Feedback — The Secret Weapon for Acquiring Expertise

Co-Author: Clay Killingsworth

Learning is an integral part of life — we are constantly learning and acquiring new skills. During this lifelong journey, there are a few things that can help us along the way. One such thing is feedback.

Feedback is the breakfast of champions.

-Ken Blanchard

Who doesn’t like Wheaties for breakfast? Why experts don’t; they would rather have a bowl full of feedback according to Ken Blanchard of The One Minute Manager fame. Feedback, at its core, is simply information that is used as a basis for improvement. Whether it’s reactions about a product or service, or a performance review at work, or even skill acquisition through training, feedback provides pertinent information that helps us improve in the future.

Research has shown that feedback is crucial for learning and skill acquisition. Feedback has also been found to drive intrinsic motivation for learning — so really good feedback can internally motivate us to keep learning and pursing new skills, just for the sake of it! In essence, good feedback is the backbone of good performance.

There are hundreds (yes, hundreds, really) of ways to vary the presentation and style of feedback. Feedback can vary in the type of information provided (the mechanism), the level of information provided (the specificity), the timing of presentation (immediate or delayed), whether its directive (what needs to be improved) or facilitative (guidance for personal revision and conceptualization), and by the form factor (how its presented).

With so many different options, how do you know what type of feedback to give someone? Well, it really depends on what you are trying to gain from providing feedback. For example, if you are teaching your teenagers how to change their car engine oil, you’ll want to give them immediate feedback — it wouldn’t be very effective to wait until their engine burned out… definitely a form of highly undesirable feedback.

However, if you are giving someone a performance review at work, it actually may be really helpful for them to know what they did well on and not so well on six months ago, so that they can continue building upon their strengths and overcoming their performance gaps in the future. Such delayed feedback actually fosters curiosity, encouraging learners to anticipate the feedback and thus pay more attention to it when it is received. Delayed feedback also fosters transfer of learning.

Beyond timing, feedback varies in its mechanisms, specificity, and form factor. For example, if you’re providing feedback on a product or service, you would probably include elaborative responses instead of just yes/no answers. In terms of perspective, the extent to which feedback is future (i.e., future desired behaviors, steps to take to realize future success, etc.) rather than past focused, the more effective it will be in enhancing confidence and ultimately performance.

In this blog post, we’ll discuss the different types of feedback, and the associated best practices, in the context of acquiring expertise through training.

Types of Feedback — A Brief Review

Let’s take a deeper dive into the many variations of feedback. Feedback can be categorized into four main characteristics — the mechanism, specificity, timing, and form factor.

Mechanism refers to the type of information provided and actions that could come next. For example, with Verification-type feedback, you would simply provide information on the correctness of an answer, or even the total score. However, if you gave someone Elaborated-type feedback, you would provide them with some type of elaborative response (i.e., supplemental information designed to foster deep understanding) — what, exactly that is, can vary (e.g., attribute isolation, topic or response contingent, worked examples, hints/cues/prompts, misconception related) and depends on the level of specificity. Shute explores feedback mechanisms here.

Specificity refers to the level of detail provided in feedback information, particularly task planning information, with use of nouns and descriptive adjectives, descriptions of concepts and criteria, provision of helpful learning strategies, and use of precise and specific examples recommended. Are you simply telling someone if they got something correct or not? Are you error-flagging and showing them exactly where they made an error in a procedure? Are you giving them hints or cues about the correct answer? What about providing a lengthy response regarding exactly why a specific answer was incorrect (response contingent)? All of these examples fall under the grouping of specificity. Shute provides a really great breakdown of some of these in her article, which you can read here.

Timing is simply when exactly you present feedback. Are you proving feedback immediately following a response? Or are you waiting until later? If you’re waiting, how long are you waiting? Generally, timing is broken into two groups: immediate or delayed. However, it’s important to keep in mind that the length of the delay also matters. A delay of 1 day is not the same as a delay of 8 weeks. As a general rule of thumb, it’s best to present feedback immediately after a response. However, there are some cases where delayed feedback is better (typically with higher proficiency learners…which we will discuss later on). In general, delayed feedback is thought to be superior when fostering transfer of learning, while immediate feedback is preferred when learning procedural skills. Shute explores the nuances of feedback timing here.

Form Factor refers to the way feedback is presented. You can provide feedback orally, through written communication, and even through haptics or tactile sensors (think of an intense buzzer letting you know you missed a question). Rezazadeh and his colleagues explore oral and written form factors here.

Here are some of the most common types of feedback — remember, they can all be varied by timing and form factor.

Feedback and Expertise

True intuitive expertise is learned from prolonged experience with good feedback on mistakes.

-Daniel Kahneman

Nobel laureate Daniel Kahneman advocates for feedback that is both rapid and unequivocal to foster skill and expertise development. Feedback is thus seen as critical in the pursuit of acquiring expertise. Just as there are many varieties of feedback, there are many varieties of expertise — Nobel-prize winning scientists like Kahneman are widely recognized knowledge experts, whereas world-class golfers are seen as skill (associative) experts. One thing all types of experts have in common is a long history of intentional effort to improve their performance, also referred to as deliberate practice.

