Have you noticed that while using GPS for directions is an effective way of getting to your destination, it hasn’t significantly improved your knowledge of geography? If you are like most users of maps and satellite data, you probably have found that even if you followed the directions from your GPS device precisely on your first trip to an address, repeating the same trip without the aid of GPS is less successful.

On the other hand, you likely have also found that once you’ve worked to find a location on your own, following verbal directions or even simply driving around looking for an address, you have a much stronger knowledge of where the location is and how to get there.

In other words, even though GPS provides effective guidance to a location through clear and simple directions (i.e., it provides good performance support), it is less effective at teaching you how to get somewhere than if you were putting in more effort with less guidance (i.e., using GPS does not lead to effective learning).

Why does effort help learning? In this article, we explore this question and offer some ideas to help you increase the effectiveness of your eLearning products.

Divergent training: performance support versus performance learning

In the work environment, the GPS example can help us to understand how learning works and to describe divergent forms of training. In corporate training, there are two types of goals to keep in mind:

1) There are certain tasks that you want people to perform well, but do not necessarily want them to memorize or learn. It may be a task that is used very rarely or for a system or procedure that is likely to change in the future. For instance, a worker who occasionally needs to upload a file to a particular database may need guidance only in the form of quick and handy directions. Learning the steps of an infrequent task in software that might change every few months isn’t worth the time.

2) There are other tasks or skills that you want people to both perform well and learn well, so that the knowledge is both durable (it lasts into the future) and flexible (it can be used in different situations). These are tasks that are likely to recur frequently or that are central to a job, such as guidelines for dealing with clients, office HR policies, or repeated tasks on a manufacturing floor where every instance counts and there is little time for retraining or on-the-job referencing.

These two different goals suggest divergent training procedures: For the former, it is enough to simply provide performance supportdeliver guidance and support to employees only when they need it. For the latter, however, we ought to shift our thinking from performance support to performance learning, and we should carefully consider how we can enhance future performance in the absence of direct guidance.

Incorporating desirable difficulties to enhance learning

When it comes to learning, research from cognitive psychology tells us the training procedures that support performance can actually hinder long-term learning. Training procedures that focus on enhancing only current performance often provide crutches that allow learners to bypass the effort and engagement necessary to learn. On the other hand, certain “desirably difficult” conditions of learning that more actively and effortfully engage learners lead to better long-term learning. (Bjork, 1994; Rohrer and Pashler, 2010; Yan, Clark, and Bjork, 2016. See the References at the end of this article.)

Examples of desirable difficulties include:

  • Retrieval practice. Requiring people to retrieve information from memory before providing corrective feedback, versus simply telling them what to do, will ensure that the learner is better able to recall that information independently the next time it is needed (Kornell, Bjork, and Garcia, 2011; Roediger, Putnam, and Smith, 2011).
  • Spacing. Spreading training out over multiple shorter sessions, rather than concentrating training in a single long session, leads to stronger and better-sustained learning (Baddeley and Longman, 1978; Carpenter, et al, 2012).
  • Interleaving. Mixing up the introduction to and the practice of different skills, rather than focusing on one skill at a time, leads to both stronger long-term learning as well as greater ability to flexibly call up the skills as they are needed (Hall, Domingues, and Cavazos, 1994; Kornell and Bjork, 2008).
  • Variability. Varying the conditions under which a task is practiced—for example, seeing a task completed or explained in a different way, or practicing in different locations—strengthens learning. Learners often make mistakes when something unexpected happens or when there is something a little bit different about the situation, and these mistakes are more likely to occur when training is highly and rigidly contextualized (Smith and Handy, 2014; 2016).

These “desirable difficulties” can be counterintuitive (Bjork, Dunlosky, and Kornell, 2013; Yan, Clark, and Bjork, 2016) because they often temporarily lower current performance, leading people to make more mistakes early in training or appear to be learning slowly: Asking people to retrieve information that is not yet well-learned may lead to errors, may feel difficult, or may appear to be less efficient than simply telling them the answer. Breaking what would otherwise be a long training session into multiple shorter sessions encourages helpful forgetting between these sessions. Interleaving practice or different skills may heighten a sense of confusion or difficulty early on during training. And introducing variability may appear to hinder quick (yet thoughtless) automaticity. 

Yet, all of these methods have been demonstrated time and time again to make learning stick more effectively and efficiently. In other words: Harder learning leads to better learning.

Implications for the workplace

To effectively provide performance learning, training must necessarily look different from performance support. This requires a different mindset about what “learning” means. Learning is not just about performing tasks or immediately regurgitating information after a single training session. Effective learning is necessarily active, requires revisiting information over time, and integrates mistakes and challenges into the learning process.

This implies that we should provide a space for “safe failures” during training to allow people to be challenged and learn through mistakes. We should neither demand immediate perfect performance nor expect that training is complete after a single session.

But if training is spaced out over time, what does this mean for performance on the job in the meantime? Hybrid methods that combine both desirable difficulties and performance support may be the answer: For example, in addition to spacing learning out over time, you could inject performance support (e.g., crammed training) right before the task or skill is needed on the job.

