Big data has been used to improve almost every aspect of our lives, and higher education is no exception. Over the past few years, universities have been working hard to figure out how to leverage the massive amount of data theyre collecting to make their schools perform better and assist students in completion. Predictive analysis of data sets can be used to improve efficiency and tailor the college experience according to students needs. However, some insights gained can be used for irresponsible purposes, and higher education organizations need to be aware of these pitfalls and avoid them. Here are some of the ways big data can be used responsibly in higher education.
1. Improving Graduation Rates
Universities have been challenged to maintain high retention rates for years, and many state schools receive funding based on several performance benchmarks that include retention, on-time completion, and transfer rates. Georgia State University achieved great success in using data analysis to help improve graduation rates for low income and minority students. Using the data they collected from students, Georgia State amped up advising efforts and was able to close the gap between these low income/minority students and the rest of the student body over a ten year period. Not only did the university close this achievement gap, it was able to raise the overall graduation rate by 22 points.
2. Providing Data to Drive Policy
In a data-driven society, its important for universities to provide policymakers with concrete evidence that issues such as completion rates for low income students exist. Doing so can help encourage governments to offer more funding for these students, or pass legislation that helps schools accomplish their goal of helping all students succeed.
3. Adjusting Resources Offered
Schools have been using predictive analysis to figure out why some students succeed while others dont. Washburn University has used this approach to offer needed resources that keep students in school and on track for graduation, like advising and counseling. Using data from academic advising, the school noticed that students who work or live on campus are more likely to persist. From these findings, the university is working on expanding work study and on-campus housing access to help students succeed.
Big Data Pitfalls to Avoid in Higher Education
Big data is an exciting tool for schools, and it can help accomplish many important goals in higher education. However, data experts at these institutions need to be aware of the potential pitfalls and misuse of data that can easily occur. Here are some traps to avoid when analyzing data:
1. Using Predictive Analysis to Exclude Students
Data can reveal markers for predictions of success in certain classes. If profiles like these are misused, there is the potential for students to be discouraged from enrolling in these courses, which would curtail students ability to test out different paths and fail in a safe space. Seventy-five percent of students are searching for meaning/purpose in life, and the university setting should be set up to encourage students to find their own path, not just the one that will boost all of the institutions success markers. If researchers notice trends in student performance that indicate a high rate of failure, this discovery should instead be used to allocate more resources for assisting these students.
2. Security & Privacy Concerns
Universities are still unsure about how to use student data, amidst concerns about privacy and security. There are not many formal restrictions on how student data can be used, and many universities have additional issues with cyber security, which leaves student data vulnerable to theft. Finding the right balance of privacy and benefitting students with the outcome of data analysis is still murky, and schools continue to grapple with the issue. However, if the data is used, then it is the institutions responsibility to protect student data as much as possible with cyber security measures, as data breaches are on the rise at universities.
3. Lack of Best Practices
Because big data in the higher education industry is still fairly new, there havent been many best practices established for collecting and analyzing data. Many universities are still unsure as to how best to apply the findings from these data sets to improve school policy and implement changes, which will likely fuel the demand for data specialists in the near future to refine the process.
The Future of Big Data in Higher Education
Though predictive analysis is on the rise in the higher education community, this data is not always being used responsibly or effectively to improve university performance and efficiency. As time goes on and some of the problems involved with higher education big data analysis are reduced, the practice will become an even more effective tool for creating institutions that serve students, first and foremost.