Ericsson et al. (1993, p. 368) define deliberate practice as follows:

…a highly structured activity, the explicit goal of which is to improve performance. Specific tasks are invented to overcome weaknesses, and performance is carefully monitored to provide clues (a.k.a. feedback) for ways to improve it further.

Becoming an expert is thus all about progressively improving skills and expanding knowledge via systematically directed practice and associated feedback. Successful deliberate practice, and thus acquiring expertise, depends on a number of factors which are discussed in greater detail in one of our earlier articles in Psychology in Action. Chief among these factors is feedback. Both core elements of deliberate practice — identifying areas for improvement and going about improving them — depend on accurate and timely feedback.

Bottom line… If the goal is to facilitate the acquisition of expertise, then feedback should be coupled with deliberate practice. The best way to accomplish this, however, is determined by factors of both the learner and domain to be learned.

Type of Skills or Knowledge

First, look at the type of skills or knowledge to be learned. All skill and knowledge domains can be subjects of expertise — that is, can be mastered to the point that one achieves reliably superior performance. However, procedural or associative learning depends on a qualitatively different sort of feedback than does explicit knowledge learning.

Associative learning — a broad academic term roughly synonymous with skill learning — refers to any domain for which a stimulus and response must be associated to achieve high performance. Associative learning tasks may be further categorized as either procedural or perceptual:

• Procedural learning involves associating a stimulus with a motor response; a tennis backhand, inserting an IV, and touch typing all depend on procedural learning.
• Perceptual learning depends on associating a stimulus with a neural response. Reading an x-ray, picking perfectly ripe fruit, and playing high-level chess depend on well-learned perceptual associations.

In all cases, associative learning depends on large amounts of repetition. Repetition serves to create and strengthen the association between a stimulus and a response — be it procedural or perceptual — via evolutionarily old brain structures that depend on dopamine-driven reward systems (F. G. Ashby et al., 2003; Maddox, Bohil, et al., 2004). This is relevant to determining the right type of feedback because dopamine-driven stimulus-response association is highly sensitive to timing; if feedback is not provided within a couple of seconds, learning is unlikely to occur.

So, for this type of skill-based expertise acquisition, you really want to start with immediate feedback. The mechanism, specificity, and form factor can all vary, but we really shouldn’t be using delayed feedback for associative learning (yet…remember what we said about more proficient learners).

Explicit or knowledge learning is accomplished by holding information in working memory, forming and testing hypothetical rules and frameworks to make sense of it and connect it with prior knowledge, and transferring the results to long-term memory. Whereas associative learning depends on unconscious stimulus-response associations, explicit knowledge is accessible to conscious thought. Learning the laws of physics, acquiring a new programming language, and telling apart cats and dogs are examples of explicit learning.

A simple litmus test for distinguishing explicit and associative learning is whether you can verbalize the way to achieve high performance: I can clearly articulate how to distinguish a normal sinuous cardiac rhythm from a lethal one, but I can’t explain how to tell if a mango is ripe without showing you lots of examples.

In contrast with associative learning, explicit learning does not require immediate feedback (Maddox & Ing, 2005). However, it does require that feedback be presented for long enough that it can be adequately processed (Maddox, Ashby, et al., 2004)….meaning, you’ve got to show the feedback for enough time that the learner understands it — you can’t simply flash a correct/incorrect card and move on. Associative learning only requires that stimulus and response associations be stimulated; feedback need only be presented long enough to be perceived — about 200 milliseconds — to produce the full effect. Explicit learning, on the other hand, requires perceiving the feedback, incorporating it into the schema held in working memory, and in some cases connecting it to knowledge stored in long-term memory. All of these processes require time and, usually, a meaningful amount of effort to complete.

Too quickly removing feedback or introducing additional demands on working memory (e.g., new information or another task) will interfere with feedback processing and may limit or negate learning. In these cases, we advocate for more elaborative-type feedback…so, the mechanism and the specificity are really important here as we want the learner to really process and connect the feedback with the content.

Proficiency Level of the Learner

The next consideration is the proficiency level of the learner. As a rule, feedback specificity and immediacy should decrease as expertise increases — so how much information is provided and when it is provided both change as a function of how proficient a learner is. Research has told us that more expert learners need less information about what they did wrong, and it’s easier for them to take in this information much later after the learning instance. On the other hand, more novice learners need immediate feedback that is more directive and elaborative.

Look at the example below, this type of immediate, directive, and elaborative feedback is great for novice learners who are just starting out. It provides information of what was performed incorrectly (i.e., response contingent feedback), what could happen if this was done in the real world (i.e., attribute isolation feedback), and directions for how to perform the action correctly (i.e., error-sensitive feedback).

