The Science of Skill Building: What Evidence-based Learning Frameworks Tell Us About Effective Training
What Pilot Training Can Teach Learning & Development Professionals
In the interest of providing multiple means of engaging with this blog, below is the podcast style conversation, using our AI friends Johnny and Joanne. I have also done a voice over of the original blog, or below is the usual written format.
When a pilot steps into a real aircraft, their training has not been reliant on a PowerPoint presentation about lift coefficients or a manual about emergency procedures. Instead, they have been through a carefully designed learning environment that has systematically built their expertise through deliberate practice, active engagement, and carefully calibrated challenges. This approach to learning - systematic, evidence-based, and highly effective - stands in stark contrast to how many organisations approach learning and development. The growth in e-learning, which is often passive and information about performance is only amplifying the disconnect between the science and practice of learning.
Modern learning science, drawing from multiple theoretical frameworks, provides us with a remarkably consistent picture of how people truly learn. Whether we examine Dehaene's cognitive pillars (Dehaene, 2020), Williams and Hodges' (2023) SAFE framework, or Ericsson's (2019) deliberate practice research, clear patterns emerge that can guide how we design learning experiences. I am deliberately ignoring the pedagogical ‘ologies of information processing, ecological psychology or active inference, instead focussing on the practitioner level agreements across these different thought collectives. Let's explore these unified principles and their practical implications for L&D professionals.
The 6 Foundations of Effective Learning
1. Active Engagement Through Discovery
Across all frameworks, we see that learning requires active engagement rather than passive reception. Dehaene emphasises active engagement as a fundamental pillar, whilst Williams & Hodges’ SAFE framework highlights the importance of discovery-based learning over instructional approaches. This isn't just about "hands-on" learning - it's about creating environments where learners must actively explore and discover solutions rather than simply following prescribed steps.
Practical Application: Instead of telling salespeople the "right" way to handle objections, create scenario-based training where they must discover different approaches through experimentation. The environment should be designed to make certain strategies more likely to succeed, but learners should discover these through exploration rather than instruction. These deliberate designs by trainers might be actors who are predisposition to respond differently to different techniques. This is not about instructing a sales person to use a technique but varying customer types using blocked, varied or random practices that allow them to practice.
2. The Performance-Learning Paradox
A crucial insight emerging from all frameworks is that immediate performance is often a poor indicator of learning (Soderstrom & Bjork, 2015). The SAFE framework explicitly addresses the balance between training for short-term performance versus long-term learning, while Ericsson's research distinguishes between naive practice (which might look good in the moment) and deliberate practice (which builds lasting expertise).
Practical Application: Design learning programs that deliberately incorporate "desirable difficulties."(Bjork & Bjork, 2020) This might mean:
Reducing immediate feedback in favour of delayed reflection
Creating variable practice conditions rather than blocked repetition
Including elements of stress and fatigue in practice scenarios
Building in opportunities for productive failure
3. The Crucial Role of Feedback
Feedback appears as a critical element across all frameworks, but with important nuances. Dehaene emphasises error feedback as a fundamental pillar, while the SAFE framework incorporates feedback through environmental design. This isn't just about telling people what they did right or wrong - it's about creating conditions where the consequences of actions are clear and meaningful. We covered different types of feedback here. The key is knowing how much the environment or trainer is giving knowledge of results and knowledge of performance.
4. Environmental Design Over Direct Instruction
A surprising convergence across frameworks is the emphasis on environmental design over direct instruction. The SAFE framework explicitly advocates for "facilitating learning through designing the environment," while Dehaene's attention principle highlights the importance of directing attention through environmental cues rather than explicit instruction.
5. The Art of Individual Calibration
Perhaps the most challenging aspect of learning design is addressing individual differences effectively. Just as flight simulators can be adjusted for different skill levels, learning environments must flex to accommodate varying learner needs while maintaining appropriate challenge levels.
Understanding Individual Training Zones
The line between maintenance practice and growth practice isn't fixed - it's highly individual and context-dependent. What constitutes a growth zone for one learner might be purely maintenance for another, or possibly even overwhelming. Consider these key factors:
Cognitive Load Capacity: Learners vary significantly in their ability to handle multiple demands simultaneously. Some can incorporate feedback while performing, others need clean separation between performance and reflection. In our pilot training analogy, some trainees might handle multiple system failures simultaneously, while others need to master each type of failure individually before combining them.
