Advantages and Disadvantages of Using Simulators for Training and Simulation Models for Efficacy Determination

This article discusses the advantages and disadvantages of using simulators for training and evaluates the suitability of different simulation models for determining efficacy. It also includes a statistical analysis of data obtained from a trial comparing two simulators.

Running Header: Training and Simulation 1
Training and Simulation
Students’ name:
Student’s ID:
Institution:

Training and Simulation 2
Question 1
a) Discuss the advantages and disadvantages of using a simulator versus conducting
live training with an actual tug and an aircraft.
Advantages of simulators
Using simulators has numerous advantages compared to conducting live training with an actual
tug and an aircraft. For starters, using simulators ensures that there is an increase in efficiency
(Gafullin & Sviridenko, 2018). Training is conducted in simulators are not affected by various
constraints, both predictable and unpredictable. Moreover, simulators make it possible for a
trainee to repeat maneuvers without incurring extra costs and time. Simulators provide a variety
of environments or geographical areas using different airports in one location (Sheets & Elmore,
2018). Further, the simulators can provide conditions at any time of the day regardless of day or
night.
Simulators also ensure that there is increased safety during the training. Trainees can be
undertaken to gauge their response to emergencies during their initial and annual refresher
training. The training, such a non-normal and emergency procedures and system failures would
be very unwise to be performed in flights unnecessarily.
In terms of costs, simulators provide lower training costs. The simulators can be purchased and
operated on cheaply compared to a large commercial aircraft (Tee & Zhong, 2018). The need to
minimize cost has directly led to developing zero-flight time training for transforming qualified
pilots onto new types of aircraft. Thus, it has pushed or Annual Instrument Rating Tests in to be
conducted in simulators.

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The environmental impact of simulators on the environment is very minimal (Dautermann et al.,
2017). The simulators offer a reduction in greenhouse gases emissions and elimination of noise
pollution especially around airfields during night flying.
Unlike conducting of live training with an actual tag and an aircraft, simulation allows for the
trainee and the trainer to log, replay and review performance so as to provide feedback
instantaneously. Simulators permit the adaption of a richer instructional strategies variety.
Instructors can also intervene at critical points during an exercise through freezing a simulation
in order to exercise and review the situation with the student instead of waiting till the tail end
which results in a significant reduction in time that is non-productive. The progression of the
students can also be logged on.
Simulation purposely offers trainees to undertake activities that are high-risk or tasks that are
procedural within a safe environment without implications that can be dangerous (Edkins, 2002).
Consequently, the trainees can comprehend greatly with regards to the consequences of their
actions and the need to reduce potential errors (Markkula et al., 2018). Simulators also give
trainees the advantage of getting hands-on skills and also thinking skills. The skills include
knowledge in action, decision making, effective communication, and procedures.
Disadvantages of simulators
Though the simulators have many advantages, the simulators are plagued with the disadvantage
of fully replicating the real world in the simulator. Consequently, the simulators can prove to be
very expensive as they may require constant maintenance and updates (Kua et al., 2017). On the
other hand, not every situation can be included. Time and costs can also rise significantly since
the staff will be needed to be trained on how to use the hardware and the software. The

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effectiveness of the results and the feedback are also possible as the actual training that is
provided. Finally, the safety aspect of the simulators can prove to be a major problem for
graduating trainees (Hopkin et al., 2016). The trainees will not have experienced any real
consequences for the mistakes and may, therefore, result in students underperforming as they
will not be able to fully engage in the training and thereby producing results that are inaccurate.
b) Critically evaluate the suitability of following simulator evaluation models for
determining the efficacy of the simulator for training for both normal operations
and the handling of emergencies and make a recommendation about which model
or combination of models you would recommend to AeroTugs for this purpose:
Analytical Models:
Fidelity analysis
Simulator fidelity analysis model is based on the notion that the greater the simulator’s fidelity,
the greater the conducted training effectiveness (Longridge, et al, 2001). Fidelity analysis is
aimed at identifying critical aspects of fidelity that are missing. The method is valuable even if it
seems to step out of the more contemporary models of the relationship between transfer and
fidelity.
Opinion Surveys Performance Evaluation Models:
Backwards transfer
A backward transfer entails the performance of the same skills of a skilled operator who has
mastered the skills that should be in calculated in the operational aircraft into the simulator
(Wickens et al., 2015). When the skilled operator is able to perform these skills without practice
in the simulator then there will be an occurrence of a backward transfer. The transfer from the