Example of Novice-Level Feedback

But what makes someone a novice? Or even someone more proficient? Well, some organizations may have their own levels and definitions (for example, the US Army uses the terms ,’CRAWL’, ‘WALK’, and ‘RUN’ to categorize expertise), but you can use the descriptions below as a reference guide.

• Novice. At the Novice level, learners are being exposed to introductory topics that focus on remembering formal declarative knowledge (e.g., semantic, temporal, and spatial information). Learners are able to recall basic steps in a procedure, though they will not understand the rationales for the steps (e.g., knowing how, but not understanding why). At this level, feedback should be immediate and elaborative, with response- and topic- contingent, attribute isolation, error-sensitive, and “try-again” feedback being effective at this stage.
• Competent. At the Competent level, learners are able to demonstrate a mastery of procedural knowledge and are able to successfully complete procedures with limited environmental stressors. Competent-level learners are acquiring conditional knowledge and are able to use abstract information to facilitate their decision-making skills. For competent-level learners, you should provide immediate, minimally elaborative, verification, and error-flagging types of feedback. You should also provide learners with information on where an error occurred, as well as why it occurred. This will foster great deliberate practice and intrinsic motivation for learning.
• Expert. At the Expert level, learners are demonstrating their mastery of higher-order skills, analytic abilities, and self-regulation. They have extensive domain knowledge that affords the ability to transfer problem solving skills to new, novel scenarios. Expert level learners should also demonstrate mastery of generating ideal courses of actions (COA). Additionally, learners should be able to determine the relevancy of presented cues and judge the efficacy of alternative solutions. At this level, feedback should be delayed and provide opportunities for self-explanation. You should also provide performance metric information, such as information on which areas where underperformed so that a learner can focus on those areas specifically (e.g., delayed, topic contingent) — again, this will facilitate deliberate practice and progressively speed up expertise progression.

Some Last Tips…

When developing feedback, keep in mind that:

1. Feedback should not be overly complex — no matter the proficiency level of the learner, the purpose of feedback is to facilitate improvement. If the feedback is too complex, the message won’t be received.
2. Don’t include praise or be critical of the learner — you don’t want to inadvertently bring self-esteem into feedback. Research has shown that when you threaten self-esteem, you can actually hinder learning. Even providing praise can make learners associate their self-esteem with their performance on the material. It’s best to avoid such judgment all the way around.
3. Mix up the form factor of the feedback — include auditory, visual, and haptic feedback where you can (and when it’s appropriate). Also, break out of boring PowerPoint-style presentations and mix it up with animations, 3D models, and videos to reinforce exposure. Did a novice-level learner perform a procedural task incorrectly? Show them an animation of it being done the right way instead of just presenting text on a screen.
4. When possible and appropriate, focus feedback on what will make a learner most successful going forward, as such prospective guidance will motivate learners to accept feedback and foster positive behavior change.

References

Ashby, F. G., Ell, S. W., & Waldron, E. M. (2003). Procedural learning in perceptual categorization. Memory & Cognition, 31(7), 1114–1125. https://doi.org/10.3758/BF03196132

Ashby, F. Gregory, & Maddox, W. T. (2011). Human Category Learning 2.0. Annals of the New York Academy of Sciences, 1224, 147–161. https://doi.org/10.1111/j.1749-6632.2010.05874.x

Ericsson, K. A., Krampe, R. Th., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406. https://doi.org/10.1037//0033-295X.100.3.363

Gnepp, J., Klayman, J., Williamson, I. O., & Barlas, S. (2020). The future of feedback: Motivating performance improvement through future-focused feedback. PloS one, 15(6), e0234444. https://doi.org/10.1371/journal.pone.0234444

Goodman, J., Wood, R., & Hendrickx, M. (2004). Feedback specificity, exploration, and learning, Journal of Applied Psychology, 89(2), 248–62. https://doi.org/10.1037/0021-9010.89.2.248

Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004). Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory & Cognition, 32(4), 582–591. https://doi.org/10.3758/bf03195849

Maddox, W. T., Bohil, C. J., & Ing, A. D. (2004). Evidence for a procedural-learning-based system in perceptual category learning. Psychonomic Bulletin & Review, 11(5), 945–952. https://doi.org/10.3758/bf03196726

Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology. Learning, Memory, and Cognition, 31(1), 100–107. https://doi.org/10.1037/0278-7393.31.1.100

Rezazadeh, S., Ashrafi, S., & Foozunfar, M. (2018). The effects of oral, written feedback types on EFL learners’ written accuracy: the relevance of learners’ perceptions. Proceedings of the 2nd National Conference on English Studies: Applied Linguistics Perspectives on EFL Reid, M. Joy. 1993. Teaching ESL Writing. New Jersey: Tina B. Carver. https://www.researchgate.net/publication/324953301_The_Effects_of_Oral_Written_Feedback_Types_on_EFL_Learners%27_Written_Accuracy_The_Relevance_of_Learners%27_Perceptions

Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795