In an earlier article, we described the University as a system with inputs such as incoming student’s’ characteristics, and outputs such as graduates’ characteristics.
While that framework has allowed us to focus our attention on what can be changed internally, (e.g. the curricula, student support services, quality of teaching, etc.) versus what is usually beyond the university’s direct control, (e.g. students’ readiness, traditional revenue sources, etc.), there is more to the story than a simple linear model. Borrowing again from systems theory, the university system has multiple inputs and outputs that interact and influence each other in a nonlinear fashion.
On the (Delayed) Impact of Changing Inputs
It is naïve to focus our attention on quickly improving graduation rates by simply paying closer attention to incoming students’ characteristics. At the University of New Mexico, we admit students from a variety of backgrounds and preparedness levels, from those who have cleared our minimum requirements to national merit scholars who have been admitted to the most selective schools in the world. It is tempting to simply filter out those students whose earlier academic performance points to low graduate rates, but even if one were to stretch the university mission, such a strategy would take three to four years to show any results and would also result in a short-term reduction in the number of admitted and matriculated students.[1]
If such a strategy were to be adopted by the board, the leadership and the faculty, it would need time to mature, due to the time delays in the system and to the initial decline in the number of students. The time delay is familiar to most people but seems to be regularly ignored as performance metrics for incoming presidents usually include unrealistic short-term goals (like improving the four-year graduation rate a certain percentage in the first year of presidency).
What’s more, the initial decline across many outputs that results from the change in an input is a characteristic of a non-minimum phase system, and leads to the abandonment of otherwise sound strategy because the immediate response was not as predicted. In fact, the longer-term effect of the aforementioned strategy would indeed be an increase in the number of academically ready applicants and ultimately an increase in enrollment as the reputational effects take hold.
Defining Success Is Not Straightforward
A closer examination of the system’s concepts, however, points to a glaring weakness of most current metrics for any university’s performance. The most relevant measurement for any university must certainly be its value added to its students’ learning and growth, such as the recent efforts exemplified in the Gallup-Purdue Index Reports. Other variables of interest include the efficiency and effectiveness of assigning resources as needed to deliver the highest possible value.[4]
It is unfortunate that most of these measures are not available directly and one has to rely on indirect measures such as reputational scores, graduation rates, job placement, etc. to deduce the positive effect of a university on its students. Absent an effective measure of what the mission of the university describes as important outcomes, we revert to measuring what can be easily counted and value what we can easily measure.
While new advances in learning science and big data are beginning to be introduced in this arena, most measures and metrics for the performance of a university remain fixated on outputs (what can be measured) versus states (what should be known).
How Networking Impacts the System
Last but not least, the university system is benefiting and suffering from the effects of networking in society and in organizations. To wit, decades ago, decisions were made in a hierarchal fashion and communicated through the chain of command from leaders through their organizational structures. Information also propagated in an orderly fashion and largely controlled by those with the biggest megaphones, usually at the upper end of the hierarchy.
Thanks to the advance of modern communication techniques and to social and information networks, information—and in many cases misinformation—can propagate in any direction. Information about decisions that were never made travel around campuses and towns before the intended recipient or the decision maker have had a chance to react or explain. Faculty, staff, students and external constituents now have the ability to affect change and to shape the narrative according to their own information and interests.
The new model leads to efficiencies but also require an adjustment in the reaction time of the university.
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References:
[1] See Flows under Students at: informatics.unm.edu
[2] Massey, W.F. Reengineering the University: How to Be Mission Centered, Market Smart, and Margin Conscious, The John Hopkins University, 2017.
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