Emotional Resilience: The ability to handle failure and maintain engagement varies dramatically between learners. Some thrive on challenge and frequent failure, others need more successful attempts to maintain motivation. This isn't about coddling learners - it's about optimising their learning trajectory. We can also incorporate elements of Universal Design for Learning to help training designers individualise learning.
Practical Constraint Manipulation
Learning designers can adjust several key variables to maintain optimal challenge levels:
Time Constraints:
Extend or compress time limits
Vary preparation time allowed
Modify the rhythm between attempts
Information Load:
Increase or decrease available information
Vary the clarity of information
Modify the timing of information delivery
Adjust the number of variables to consider
Performance Conditions:
Modify environmental pressures
Adjust task complexity
Vary the predictability of scenarios
Change the consequences of errors
Variation in Practice
Novices early on might benefit from blocked practice. Taking a single learning aim and practicing this with small levels of variation. This might be that sales person with the same actor each time, or a pilot landing at the same airport with rain. Incorporating feedback and trying again. This will give some immediate performance improvements, boosting self-efficacy, but it has less transfer. Remember, you are likely observing increases in short term performance, not long-term robust learning.
How quickly can you move them onto varied. practice. This might be adding multiple scenarios on. Think different weather conditions when learning to land a plane at the same airport. Then random practice which might be various weathers, varying airports at either take off or landing. This is really about practicing the attention elements Dehaene talks about. How are they reading the customer, how is the pilot feeling the impact of air density and adjusting their actions? This is not about repeating a solution, but the repetition of finding a solution to a rainy cold day, a customer who is sceptical, or an employee who is nervous about a colleagues behaviour.
Creating Adaptive Learning Environments
The key is designing environments that can flex to accommodate different learners while maintaining the core learning challenge. This means building in:
Multiple progression paths
Variable challenge points
Flexible support systems
Individual calibration tools
6. Transfer Through Specificity
A final convergence point is the importance of practice conditions that match performance conditions. This appears in the SAFE framework's emphasis on specificity and in Ericsson's focus on domain-specific practice.
Implementation Challenges and Solutions
The biggest challenge in implementing these principles is often organisational rather than pedagogical. Traditional learning metrics, time constraints, and scalability concerns can push organisations toward less effective but more easily delivered learning solutions.
To address these challenges:
Start small - pilot these principles in high-impact areas first
Identify the problem you are solving, there is no universal “management training”
Build measurement systems that capture long-term learning, not just immediate performance
Use technology to create scalable, adaptive learning environments, VR and even AI provide opportunities for management training in the same way simulators do for pilots. Design representative learning environments that maintain the perception-action coupling.
Invest in developing L&D professionals' expertise in learning design rather than just content delivery
The Way Forward
The science of learning presents a clear challenge to traditional L&D practices. Just as we wouldn't train pilots through PowerPoint alone, we need to fundamentally rethink how we approach workplace learning. This means moving from being content experts to becoming learning environment designers, from instruction to facilitation, and from performance measurement on quantity of learning completion to learning measurement.
The question isn't whether these principles work - the evidence is clear across multiple theoretical frameworks. The question is whether we're willing to transform our L&D practices to align with how people actually learn. Are we ready to build our own equivalent of flight simulators for workplace learning?
The science is clear. The principles are consistent. The true art lies in creating environments that can flex and adapt while maintaining their core learning objectives. Are we ready to embrace this more sophisticated approach to learning designing organisations?
Anders Ericsson, K., & Harwell, K. W. (2019). Deliberate practice and proposed limits on the effects of practice on the acquisition of expert performance: why the original definition matters and recommendations for future research. Frontiers in Psychology, 10(OCT). https://doi.org/10.3389/fpsyg.2019.02396
Bjork, R. A., & Bjork, E. L. (2020). Desirable Difficulties in Theory and Practice. In Journal of Applied Research in Memory and Cognition (Vol. 9, Issue 4, pp. 475–479). Elsevier Inc. https://doi.org/10.1016/j.jarmac.2020.09.003
Dehaene, S. (2020). How We Learn. Penguin, Random House.
Soderstrom, N. C., & Bjork, R. A. (2015). Learning Versus Performance: An Integrative Review. Perspectives on Psychological Science, 10(2), 176–199. https://doi.org/10.1177/1745691615569000
Williams, A. M., & Hodges, N. J. (2023). Effective Practice and Instruction: Skill Acquisition Framework for Excellence (SAFE). Sport & Exercise Psychology, 41, 833–849.