Published on 2018/03/20

Measure What Matters: Time for Higher Education to Find Their Eureka Moment

The EvoLLLution | Measure What Matters: Time for Higher Education to Find Their Eureka Moment
With the broad shift toward performance-based funding for public colleges and universities, it’s critical for institutional leaders to be vocal in conversations about the metrics being highlighted and the purpose of the exercise.

As the story goes, the Greek mathematician and measurement guru Archimedes, upon discovering the best measurement to evaluate the purity of a gold crown, exclaimed Eureka! Having previously served as a director of institutional research and planning, and vice-chancellor of education for a large community college system, I often wondered what it might take for higher education to find its Eureka-moment regarding statewide performance-based funding systems and related metrics.

If one accepts Drucker’s premise “what gets measured, gets managed,” then two things are apparent: Measurement is valuable, but measuring the wrong thing has consequences. Data collection efforts focusing on the wrong metrics lead to mismanagement and failure to recognize potential opportunities.

Focusing on the right measures matters. For example, in Moneyball Michael Lewis describes how the Oakland Athletics improved their win-loss record by revising player evaluation metrics to more fully understand players’ potential to score runs. A growing number of States (more than 30 in 2017) have adopted performance-based funding systems to allocate funds to state-supported higher education institutions.[1] Such systems focus on increasing the number of degree completers and have been fueled by state and federal calls for increased accountability for higher education funding. Proponents of performance-based funding believe the logic behind performance-based funding seems clear: Tie state funding to the achievement of performance metrics, and colleges will improve their performance.

Although the logic might be clear, current research suggests it is time to re-examine the assumptions behind performance-based funding. In Why Performance-Based College Funding Doesn’t Work, Nicholas Hillman provides evidence of little to no connection between performance-based funding systems and improved educational outcomes.[2] The disconnect between performance-based funding systems and improved outcomes widens when one considers open-enrollment colleges and/or colleges which tend to serve a high percentage of adult, non-traditional or low-income student populations. More than half of those enrolled in higher education today are defined as non-traditional students and one-quarter of all students are over 30 years of age.[3] In addition, according to the National Center for Education Statistics, future projections show the adult student population growing at a higher rate than the traditional student population. [4]

Failure to grasp this changing trend and develop appropriate metrics within this new reality raises serious questions regarding the utility and equity of performance-based funding systems. This growing gap is a product of performance-based funding systems’ over-reliance on traditional metrics such as number of degrees awarded, term-to-term retention, and arbitrary time-to-completion rates. Such metrics simply don’t relate well to the educational goals and needs of these student populations.

So why are more states jumping on the performance-based funding train? States are under political pressure with calls for increased accountability and limited taxpayer dollars, but do the chosen performance metrics capture the full scope and impact of education? Do the chosen performance metrics serve the public and result in better allocation of state funding? The jury may be still out on these questions, but Hillman’s evidence suggests the answer is a resounding “No.” A review of national and state-specific studies reveals “states employing performance-based funding either decreased their degree productivity or they simply do not out-perform other states.” [5]

Regardless of the industry, pay-for-performance incentives seem to be effective when the task or process is straightforward and simple. An employee can be directed to ensure that Part 1 is connected to Part 2 and a final product is produced. The employee has full control over the process and a manager can easily track these steps and incentive improved performance. Even the most ardent supporter of higher education performance-based funding systems will acknowledge the education of a student and the awarding of a degree is far more complex and involves multiple individuals. Plus, not all entering students enroll with an equal set of skills, resources and abilities.

To ensure more equitable and appropriate use of performance metrics, colleges and states need to revisit current performance metrics and challenge related assumptions. An incorrect or incomplete set of metrics hurts student success, limits improvement efforts, and maintains funding inequities. For example, when a student transfers from a community college (without a two-year degree) to a four-year college, should that action count against the community college’s degree completion metric? Might that student have been well served by their time at the lower-cost college? When community colleges provide access to students most in need, should they be penalized for not graduating such students at a “high enough rate”? Might that student and that community have been well served by improved access to economic opportunities related to college enrollment?

Colleges and state systems should first partner to recognize the purpose of performance measurement is to help build capacity and improve performance for all colleges. Once the purpose has been set, these parties can then work together to define expected goals and create educational activities/policies to improve performance, and clearly define metrics and data collection strategies required to analyze performance.

Most importantly, states and colleges should link the analysis of performance metrics to clear and funded pathways for improvement. Funds to support improvement must be available to colleges across the performance spectrum from low to high. Using performance measurement to consistently direct funding to already high-performing colleges, while restricting funds to colleges who require additional resources to improve is not the answer. Such a system will continue to lead to inequities in funding mechanisms.

Capacity building and equity, not performance metric traps, should pave the pathway to improved performance. By stepping back to recognize the purpose of performance measurement is to help build capacity and improve performance in a more meaningful and equitable manner, states and colleges will experience a “Eureka moment”. This new-found purpose will open the door for colleges and states to measure what matters and use data for continuous improvement and equitable and efficient allocation of resources.

– – – – References

[1] Tiffany Jones & Sosanya Jones, ‘Can Equity Be Bought? A Look at Outcomes Based Funding in Higher Ed.”, The Education Trust, 2017, https://edtrust.org/the-equity-line/can-equity-bought-outcomes-based-funding

[2] Nicholas Hillman, “Why Performance-Based College Funding Doesn’t Work”, College Completion Series: Part Four, The Century Foundation, 2016, https://tcf.org/content/report/why-performance-based-college-funding-doesnt-work

[3] Eric Westervelt, “Shaken by Economic Change, Non-Traditional Students are Becoming the New Normal”, nprEd, How Learning Happens, 2016 https://www.npr.org/sections/ed/2016/09/25/495188445/shaken-by-economic-change-non-traditional-students-are-becoming-the-new-normal

[4] Education Commission of the States, “27 Is the New 18: Adult Students on the Rise, 2018. https://www.ecs.org/27-is-the-new-18-adult-students-on-the-rise

[5] Nicholas Hillman, “Why Performance-Based College Funding Doesn’t Work”, College Completion Series: Part Four, The Century Foundation, 2016, https://tcf.org/content/report/why-performance-based-college-funding-doesnt-work

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