I__. - International Military Testing Association

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In the TDS R&D work, training time models were developed from SlMEs’ judgments
concerning training times in various training settings required to reach full proficiency. This
approach proved satisfactory for ongoing MPT planning applications (Vaughan, et al, 1989).
However, it poses several problems for the weapon-system-design application. First, it
requires SMEs who are familiar with training on the subject tasks. For a new weapon
system, there are no SMEs with “hands on” experience with training. This is a common
problem in Logistics Support Analysis (LSA); the usual solution is to find extsting systems
that are comparable to a new system. Data and SMEs are used for the existing comparable
systems. In general, this approach involving comparable existing systems could be used to
estimate training time models for the new weapon system tasks. However, because the new
system often makes use of technology not incorporated in any comparable existing system,
some of the new tasks have no counterparts on existing systems. Thus,. the comparable
e.xisting system approach is not entirely satisfactory for our use.
Experimental Approach
In the weapon-system-design application, the TDS should be sensitive to design changes
and should provide feedback to designers concerning which features or aspects of their
designs are the primary drivers of training requirements. The training-time-modeling method
does not identify which task features or characteristics determine a task’s training time model
and cannot provide feedback to designers concerning how change a design in order to reduce
training requirements. That method would rely entirely on SMEs’ judgments based on global
task experience to obtain the training time models. As a consequence, the method is not
likely to be very sensitive to impacts of design changes on task training times.
Equation 1 is the model that we used on the TDS R&D to estimate the training time
curves such as illustrated in Figure 1:
p = ac,hc, + acIzhcl* + a,h,, + acorLhmrZ + aSohho + ahoZhho2 + ao,~holl
+ ao,12ho,12
[Equation l]
where p = relative proficiency, a:s = regression weights, hi’s = training hours in various
training settings, and subscripts for training settings are defined as:
cl = classroom,
car = correspondence course,
ho = guided hands on (Air Force field training detachment courses, etc), and
ojt = on-the-job training.
The model of equation 1 has several features. First, it has no additive constant; zero
training hours produces zero proficiency. Second, each curve of Figure 1 corresponds to the
second-order polynomial equation section of equation 1 associated with a particular training
setting. Third, the polynomial equation segment associated with each training setting is
neg:tivelqr accelerated. All eight model parameters associated with a particular task were
estimated simultaneously in a single regression analysis.
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