Assuaging fears and persuading managers

There are two major challenges to implementing performance learning:

1) Motivating change. How do we convince managers to shift away from more intuitive but less effective modes of learning? Managers have to balance short- and long-term concerns, but more effective learning has benefits for both, since desirably difficult learning doesn’t require your employees to take more time out of their jobs. In fact, given the right technology, training that comes at intervals in shorter, 10- or 20-minute bursts (e.g., directly to a workstation or mobile device) may be more time- and cost-effective than spending an entire work day with your employees gathered in front of a hired trainer.

2) Anxiety. Another concern is that the initial difficulty can increase anxiety in employees, who may arrive at training with an expectation that they should be able to master new skills quickly. We have two responses to this concern: First, increased difficulty or challenge does not necessarily have to be unpleasant. Challenge can be interesting, rewarding, and perhaps even fun. If we can correct and shift our assumptions from “learning should be easy” to “difficulty and errors are an integral part of the learning process,” we sidestep many of these anxiety-related issues. Second, ill-designed training that supports accurate performance during training without promoting learning does not eliminate anxiety. Rather, it simply postpones anxiety and errors to a high-stakes situation on the job. We propose that any anxiety is better brought forward into an environment of “safe failure” and true learning.

“No pain, no gain”

It is tempting to believe that we should make learning easy and provide support wherever possible. Like a lot of other worthwhile goals in life, however, deep and robust learning emerges from more effort, not less. Instead of fearing failures and cheating ourselves out of real learning opportunities, we should invest in shifting our modes of instruction toward empirically driven “desirably difficult” strategies. Leveraging scientific research from cognitive psychology can lead to more productive training and provide an effective long-term strategy for efficient care of your organization’s knowledge.

Note: For more about the theory behind how we learn and the implications for performance support, see this short nine-minute video by Veronica Yan. 


Baddeley, A.D., and D.J.A. Longman. “The Influence of Length and Frequency of Training Session on the Rate of Learning to Type.” Ergonomics, Vol. 21, No. 8. 1978.
http://www.tandfonline.com/doi/abs/10.1080/00140137808931764 (subscription or payment required for access)

Bjork, Robert A. “Memory and Metamemory Considerations in the Training of Human Beings.” In Metacognition: Knowing about Knowing, edited by Janet Metcalfe and Arthur P. Shimamura. Cambridge, MA: MIT Press, 1994.

Bjork, Robert A., John Dunlosky, and Nate Kornell. “Self-Regulated Learning: Beliefs, Techniques, and Illusions.” Annual Review of Psychology, Vol. 64. January 2013.

Carpenter, Shana K., Nicholas J. Cepeda, Doug Rohrer, Sean H.K. Kang, and Harold Pashler. “Using Spacing to Enhance Diverse Forms of Learning: Review of Recent Research and Implications for Instruction.” Educational Psychology Review, Vol. 24, No. 3. September 2012.

Hall, Kellie Green, Derek A. Domingues, and Richard Cavazos. “Contextual Interference Effects with Skilled Baseball Players.” Perceptual and Motor Skills, Vol. 78, No. 3. June 1994.

Kornell, Nate, and Robert A. Bjork. “Learning Concepts and Categories: Is Spacing the ‘Enemy of Induction’?” Psychological Science, Vol. 19, No. 6. July 2008.

Kornell, Nate, Robert A. Bjork, and Michael A. Garcia. “Why tests appear to prevent forgetting: A distribution-based bifurcation model.” Journal of Memory and Language, Vol. 65, No. 2. August 2011.

Roediger, Henry L., III, Adam L. Putnam, and Megan A. Smith. “Ten Benefits of Testing and Their Applications to Educational Practice.” Psychology of Learning and Motivation, Vol. 55. 2011.

Rohrer, Doug, and Harold Pashler. “Recent Research on Human Learning Challenges Conventional Instructional Strategies.” Educational Researcher, Vol. 39, No. 5. June/July 2010.

Smith, Steven M., and Justin D. Handy. “Effects of Varied and Constant Environmental Contexts on Acquisition and Retention.” Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 40, No. 6. November 2014.

Smith, Steven M., and Justin D. Handy. “The crutch of context-dependency: Effects of contextual support and constancy on acquisition and retention.” Memory, Vol. 24, No. 8. 2016.
http://www.tandfonline.com/doi/full/10.1080/09658211.2015.1071852 (subscription or payment required for access)

Yan, Veronica. “The Surprising Dynamics Behind How We Learn: Implications for Performance Support” (video). YouTube. 17 June 2016.

Yan, Veronica X., Courtney M. Clark, and Robert A. Bjork. “Memory and Metamemory Considerations in the Instruction of Human Beings Revisited: Implications for Optimizing Online Learning.” In From the Laboratory to the Classroom: Translating Science of Learning for Teachers, edited by Jared C. Horvath, Jason Lodge, and John A.C. Hattie. London, UK: Routledge, 2016.