This Is What's Wrong With Your Training...

The science behind effective instructional design is complex. When training is designed systematically and based on the science of learning, it is more likely to yield a positive result. But if not designed according to the science of learning, the harm to an organization can far outweigh the investment it takes in people and systems to have done it right the first time. 

It's worth taking a moment to think about the real issues you face as a training professional (and those really aren't about the latest, greatest emerging trends such as mobile learning or social learning). Instead, there are two deeply complex issues that every training organization should be focused on:

1. Determining how to effectively support a multi-generational, cross-cultural global workforce, all with different motivations, expectations, and approaches to learning.
2. Ensuring the workforce has the skills to adjust to the near-instantaneous pivots happening in today's world of work.

Nothing else matters. Nothing. But too often, we abandon the science of training design and fall into the dark, murky waters of assumption. Assumptions can lead us astray, and too often result in unmeasurable outcomes, confused audiences, and a low return on investment. Some of us are purists and argue, holler and scream for the time to do it right. Many of us are pragmatists and begrudgingly accept the constraints inherent to business and press forward trying to make the best of those assumptions. It's not a perfect world. However, it's worth noting the cause and effect of too many assumptions so you can try and mitigate them as you move forward. This list of those assumptions is derived from the work of Eduardo Salas, who has written a great article on what matters most in good training grounded in the science of learning (I encourage you to read the article):

• Assumption: It’s what happens during training that matters.
  Actually, what happens before and after training can be just as important. Beforehand, take care to communicate clearly what the training is about and how it relates to the job; afterward, seek learner feedback and offer follow-up support for newly-acquired skills.
• Assumption: Focus the training on getting workers to remember everything they need to know to do their jobs.
  There’s a firehose of information available today to learners. The key for designers is to distinguish between “need-to-know” and “need-to-access.” For the latter category, Salas writes, “training should teach people where and how to find that information rather than seeking to have them retain that information in memory.”
• Assumption: As part of the training, learners should be tested on their abilities and asked to focus on the areas that need improvement.
  Research shows that training is more effective when it’s presented as an opportunity, rather than as a test, and when it emphasizes its benefits to participants, rather than participants’ existing deficits.
• Assumption: Once employees have been trained, those skills are in place and subsequent training can move on to teaching new skills.
  In fact, “skill decay is a major problem in training,” writes Salas. He cites a meta-analysis finding that a year after training, trainees have lost over 90% of what they learned. Skill decay can be prevented by giving workers frequent opportunities to practice their new skills, and by scheduling “refresher” training. This is where you must look at spaced repetition, especially with complex skills acquisition.
• Assumption: The motivation to learn has to come from within the individual employee—there’s not much employers can do about it.
  Actually, Salas reports, employers can increase workers’ motivation simply by being clear about the link between what’s being taught and how it will be used on the job, and by making sure employees feel supported in their efforts to learn by their supervisors and by the organization as a whole. You must consider not only the worker, but their leaders and their environment when looking at the efficacy of the program.
• Assumption: Video and digital media are the best way to deliver training.
  These media, which emphasize information and demonstration, “remain the strategies of choice in industry. And this is a problem,” Salas writes. “We know from the body of research that learning occurs through the practice and feedback components.” Please stop propagating the non-interactive "talking head" video as a great way to disseminate information, solely relying on its short length as a motivator to create it, under the false impression that a person gets distracted after 3-5 minutes of watching. Please, just stop. You can easily increase the effectiveness of training by making the process more active and engaging for learners. A passive video is not that.
• Assumption: The best way to arrange training is to show workers what to do, then let them jump in and try it for themselves.
  “Not all practice is created equal,” Salas notes. “Unstructured practice without objectives, appropriate stimulation, and useful feedback can teach wrong lessons.” Workers get the most out of practice when they are provided with constructive and timely feedback that identifies what they may be doing wrong and how to fix it. Feedback, feedback, feedback. And meaningful feedback -- the most important element in how the brain moves information to skills acquisition.
• Assumption: The better workers perform during training, the better they’ll perform on the job.
  Not necessarily. Research shows that conditions that maximize performance during training are often different from those that maximize the transfer and retention of those skills. “Drilling” information leads to rapid learning during training, for example, but it leads to poorer retention and transfer than other methods that promote “deep learning.” That's why it's "drill and kill" in some respects. You're doing more harm by overloading with too much practice during training.
• Assumption: Making errors during training should be avoided.
  “Because errors often occur on the job, there is value in training people to cope with errors both strategically and on an emotional level,” Salas notes. Guiding workers to make errors, and then providing them with strategies to correct their mistakes, will lead them to understand the task in greater depth and will help them deal with errors on the job.
• Assumption: Adding technology is a surefire way to improve training.
  “Both traditional forms of training and technology-based training can work, but both can fail as well,” Salas observes. Technology must be implemented in a thoughtful way, in accordance with scientific findings, in order to add to the effectiveness of training.
• Assumption: Workers should always be allowed to make their own choices about what they need to learn.
  Research shows that “learner control,” although it sounds appealing, doesn’t lead to greater learning. Left to their own devices, workers may not be knowledgeable or motivated enough to make wise decisions about how and what to learn.
• Assumption: In training using simulations, it’s important for the virtual setting to be precisely the same as the one the worker will encounter on the job.
  Actually, Salas writes, what matters is not the “physical fidelity” of the simulation, but its psychological fidelity—how accurately it evokes the feelings and the responses the worker will have on the job. Also, see Karl Kapp's article on fantasy to confirm Salas' point here even further. We spend too much time trying to be too authentic in replicating the learner's work environment in "training mode".

It doesn't take too much effort to back away from the assumptions we too often cling to when "checking the box" to get the next training program done. All it involves is a thoughtful, often repeatable process of invoking scientific rigor to break down assumptions as we embark on the design of training.

Reference:

•  Salas, E., Tannenbaum, S., Krater, K., Smith-Jentsch, K. "The Science of Training and Development in Organizations: What Matters in Practice". June 2012 vol. 13 no. 2 74-101. https://psi.sagepub.com/content/13/2/74.full.pdf+html?ijkey=g8tvuLmoeZfN2&keytype=ref&siteid=sppsi