Analytics and Student Success in the Two-Year Sector
In today’s higher education environment, the goalposts for many institutions have shifted significantly. Where the focus and incentives—both in terms of market incentives and government funding—were once squarely focused on access, today institutions are being held accountable for student success and completion. The incentive structures have changed and it’s forcing higher education leaders, especially in the two-year, open-enrollment sector, to rethink how their institutions support student success. Fortunately, this shift is happening at the same time as analytics and Big Data are making waves in the postsecondary space. These tools are providing institutional leaders with a new capacity to understand and respond to student needs. In this interview, Anton Reece and Renea Akin share their insights into how Big Data can transform student success initiatives at the two-year level.
The EvoLLLution (Evo): How have Big Data and analytics changed the way colleges and universities approach enrollment management?
Anton Reece/Renea Akin (AR/RA): The use of data and analytics has enabled the college to identify new potential areas of growth and expansion. In addition, data/analytics allows the institution enrollment team to identify short-term and longitudinal funding and impact from recruitment through retention and graduation.
Evo: How are analytics being leveraged to support student success and outcomes?
AR/RA: West Kentucky uses data to help ensure consistency in student success. For example, departments disaggregate student outcomes by location and mode of delivery to ensure the course outcomes for all courses are the same. If discrepancies are identified, action plans are set in place to address discrepancies.
Student outcome data has also been used to make staffing decisions. For example, if a discrepancy is found between a faculty member’s student success outcomes in an online and traditional course, the faculty member’s teaching assignment may be changed to better align with their strengths
Evo: How does the use of data as a mechanism to support student success change between a four-year and a two-year institution?
AR/RA: The demographics of community colleges tend to be heavily represented by non-traditional students, rather than the traditional, 18- 22-year-old student populations who bring similar but distinct needs for support.
The open access features of community and technical colleges—as opposed to the more selective approach by four-year colleges and universities—correlate to the scope and kinds of data collected, analyzed, and used to inform academic support resources. Additionally, the data collected from the rigor of transfer and major requirements at a four-year institution could inform advising practices at a two-year institution.
Evo: What are some of the most significant challenges higher education institutions face in maximizing the value of data?
AR/RA: An ongoing challenge pertains to consistency in the meaning of commonly used terms. One really strong example of this can be found in that the IPEDS definition of transfer excludes students who earn a credential. A student who earns an Associate in Arts degree and then transfers is not counted as a transfer student. However, a student who transfers with all the requisite credits for a degree—but who does not earn the degree itself—would be considered a transfer student.
Without common (and legitimate) definitions, it’s difficult to establish baselines, make comparisons, etc.
Evo: Looking to the future, what are a few other areas of institutional management you’re hoping to see transformed by Big Data and analytics?
AR/RA: Leveraging Big Data can increase the strategic use of data-driven decision making, including budgeting, student learning outcomes, success coaches, and advising. Employers, community stakeholders and legislator accountability will require institutions to be more data-savvy and consistently demonstrate the relevance and impact of their efforts.