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simulator to the aircraft will there be progressive. The backward transfer approach has a potential
flaw due to the nature of the skill set of the operator’s experience. A pilot who is highly skilled
and experiences will be able to perform adequately in a reduced cueing environment which will
be wholly unsuitable for training (Gaifullin & Sviridenko, 2018). As a result, when there is such
an inadequacy in the cueing environment, it may not be open in this evaluation type.
Consequently, it will be possible that the reason why the operator has the potential to perform to
an acceptable standard in the simulator is that they have a more generalized skill which will
enable them to overcome any limitations of the simulator. However, if the operator cannot
perform well in the simulator then there is likely to be something crooked.
Transfer of training
It is paramount that training in the simulator should be transferred positively to the real
environment. A transfer that is positive should occur when the performance in the aircraft
improves as a result of the training (Liu et al., 2009). A negative transfer occurs when the
delivered training actually degrades the performance in the live environment. The issue of
simulator fidelity is of interest since one of the costs of the training is its effectiveness (Rantanen
& Talleur, 2005). Lower level training devices are cheaper to purchase and operate compared to
the Full Flight Simulator (FFS). Thus, many arguments relating to the use of appropriate
technology for curriculum delivery is based on the particular training requirements which may
not always be an FFS.
Simulator to Simulator (Quasi-transfer)
The simulator to simulator model or the quasi-transfer evaluates the performance of the students
in a higher fidelity simulator than the one being evaluated. The model’s validity is based on the

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equivalence assumption between the higher fidelity simulator and the aircraft. The assumption
may be tenuous in certain circumstances and data that may be erroneous may be produced. The
approach is valid where the higher fidelity simulator is the environment that is targeted such as a
part-task training device that is designed to reduce time spent in the simulator.
Simulator performance improvement
The simulator performance improvement is based on the carrying out of pre-and-post training
performance appraisal in the simulator. The performance improvement is accredited to the
gained knowledge in the simulator (Taylor et al., 2007). The aircraft has no evaluation, thus a
trial of this kind provides only relatively weak evidence as to the possibility of transfer and
thereby the efficiency of the simulator. The model acts as a deleterious filter similarly as some
other models. If there is no improvement in the performance, then it will be unlikely to positively
transfer the skills to the aircraft. In-simulator performance enhancement is a situation that is
necessary for efficient but does not provide a definite confirmation of the transfer that takes
place. The model suffers from all the same weaknesses as the self-control model.
Therefore, due to the dynamism of the models, AeroTugs should opt for a combination of quasi-
transfer and a backward transfer.

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Question 2
a) State the independent and dependent variables for this trial and the hypothesis.
The introduction of a new simulator is aimed at increasing fidelity compared to its predecessors.
Data was obtained between two groups with one being trained on the new simulator whilst the
other on the existing simulator. Therefore the developed hypothesis will be:
H1: There is no difference between the response speed between the new simulator and the
existing simulator.
Based on the nature of the data collected and the type of hypotheses developed, the data did not
have any dependent variable as all variables were independent of each other. Thus, speed
response from the new simulator and response time from the existing simulator are all
independent variables.
b) Produce descriptive statistics tables for the data for each group and, considering the
skew/kurtosis of the data and the variances, determine if the data can be evaluated
by a parametric or a non-parametric test. State your conclusion and the rationale
behind it.
Old simulator
New
Simulator
Mean 40.14 64.06
Median 39.5 65.5
Mode 38 66
Standard
Deviation 10.09 12.36
Kurtosis 0.57 0.34
Skewness 0.09 -0.41

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The mean of the old simulator is 40.14 speed response with a standard deviation of 10.09. On the
other hand, the new simulator had a mean of 64.06 speed response with a standard deviation of
12.36.
The old simulator and the new simulator had a kurtosis of 0.57 and 0.34, thus, meaning the two
variables are close to a normal distribution. The skewness of the old simulator is at positive 0.09
while the new simulator has a negative 0.41 skewness. Since the values are close to 0, then the
variables moderately and fairly skewed respectively. Thus, the variables are close to a normal
distribution.
Since the variables are normally distributed, then a parametric test will be carried out on them
(Ghasemi & Zahediasl, 2012). Thus, a paired sample test will be carried out on them.
c) Perform an appropriate test to determine if the data supports the hypothesis. Your
answer should include the appropriate output table(s) from the test you conduct and
your results should be reported in an appropriate format.
Paired Samples Correlations
N Correlation Sig.
Pair 1 Old Simulator & New Simulator 50 .104 .471
From the table above, it can be seen that the speed response between the new simulator and the
old simulator are weakly and positively correlated. However, the correlation is not statistically
significant at p = 0.05.

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Paired Samples Test
Paired Differences t df Sig. (2-
tailed)Mean Std.
Deviation
Std. Error
Mean
95% Confidence Interval of the
Difference
Lower Upper
Pair 1
Old
Simulator -
New
Simulator
-23.92 15.117 2.138 -28.21 -19.62 -11.19 49 .00
The table above shows that there was a significant average difference between the old simulator
response and the new simulator response (t49 = -11.189, p < 0.00). On average, the new
simulator speed response was 23.92 higher than the speed of the old simulator (95% CI [28.216,
19.624]).

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