The ways in which pilots train in the future will look very different to the ways in which they train today. This white paper from Emirates and CAE builds upon the ideas presented in the previous White Papers from CAE-Emirates on the future learning ecosystem for pilot training framework which outlined the main ideas and concepts to enable for the pilot training of tomorrow. It further develops the discussion for how Simulation for Experiential Training (SET) can be used as a means for Evidence Based Training (EBT) outlining ideas for the assessment of competencies using SET and how competency can be evaluated within this framework.
Here is a summary of their findings.
Data from training can provide different and critically important perspectives for EBT. SET provides limited but a more focused framework as a tool for EBT than operational and simulator data from more advanced simulators. SET provides a controlled and a less complex platform where performance data can be selected rather than filtered from a large amount of data. With flexible and clever design of scenarios, the recorded interactions with the SET interface should be able to be translated into competencies.
In summary the design principles were as follows:
Here the focus is placed upon how to practically make use of the parameters for the assessment of competencies, in particular:
The following is an example of the above parameters in action as illustrated in the Oceanic Fuel Leak SET which was deployed within Emirates Recurrent CRM training. Briefly, the scenario is modelled on the accident that occurred to the Air Transat Airbus A330 at Lajes, Azores, on the 24th of August 2001, where a leak of the complete fuel supply resulted in both engines shutting down mid-ocean.
The trainee(s) is clicking around, getting familiar with the interface, and is exploring functions. At the same time thinking about contingencies normally start, which leads to exploration (or at times over-exploration) of available airports for diversion, including weather, NOTAMs etc.
The problem event starts and requires that it is identified as soon as possible. By design, the simulation indicates symptoms of trouble a few minutes before the system directly warns the trainees that there is insufficient fuel to reach destination. This incremental fault indication is typical of complex systems, especially where there is a computer system monitoring the overall “health” of the system. The designers of these monitoring system are striving to reach a balance of not over or under alerting the operator. When discovered, there is ongoing parallel work with understanding the problem, finding a way to manage it and then arriving at a diversion option that is achievable with the remaining fuel and within the appropriate risk tolerance of the trainees.
At this point the trainee(s) has identified the existence of the fuel leak and are trying to understand how to mitigate the effects. The results of their efforts will have a direct effect on their diversion options. The key to this phase is to determine where the leak is coming from, in general this is one of two locations: the tank or the engine area. A leak in the engine area is usually resolved by shutting the engine down, whereas a tank leak is dealt with by isolating the affected tank. In this case, the leak is from the engine and the trainees have to deduce this by using their basic knowledge of aircraft systems. The simulation is configurable, so the leak originates from the either the tank or the engine. A tank leak gives the trainees less diversion options since any fuel located in that tank will usually be considered lost. There are a number of hazards which the trainees have to contend with. For example, if the fuel pump is turned off without ensuring the crossfeed is opened, after a few seconds, the downstream engine will flame out.
The diversion consideration is essentially an exercise in trainee risk management. There is not a “best place” to go to, but rather a “least worst” diversion airport. Each of the available airports have NOTAM, weather, landing performance or locality hazards (isolated islands for example) which have to be assessed.
As has been considered previously, gathering this type of trainee data allows a second dimension of potential feedback to open-up. This would allow provision of feedback to trainees on their performance in relation to other trainees, expanding the usefulness of the data. Receiving data-driven feedback that you are among the 10% that were fastest, slowest, to make the diversion decision should be important information, also perhaps prompting a conversation with an instructor. There may be good reason for this: However, if any trainee consistently ends up in the 10% categories, then further investigation and consideration of the reasons why this has happened should be required.
This paper continues the development of applying competency scoring for EBT within SET. It describes various possibilities for scoring but will have to be discussed with the developers of the SET as well as with instructors/pilots delivering the training. That said, the potential for Competency Scoring in Simulation for Experiential Training (SET) is extremely promising and technology for training will continue to develop in this direction in the future.
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