Data Analytics Can Save Higher Education
Thu, April 22, 2021

Data Analytics Can Save Higher Education

It is time for schools to get on board with data analytics / Photo Credit: Nirat.pix (via Shutterstock)

 

Georgia State University is known for leveraging data analytics to enhance student retention and graduation rates, according to Shailaja Neelakantan of EdTech, a magazine dedicated to K12 and higher education. Many higher education institutions use data analytics tools to gather student data to find out “who is at risk of dropping a course or flunking out.” These data can include attendance, WiFi usage, timely tuition payments, library visits, and grades. Schools can push students “towards a more suitable program of study.” 

Besides that, improving student retention alone can earn colleges approximately $1 million each year, per the findings of RPK Group, an advisory and consulting firm. If universities expanded data analytics to draw insights from the wealth of information at their disposal, they could use that data to innovate student recruiting, cost-containment, and institutional efficiency. As state spending on higher education declines and student enrolment falls, data analytics could be a “budget booster” for schools.    

Universities need to invest in time and talent to mine data, per the suggestion of Association for Institutional Research (AIR), EDUCAUSE, and the National Association of College and University Business Officers (NACUBO) in a joint statement. The data also needs to be shared institution-wide and it should not be siloed or be seen as individual properties of separate offices within a university. Utilizing analytics in higher education is effective when schools aim for clear, measurable outcomes. Further, it is important for faculty members, staff, and students to develop data literacy skills to analyze data in order to foster performance improvement in all areas. 

This strategy can help universities earn more money, but training and raising awareness among employees who mine sensitive data is also key. When using analytical technologies, universities should have a “deep understanding of the assumptions” underlying these solutions. Schools should take note that predictive algorithms might be perpetuating historical inequities and filtering low-income students or students of color into easier majors, said Jill Barshay and Sasha Aslanian of Hechinger Report, an independent education news portal. 

Setting ethics and expertise aside, now is the time for higher education institutions to get on board with data analytics. For every semester schools don’t do everything they can to ensure student success, they leave campuses without graduating, discouraged, and more in debt than when they first enrolled, AIR, NACUBO, and EDUCAUSE noted.