R. Brent Powell, Ph.D.
Adjunct Professor Human Resource Management
Houston Baptist University
And
Vice President
AEL System, LLC.
3000 Alta Mesa Blvd, Suite 150
Fort Worth, Texas 76133
USA
Human Resource Management has captured the idea of knowledge as a resource, however, the increasing age of the workforce, is leading to an experience deficit that is not being addressed by current use of knowledge management systems or human resource policies. This paper will address aging workforce issues, knowledge and experience retention, and the rapid transfer of usable experience..
In the next five years, millions of years of irreplaceable experience will retire from critical manufacturing, industrial, defense, transportation, finance, public administration sectors, and educational institutions. Many of the people will have been in place 30 years or more and know the ins and outs of their businesses and jobs at a level of mastery. The veteran knowledge and experience required to make operations run smoothly will retire as well. Without that knowledge and experience, error rate increases will be followed by cost increases as the masters retire. To prevent this new wave of challenges from becoming debilitating, knowledge and experience both must be captured and, as importantly, transferred rapidly and repeatedly to other personnel.
The focus for the last several years for both productivity and security has been high technology. Robots do more precise welding, security cameras never doze-off, and the saying; “he who controls the data controls the world”has become a bylaw. While our attention has been on improving hardware and software we seem to have forgotten about peopleware: the people who glue it all together. The people who operate and fix the machines, the people who watch the cameras, and the people who enter the data may be in dire need of an upgrade. The upgrade requires increasing knowledge and experience (KE) and while knowledge is recognized as an important resource, the experience to use that knowledge will be in even shorter supply. Looking at the impact of the absence of KE on business and governmental processes, it can be seen manifesting itself several ways. The areas to look for are: an increase in common process mistakes such as missed steps, a shortage or abundance in stock materials due to unanticipated needs, a drop off in inspection quality and efficiency, and increased time in troubleshooting and decision making.
Since the mid ‘80s a knowledge and experience gap has been expanding. The growth has been slow and subtle and disguised by things like the dotcom boom and bust, but nonetheless, the gap is growing to critical proportions.
To understand the full impact of the KE Gap we should first understand its business and industrial significance. Experience, by its very nature, takes a long time to acquire. In many of the heavy industries, personnel can take 15 to 25 years to really know, at a level of unconscious competency, how to anticipate problems or at least solve them quickly. Interviews with training and human resource professionals at international corporations like John Deere, Caterpillar, Freightliner, Halliburton and many of the defense contractors, have identified an aging workforce. The average age in that workforce is over 50 in the United States. In European countries, reports from Eurostat, UNESCO, and others provide information differently but point to a similar trend.
According to reports from the TOSCA D6 Symposium, compiled in September of 2002, in some countries as much as 99% of new job creation is in service industries. Those proceedings also report the average job stability in non-service sectors at approximately five years. From these reports and the well-documented turnover of employees in the service industries, it may be inferred that the manufacturing and industrial segments of the economy of many European nations have an aging population as well.
Those industries that had a stable workforce are now faced with losing that workforce, the experience they acquired, and the stability that KE provided, due to retirement of the baby-boomer generation. The impact of a rapid transition to significantly less experienced personnel has been compared to the impact of the Black Plague on available workforce.
The single most important aspect of quality performance is experience. A person who has experience has both knowledge and the ability to apply that knowledge in situations where accuracy, quality, and reliability are necessary.
Traditionally, experience is acquired through trial and error over time. Accelerated Experiential Learning is a process that increases the amount of knowledge acquired by an individual per unit time. AEL uses the brain’s preferred learning methodology and supplies the experiential data, eliminating trial and error.
Throughout life, the brain acquires information in the form of pictures and sounds. For purposes of this discussion, those pictures and sounds will be considered data. A person is considered experienced when they have acquired enough data to understand how the individual pieces relate to each other, and can apply what they have learned to the environment around them. The broader the depth of experience, the more knowledge, the more data, and the more time practicing the application of that data, the faster and more accurate a person becomes.
Experience is not simply the acquisition of knowledge. An individual can know a great many facts and have no practical knowledge in application. In academic circles, a great breath of knowledge in a subject area and the ability to apply it is often referred to as mastery. All employers will agree that personnel who reached a state of mastery are more valuable to their business. Trainers and educators will agree that the time and budget is not available to raise enough people to mastery.
The AEL process was derived by more than 10 years of observation and research. For a practical solution to providing the time needed for mastery, the process needed to meet several criteria. First, months of learning had to be reduced to minutes. Second, the learning process had to be applicable to a variety of subjects. Third, it had to be cost-effective. Four, the process needed to be independent of literacy levels and native language. AEL succeeds with all four criteria.
