Data analytics at colleges and universities can be used in many areas, including student success, professor effectiveness, budgeting, financial aid decisions, course offerings, course enrollment guidelines, and staffing. With the proper use of data, colleges can also reduce administrative workload and costs.

Some of the data that is used revolves around student performance, such as course completion rates, time to degree, and transfer rates. Performance data can be collected through classroom-based software and online learning and classroom-management systems. Additional data can be collected through student surveys, professor and other staff notes, and social media.

Data Insights

Sandra Beckwith, in Data Analytics Rising in Higher Education (2016), provides examples of insights that came from data analytics at a variety of schools on how to retain students:

  • Housing Options. Students who live on campus are less likely to leave than students who live in off-campus housing. Expanding on-campus housing could increase student success rates.
  • Job Opportunities. Students who work on campus persist at higher rates than those who do not. Increasing on-campus job opportunities could lead to greater student retention.
  • Staffing for Success. At-risk students who respond to a coach’s outreach are more likely to be responsive to coaching and other assistance. Thus, it may be worth the cost to employ more coaches and/or train faculty and other staff members to be coaches.
  • Financial Aid Disbursement. Students who are late in filling out financial forms or who register for fewer credit hours than they had previously registered for could be indicator that they are not planning on returning to the school. This could lead to the school changing its financial aid allocation to those students.

In Universities Can Learn from Big Data – Higher Education Analytics, Bill Schmarzo describes several other ways that colleges and universities can use big data, from initial profiling of students to determine who is a good fit for their school through to alumni giving.  Here are a few of the examples Schmarzo gives:

  • Student Course Major Selection. A first-year college student’s performance in high school and on standardized tests can be compared to the college’s former students’ profiles to make recommendations in terms of a major and courses to take.  Detailed profiles can be made based upon grades and participation in high school, areas of interest – as captured by surveys and in social media – and test scores.  These profiles can then be compared to profiles of courses and majors to find the right match for each student.
  • Student Advocacy. Graphic analysis can be used to examine a student’s social network and determine the likelihood that the student will recommend the school to family and friends. The school can use the data to target specific areas of the college experience that are better or worse for a particular student in an effort to understand why the experience is what it is and how it can be improved.
  • Teacher Effectiveness. Data can be used to measure and modify instructor performance, though not all institutions allow for this. Those that can measure performance can “benefit from insight into an individual teacher’s effectiveness when compared to similar teachers. Performance can be measured by subject matter, number of students, student demographics, student behavioral classifications, student aspirations, and a number of other variables to ensure that the teacher is matched to the right classes and students to ensure the best experience for teachers and students alike.”
  • Student Lifetime Value/Booster Effectiveness. Colleges and universities can make predictions in terms of the potential giving levels for current students and alumni. Understanding the likelihood that a student or alumnus will recommend the school as well as predicting future earnings and wealth potential can “be major factors in profiling, targeting, and messaging to optimize alumni giving.” Schools can take advantage of these insights for the early identification of future supporters of the school or particular programs at the school.

The growth of data collection methods and improvement in analysis methods, combined with the competition that schools of higher education face, will make the use of big data at this level more important in the years to come. School are already starting to have specialists in data analytics to take advantage of the potential that this field holds for them in terms of improving student performance and operations.

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Author:  Neil Starr