Study proposes new model for calculating college productivity
Arguing that higher education must do a better job defining and tracking productivity, but that the current models for doing so are inadequate, the National Research Council released a book-length study Thursday suggesting a new set of metrics that would allow for a sectorwide look at productivity.
The study is in part a critique of outside attempts by some politicians and analysts to quantify educational productivity, and in other parts a challenge to colleges to overcome barriers to producing such measurements themselves (lest more outside efforts be imposed). The report outlines the limitations and potential pitfalls of tracking productivity, and concedes that accurate measurements for instructional quality -- without which greater productivity has little value, the report acknowledges -- can be elusive.
But in an era in which universities are expected to maintain or increase the number of students they educate with more limited resources, the authors contend that colleges must open themselves to such tracking in order to have an informed conversation about how productive universities are and whether that trend is improving or worsening.
“What we really want to do is restart the conversation about the measurement of productivity in colleges and universities along directions that are technically sound in an economic standpoint and make the best use of the data,” said William Massy, an independent consultant and a study panelist.
The model is not intended to be used at individual colleges or even small groups of institutions, but the study’s authors believe it could offer a valuable way to look at higher ed as a whole and to analyze large groups of colleges.
Productivity, as the report's authors define it, would factor in both credit hours completed and degrees awarded compared to labor costs and other expenses. By accounting for both degrees and credit hours, the study’s authors say, the model would reward institutions for graduating students without penalizing them for having large numbers of part-time students.
The model, which takes 192 single-spaced pages to explain, is no doubt complex. Some parts would be hard to implement immediately, including portions that would require the creation of a national clearinghouse of student information to account for factors like transfer students and future earnings. The model also calls for changes to colleges' federal data reports. Those changes could require federal action to allay concerns of privacy law violations. The eventual goal, Massy said, is for a federal agency to take over the project and publish annual findings. (Advocates for student privacy, Republican lawmakers, and officials of independent colleges have together opposed the creation of a national system of individual-level student records.)
This is far from the first proposed measurement of college productivity, but it’s unique because of who is making the recommendations. Past efforts to quantify productivity in higher education have typically come from nonprofit foundations or government officials, including a controversial effort to measure the output of University of Texas faculty.
The National Academies study was completed by three panels, all made up largely of professors and college administrators with a few members from foundations, consulting firms and a standardized testing company.
The goal of the study wasn’t to create an accountability system, the report says, and the model intentionally avoids approaches used in college rankings. Rather, the report seeks to account for the value of the education students receive after arriving at an institution.
In a perfect world, that model would also account for the quality of a student’s education. “The elephant in the room is what about quality,” Massy said. “There is no good quality measure. We wish there was such a measure, but it doesn’t appear we’re going to have an agreement on a good measure any time soon. As long as there is reasonable quality assurance by the standard methods, there’s no reason that having aggregate statistics of this kind is going to trigger a race to the bottom in terms of quality.”
Academe has traditionally been skeptical of attempts to quantify productivity, and the study’s authors acknowledge that different calculations might be needed to account for valuable work at research universities (where faculty might spend less time teaching) and at two-year colleges (where fewer degrees might be awarded if students seek to transfer or earn a vocational credential).
But getting hung up on the inherent shortcomings of productivity measurements, the authors write, discounts the real value such measurements can provide now and the improvements possible as models are tweaked and perfected.
“It is possible, and perhaps even likely, that critics of this report will rebuke the idea of measuring instructional productivity because of the complications noted … throughout this report,” the authors write. “Our view is that this would be a mistake. Failure to implement a credible measure may indefinitely defer the benefits achievable from a better understanding of quantitative productivity.”
And in the meantime, Massy said, the academy’s traditional sore spot about tracking productivity can evolve into a more meaningful dialogue.
“The positive reason for doing it is there’s an endless debate about whether productivity in the sector is increasing, decreasing or staying the same,” he said. “A set of data will help measure that -- it will steer the conversation about that into more productive lines.”