While the human brain is collecting pictures and sounds, it stores them haphazardly. It is not until much later, after a critical threshold of information has been gathered, that the brain begins to sort and identify pieces of related data. An extreme simplification of the process would be to consider going through 15 years of family photos and sorting them. Should they be sorted by person, by location, or by topic, in combination with other people in the photo, by age, or by size, to mention just a few of the possibilities? And how many of those pictures become meaningless because you can no longer remember who, when, where, or what?
From your own experience, you will remember times when some level of detail, or some combination of events clicked into place allowing you to ride a bicycle, drive a car, or operate an equation. Now, those skills and abilities are so second nature, that it is difficult to imagine not being able to ride, drive, or balance a checkbook. But there was a time when you lacked the knowledge and application skills to perform those and hundreds of other recognition and decision-making tasks.
The AEL process imitates the native learning style of the brain. The process analyzes and identifies the key visual and auditory cues that distinguish each set of data. Once the visuals and any key sounds have been identified they are sorted into progressive levels of recognition difficulty. AEL begins with a presentation of the simplest, least variable images and progresses in complexity, as the learner is ready. It eliminates a tremendous amount of time normally required by the brain to sort and re-categorize the data that would normally be required in real time or on the job.
With AEL the visual data is presented via computer. The images are presented in such a fashion that they appear game-like and non-threatening, and the learner's brain is required to interact with the visual stimulus. For instance, an image appears on the screen. That image may be a still photograph or a video clip lasting less than the second. The learner is required to recognize one or more key components in a presentation, they are required to do it within a certain time frame, and are then required to specify what it was they saw. A complete interaction sequence may last as long as 10 seconds.
Their interactive repetition with that level of data lasts as long as needed for them to reach mastery of that level. They are required to master that level to form the necessary tagged and sorted data foundation from which to build. The learners progress at their own speed through various types of data as well as data complexity. For instance, the first level of learning might be to recognize a knife mingled with other objects. The next level might be to recognize the difference between two knives and a pair of scissors. The level of complexity increases until the learner can recognize any combination of objects against any background.
The process works by establishing what something is, what it is not, and enough variations in between two allow the learner to instantly discriminate. This process, continuing with the knife example, allows them to recognize when something is a knife, and when it is not knife-like.
Since the AEL process or System provides instant feedback on each learning interaction, the brain can immediately tag and sort the important cues provided by the images. The brain is learning not only to recognize similarities and differences, but also is doing so without the unknowns of trial and error and is learning to recognize the correct cues in real-time.
For the AEL System to work, expert recognition must be identified, similarities, differences, lessons learned, and common mistakes. By representing all these combinations within the AEL System, the time lost to confusion, discovery, study, and trial and error has been eliminated from the normal learning process. AEL makes it possible for each person to learn and master recognition and decision-making as fast as they individually can.
In studies performed with young athletes, one half-hour using the AEL process increased their ability to recognize, interpret, and react to their opponents by 30 percent. Tennis teaching professionals observing the research estimated that practicing 2 to 3 hours a day, five days a week, for at least six months would have been required to achieve the same improvement.
The tennis players were chosen for several reasons. Tennis requires each player to recognize the opponent’s intent before the opponent actually strikes the ball. This is significant because it requires the interpretation of a complex system of variables affecting how and where the ball will be hit. The nature of the game prevents studying an opponent, as each recognition and interpretation must take place in less than 6/10 of a second. Not only is speed of recognition important, but also interpretive accuracy must be nearly flawless.
The study demonstrated that AEL allows even complex three-dimensional human activities to be represented two-dimensionally on a screen in such a way, that learner's interacting with the system can actually expand, reorganize, and retag their visual data. By providing the brain with the appropriate cues, it can dramatically improve recognition, interpretation, and the speed at which the brain processes the relevant data
AEL is a systematic process for transferring recognition and interpretive experience from experts to novices and can move novices toward mastery at a highly accelerated pace. The timesaving can be manifested in three ways: a decrease in the overall learning time, an increase in the number of people successfully trained in a given time, or both.
The data driven structure allows for ease of updating, repetitive use, and standalone as well as online delivery. The flexibility of delivery allows AEL to be used as a pre-qualifying activity, a training activity, a skills elevation activity, and an updateable certification tool.
The amount of press and air time is pointing ever more clearly to concerns in the business world for the loss of the baby-boomer generation from the workforce. The replacement personnel typically have half of the experience or less. The impact of the large numbers of retirees may seriously impair the ability to operate an efficient and productive workplace.
Even if important facts can be gathered into knowledge management systems they have proven ineffective in most cases because they address facts and not their application. Accelerated Experiential Learning captures key knowledge and reference points. It creates the same type of database and associations typically acquired through years of trial and error and does it exponentially faster. The new experience is internalized and available for use immediately as if it had been gathered in the typical fashion over a period of months or years.
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