this view, noting that the production processes for colleges and universities rely on human interaction (at least traditionally), nearly fixed amounts of time inputs from faculty and students, and a key role for highly educated, highly compensated employees.

Even when steps can be taken to increase throughput, questions rightfully arise about the effect of the changes on quality. Archibald and Feldman write (2011:40):

An institution can increase class size to raise measured output (students taught per faculty per year) or it can use an increasing number of less expensive adjunct teachers to deliver the service, but these examples of productivity gain are likely to be perceived as decreases in quality, both in the quality rankings and in the minds of the students.

However, the evidence on the potential of higher education to benefit from new models of production, such as online courses, is not conclusive. Harris and Goldrick-Rab (2011) argue that “researchers and institutions themselves have rarely paid much attention to whether policies and practices are cost-effective. How would you know whether you’re spending money effectively if you’ve never even asked the question?” They conclude that colleges “can conceivably become more productive by leveraging technology, reallocating resources, and searching for cost-effective policies that promote student success.” Indeed, many industries that formerly were believed to be stagnant have been able to improve productivity dramatically. Even in the quintessential example of Baumol’s cost disease (noted above), string quartets have improved “productivity” dramatically through the capability to simulcast a performance to theaters or, more obviously, by recording their music and earning money while the musicians sleep (Massy, 2010:39). Other examples can be found in medical care, legal services, and elsewhere.

Work by the National Center for Academic Transformation (NCAT) on course redesign provides a contemporary example of what can be accomplished in this area (see Chapter 2 for a description of some of this work; see also Appendix B on NCAT’s methods). The organization’s clients analyze measures to determine new ways to combine inputs so as to produce student credit hours of the same or better quality than with traditional methods. Quality assurance also enters the process. Indeed, the changes that have been made following such analyses are the classic ones used in essentially all industries: shifts from high-cost to lower-cost labor, more intensive use of and better technology, and elimination of waste in resource utilization.

The idea that instructional productivity may potentially be increased by altering the way inputs in the production function are combined highlights why improved measurement is so important. Potential improvement in productivity also justifies requirements that colleges and universities systematize collection of data

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