy helped the institution expand its geographic reach. Further, the data enabled them to target a suitable student population who indicated their willingness to attend a private and religiously-affiliated college and had a mindset to travel to attend school.
Thus, extensive data mining can help institutions come up with a sound student recruitment strategy.
3. Accelerates Higher Education Fundraising through Alumni Donors
Encouraging alumni to donate to their alma mater is an essential practice in most educational institutions. Data science can help colleges and universities get interesting insights from donor profiles, enabling them to identify and communicate with alumni donors.
For instance, Brown University raised nearly USD 1.6 billion through a seven-year-long alumni fundraising campaign that used data mining to find 11,000 new donors. Researchers used variables like age, income, college majors, home values, and class reunion attendance to identify potential alumni donors.
Institutions are also using independent quantitative measures like how deeply alumni associate themselves with their institution are important when looking for ideal alumni donors. Research shows that people with a high level of alumni identity are ten times more likely to donate or volunteer their services to the institution.
Thus, data science can help institutions use alumni identity as a tool to accelerate philanthropy.
Listen to this podcast to see how data science can help predict whether or not an alumnus will donate.
4. Enables Institutions to Offer Industry-Relevant Data Science Education
Not only does data science transform the institution’s operations but also helps educators train future data scientists to acquire skills that translate well in the job market.
According to data presented by Glassdoor, data science is one of the highest-paying job domains with a great job satisfaction score and a median base salary of USD 110 thousand. Owing to the growing demand for data experts, numerous institutions are increasing their investment in data science through degree and diploma programs, projects, and research. The University of California, Berkeley is one such institution that includes real-world practicums for data science students, preparing them to excel in this space.
Such data science investments help educational institutions improve the quality of higher education, allowing them to align their students to the job roles in this domain. This not only boosts the colleges’ placement record but also earns them a reputation of supplying skilled and job-ready data scientists to the industry.
Further, the reputation of being an institution that offers in-demand courses for aspiring data scientists makes it easy for colleges to attract students who desire fulfilling careers.
5. Improves Quality of Campus Life
Data science and big data analytics can help educators uphold their students’ health and safety, thereby enhancing their quality of campus life.
For instance, two universities in North Carolina, are currently using data science to analyze their students’ health and lifestyle. The primary objective was to assess the risk of flu outbreaks on campus. For this, researchers at Duke University and the University of North Carolina-Chapel Hill asked students to report symptoms on their mobile app. Using this data, the odds that a student would contract or spread the infection is calculated, enabling them to keep their students safe from a bout of flu.
Further, integrating data science into the security system can help educational institutions improve student safety on campus. Data analytics-enabled security systems can pull out data, such as physical features of the suspect and identify high-risk situations, alerting the security in case of suspicious activity on the premises.
6. Gauges Student Engagement
Students who are highly engaged in college activities are more likely to complete their diploma or degree in time and have better college experience in comparison to those who aren’t. Further, a higher number of engaged students can significantly improve the institute’s standing and success rate.
Therefore, gauging student engagement and taking measures to boost it is critical for all institutions. Data science can help educators track the engagement rate using various metrics. For instance, metrics like card swipes into the college building, use of the library or gym, and online project submission among others can indicate a student’s engagement level.
The Nottingham Trent University, for instance, uses predictive analytics to calculate student engagement scores. The institution uses the VLE (virtual learning environment) access, library usage, card swipes, and assignment submissions to analyze and improve the engagement rate and build better relations between students and tutors.
The inferences from such data points can help educators develop better student-engagement activities and identify students who have a low engagement rate before the situation impacts the institution’s performance.
From boosting student engagement to enhancing institutional reputation, data science is changing the way educators operate. Though most universities and colleges are at an early stage of applying data science, a few pioneering ones are already using it to boost student success and foster institutional intelligence. Going forward, the role of data science in higher education is only expected to grow.