Advantages And Disadvantages Of Simulation Training And Fidelity Analysis

Introduction of Simulators for Training

1.

Discuss the advantages and disadvantages of using a simulator versus conducting live training with an actual tug and an aircraft.

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. 

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.

Advantages of Simulation Training

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 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.

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:

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.

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 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.

Disadvantages of Simulation 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.

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 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.

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.

2.

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:

Fidelity Analysis in Simulators

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.

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

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.

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.

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]).

References

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Edkins, G.D., 2002. A review of the benefits of aviation human factors training. Human Factors and Aerospace Safety, 2(3), pp.201-216.

Gaifullin, A.M. and Sviridenko, Y.N., 2018, March. Dynamics of the aircraft in a vortex wake. In Journal of Physics: Conference Series (Vol. 980, No. 1, p. 012005). IOP Publishing.

Ghasemi, A. and Zahediasl, S., 2012. Normality tests for statistical analysis: a guide for non-statisticians. International journal of endocrinology and metabolism, 10(2), p.486.

Hopkin, V.D., Wise, J.A. and Garland, D.J., 2016. Handbook of aviation human factors. CRC Press.

Kua, S., Baikb, H. and Yunc, S., 2017. Air Sransportation Simulation to Runway Incursion Safety. International Journal of Applied Engineering Research, 12(22), pp.12409-12414.

Liu, D., Blickensderfer, E.L., Macchiarella, N.D. and Vincenzi, D.A., 2008. Transfer of training. Human factors in simulation and training, pp.49-60.

Markkula, G.M., Romano, R., Jamson, A.H., Pariota, L., Bean, A. and Boer, E.R., 2018. Using driver control models to understand and evaluate behavioural validity of driving simulators. IEEE Transactions on Human-Machine Systems.

Rantanen, E.M. and Talleur, D.A., 2005, September. Incremental transfer and cost effectiveness of groundbased flight trainers in university aviation programs. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 49, No. 7, pp. 764-768). Sage CA: Los Angeles, CA: SAGE Publications. Sheets, T.H. and Elmore, M.P., 2018. Abstract to Action: Targeted Learning System Theory Applied to Adaptive Flight Training. Air Command and Staff College, Air University Maxwell AFB United States.

Taylor, J.L., Kennedy, Q., Noda, A. and Yesavage, J.A., 2007. Pilot age and expertise predict flight simulator performance A 3-year longitudinal study. Neurology, 68(9), pp.648-654.

Tee, Y.Y. and Zhong, Z.W., 2018. Modelling and simulation studies of the runway capacity of Changi Airport. The Aeronautical Journal, pp.1-16.

Wickens, C.D., Hollands, J.G., Banbury, S. and Parasuraman, R., 2015. Engineering psychology & human performance. Psychology Press.