Physicist Niels Bohr once said, ‘prediction is very difficult, especially if it’s about the future’. Despite the difficulties, we can safely assume advances in technology will reshape the future training programmes of aviation professionals. This article addresses four teaching technology trends.
Due to mandatory recurrent training cycles and the resulting escalating costs, the aviation industry has become an innovator and early adopter of training technologies. From the introduction and evolution of the flight simulator to being the first industry to widely adopt computer-based training, the aviation industry is quick to embrace technologies that may lead to enhancements.
#1 – How will the next generation of professionals learn?
Before exploring new technology, we must consider how future professionals will approach learning. Today’s students are not the ones our training methods were designed to teach – because they grew up immersed in technology, they have different approaches to learning. While many experienced instructors assume that learners are the same as they always have been, and the same teaching methods that worked for us will work for them, the assumption is no longer valid. The next generation learns differently. They are:
- multi-taskers, collaborative and team-oriented;
- ‘native speakers’ of technology; embracing simulation, interaction, and gaming; expecting immediate gratification; and
- demanding knowledge
This is not a panic-worthy situation since there is evidence that young people seem to conform to the approach used by their instructors. But as we move towards the future, we must continue to assess the effectiveness of our teaching and ensure the curriculum is learner-focused.
#2 – Wearable technology
Wearable technology refers to the portable devices that attach to the human body, collecting data and delivering information to the wearer. While these are new tools in aviation training, these devices are increasingly prolific given that there were 526 million wearable devices around the globe in 2017, a number that grew from 109 million in 2014. Wearable devices have many of the same features as mobile phones but offer the enhancement of scanning features and sensors.
The data collected from them can be used in a range of applications:
- augmented reality helmets (like Google Glass) superimpose digital information over a person’s view of the real world;
- wearable smart-clothing senses a variety of health data (respiration, heart rate, body temperature, etc.) ; and
- fitness wristbands track movement, calorie expenditure, sleep quality and quantity.
These types of technologies may impact aviation training since the effectiveness of instruction will always be linked to a learner’s physiological state. For example: did these trainees get sufficient sleep; are they impaired by substances or medications; are they stressed or overwhelmed by information; or are they physically overworked?
Some applications within aviation training may include:
- eye-tracking devices that sense stress levels associated with cognitive load – theoretically identifying when learners have achieved a level of competency with new material; and
- augmented reality systems that superimpose textbooks, media, or systems and components to:
- replace air traffic controller’s paper flight strips with a digital presentation of the information superimposed on their workspace;
- present maintenance personnel with a digital view of systems that may not be visible because they are obstructed by covers or other components;
- display the name and preferences of passengers to cabin crew as they walk through the cabin; and
- generate a digital representation of the ideal approach path for pilots
Wearable technology also has limitations associated with costs and privacy, and is dependent on the willingness of the individuals who will be asked to use the devices. Google Glass is an example of a product that was rejected because people felt they ‘looked goofy wearing them’ and because they had a short battery life. Google recently discontinued Google Glass, going back to the drawing board to rethink their devices.
#3 – What is ‘Big Data’ and how can it drive teaching?
Mobile devices play an important role in our personal and professional lives. Just as Hansel and Gretel left a trail of breadcrumbs as they walked down the forest path in the Brothers Grimm fairy tale, each of us is generating a stream of data that can be thought of as ‘digital breadcrumbs’. As we move about throughout the day, we generate data points associated with where we were, who we spoke to, and anything we documented.
Beyond the data that is generated by individuals, massive amounts of data are created and recorded by machines. Google presents a good example of this in how they are able to detect disease outbreaks based on the number of illness-related web searches in a geographic area. We have entered an era where data will be measured in zettabytes; society is generating an enormous and ever-increasing amount of big data. To put this into context, if your extra-large cup of coffee represented the volume of one gigabyte, a single zettabyte would be equal to the Great Wall of China!
On a global scale, this has created a new industry that seeks to generate, analyze, and sell insights from the massive pool of big data using proprietary mathematical algorithms. Although there are concerns associated with privacy and the accuracy of findings, applications of big data are already being used in aviation training:
- proactive safety management programmes use cluster analysis on routine operational data from Flight Data Recorders to identify anomalies at specific airports and assign training content;
- machine-driven learning algorithms that continually analyze data from simulated and line-operation scenarios to understand individual training needs and allow individuals to see and understand where their performance is relative to the norm; and
- recruitment and selection practices based on the comparison of applicant attributes against competencies demonstrated by top-performing employees through predictive analytics.
In the future, big data is likely to impact the types of employees hired and the training they are provided throughout their careers. Although this leads to interesting training customizations, it is important to note that the quality of findings from big data is entirely reliant on the quality of information it receives. This is an exciting, but far from foolproof, innovation.
#4 – Adaptive eLearning
Beyond the use of big data to identify training needs within an organization, data can also be tapped into on an individual basis to drive the curriculum of training. Where traditional static e-learning targets the 50th percentile (the average learner), adaptive eLearning customizes the content to the learner based on their individual needs and abilities.
As technology evolves, new electronic courseware emerges. The training is designed to adapt to the needs and learning style of each individual learner so that they can achieve the highest level of learning possible, based on their unique intellectual capacity. Adaptive learning broadly refers to any educational computer programme that utilizes some type of artificial intelligence to guide the structure of the curriculum.
Interestingly, one of the first practical adaptive e-learning courses was developed within aviation. In the mid-1980s, F-15 avionics technicians presented a training challenge. Because they worked in their positions for a brief period, carrying out mostly routine tasks that were well supported by technology, their work did not allow for the opportunity to develop complex problem-solving skills. This left the Air Force with a problem – training assumed that test-station-repair troubleshooting would be learned on the job, but the job did not offer enough practice opportunities. A computer-based Air Force electronics practice tool called ‘Sherlock’8 was created as an environment where avionics technicians could practice troubleshooting skills – and it was extremely successful. Novice technicians who practiced on Sherlock for 20-25 hours developed troubleshooting skills comparable to their colleagues with four years of on-the-job experience.
Although adaptive eLearning offers great potential to improve learning, there are downsides. Development is significantly more expensive than traditional static eLearning, and it can’t automatically sense when a learner is becoming frustrated or overwhelmed. But new research is incorporating emotion-sensing technology using computer webcams – so this may change in the future.
Since adaptive eLearning will likely, eventually, become a training component of training for all aviation professionals, this technology should be regarded as a compliment and an extension, rather than a replacement, of existing classroom, simulator and real-world teaching practices.
As new technologies are quickly integrated within our daily lives, new devices and methodologies will follow them into our training centres. But, it is crucial to remember that new technology does not necessarily result in more effective training. In considering the earliest days of computer-based training, many of the organizations that were first to deploy eLearning produced very low-quality ineffective courses because little was known at that time about how to make this type of training effective. To learn from the mistakes of the past and ensure they are not repeated, a cautious approach to the incorporation of future technology must be adopted. This will ensure technology effectively improves learning – before it is fully deployed.
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