3-11--2011
Florida A&M University
Program Productivity Methodology
Introduction
In Spring 2009, the Academic Affairs work group initiated the process of determining the
productivity levels of the academic programs within the University. This was deemed
necessary as we addressed issues of budget cuts, efficiency and effectiveness.
Methodology
The guiding question for this activity was: "How can the University's scarce resources be
used most effectively to serve the needs of students and faculty, and the institutional
mission?"
The initial task of the work group was to develop a process for assessing the multiple
factors that contribute to the productivity of academic programs. A decision tree was
developed which provided a basis for reviewing the academic programs. This framework
enabled the work group to conceptualize important factors of productivity and filter the
programs through a series of steps designed to address questions of productivity. As a
result of faculty input, the decision tree was revised (see Attachment 1) and it led to the
development of steps and identification of factors that were utilized later to score each
program. The decision tree was also a guide in considering low-scoring programs for
suspension, termination or for actions to increase productivity.
As a result of discussions within the work group, the University Budgeting and Planning
Committee and with the academic deans, it was concluded that examination of data was a
critical starting point for discussions and considerations. Data within degree programs in
the following areas would be considered:
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Enrollment of majors by level
Degrees awarded by level
Student FTEs by level
Sponsored research awards
These factors were selected because they are standard data used by most institutions
conducting program productivity reviews. Further, these factors impact program revenue
and accountability. Objective data for each of the factors were available through the
University data files used to report the official institutional data to national and state
agencies.
In addition to the above factors, the University sought input from the academic units.
Upon convening focus groups in the academic areas, consisting primarily of faculty,
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participants identified a number of factors to be considered in productivity. All but three
were already part of the methodology. The three additional considerations that were
recommended by the focus group participants were:
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Productivity per faculty
Return on investment
Productivity in relation to similar programs at peer institutions
These additional factors were integrated in the subsequent analyses as described below.
Using the data identified above, the following steps were used to determine program
productivity.
1. Scoring model
All academic programs were rated based on the following factors:
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Enrollment of majors
Degrees awarded
Student FTEs produced
Sponsored research awards
Cost per credit hour*
Return on investment (ROI) on research*
* The last two factors (cost per credit hour and ROI research) were added based on
recommendations of the academic focus groups.
The work group’s initial analysis, using the first four factors, led to a listing of programs,
ranked from low to high productivity scores. Programs with low scores, possibly
indicative of low productivity, were addressed first. All aspects of the low scoring
programs were reviewed. Deans were provided with the scoring results and were
provided with opportunities to provide additional information to justify outcomes or
recommend changes in the identified areas that emerged with low scores.
Upon adding the last two factors, based on focus group recommendations, all the
programs were scored again, and ranked anew from low to high productivity scores.
Following is a brief description of the factors. Bachelor’s degree measures on
enrollment, degrees awarded and FTE were weighted by one (1), master’s degree
measures by two (2), and doctoral degree measures by three (3), corresponding to the
increasing levels of state funding for the respective degree levels.
For a given program, data in each of the factors enrollment, degrees awarded and FTE,
were multiplied by the appropriate weight for the degree level and the resulting number
was assigned a score. Sponsored research awards were also assigned scores depending
on the size of the award.
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Cost per credit hour was considered as an indicator for productivity per faculty as it
normalized the credit hours in relation to the instructional cost of a discipline, and the
cost was largely determined by faculty salaries. Data for cost per credit hour were
calculated from data in the University’s and the State University System’s (SUS)
Expenditure Analysis Reports. For each discipline category, by degree level, the direct
cost per credit hour for FAMU was compared to the system average for the discipline. If
the FAMU cost was lower than the system average, points were added to the program
score. If the FAMU cost was greater than the system average, points were subtracted
from the program score.
ROI data for research was calculated using the sponsored research awards for each
discipline category and FAMU’s Education and General (E&G) expenditures for
research in the discipline. If the discipline ratio of awards to E&G expenditures was
greater than one (1), points were added to the program score. If the discipline ratio of
awards to E&G expenditures was less than one (1), points were added to the program
score.
All of the above led to a composite score for each program. The specifics on the scoring
and an example are provided in Attachment 2.
2. Model combining indicator of societal need with productivity
Societal need was indicated by whether a program was on the Board of Governors
targeted list, in areas of critical needs in education and health, STEM fields, security and
emergency services, and globalization. These are also the programs that FAMU has
identified as priority areas that the University wishes to build upon. If a program was on
the list, it was designated as high need; if not, it was designated as low need. The
combination of the indicator of societal need and program productivity (from the score
derived from item 2 above) resulted in all programs being assigned to one of four (4)
quadrants:
i.
ii.
iii.
iv.
high need, high productivity
low need, high productivity
high need, low productivity
low need, low productivity
The programs in the FAMU-FSU College of Engineering were not placed in the
quadrants, because of the complexities involved as a joint college in which both FSU and
FAMU offer identical degrees and share all resources.
Additional Dimensions
Assessing the productivity of programs is a complex task, that cannot be confined purely
to quantitative measures. Therefore, upon assigning each program to one of the
quadrants in item 3 above, further analyses were conducted, using multiple filters, many
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of them qualitative in nature, in an attempt to ensure that no important factors were
overlooked prior to recommending suspension or termination of some programs. The
additional factors include:
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Programs that are central to the FAMU mission;
Programs that represented the strengths of FAMU and priorities for the future;
Programs that provided significant potential for the future growth through radical
redesign;
Comparison to productivity of similar programs at peer institutions (the third
factor recommended by the academic focus groups).
Next Steps
Initially, programs in the first (high need, high productivity) and fourth (low need, low
productivity) quadrants will be identified. After careful scrutiny outlined in the steps
above, including the additional dimensions, some programs in the fourth quadrant will be
recommended for suspension or termination. Information will be collected on the
number of students majoring in the program who will be affected, if any, a plan and a
timetable to teach out the students. Subsequently, over a period of several months, the
programs in the third quadrant (high need, low productivity) will receive further scrutiny
to determine if any of them should be recommended for suspension or termination.
Low productivity programs that remain after this careful review will be asked to provide,
within two months, action plans for increasing productivity and to implement those plans,
once approved. The implementation of these plans will be monitored over a two year
period with the expectation that enrollment data will demonstrate a significant increase.
At the end of the two-year period, the programs will be reassessed for continuation.
The slate of academic programs an institution offers is one of the most important factors
that determines the future of an institution. As we engage in the restructuring process,
decisions regarding maintaining, suspending or terminating programs must be made with
thoughtful consideration of multiple factors and a clear vision of the future.
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