DEBUNKING LEARNING MYTHS WITH CLARK QUINN

Clark Quinn‘s latest book, “Millennials, Goldfish & Other Training Misconceptions: Debunking Learning Myths and Superstitions”, is now available on ATD Press and Amazon.

Through this book, Clark explains how myths, despite publicity about their invalidity, still persist in instructional design practices… and, how beliefs about learning – that don’t reflect what is known from science – continue to exist. He also elaborates on myths that affect adult learning in the organization.

In this exclusive interview with Learnnovators, Clark further debunks the general misconceptions surrounding learning myths and superstitions.1. Myths surrounding the science of learning seem to have a hypnotizing aura going with them because even the learned fall for its power.

As you point out, why else would 78 percent of those with some neuroscience education believe in learning styles? Could you please explain the ‘safe’ thought processes that persuade them to stay loyal to the myths against all odds?

Clark: I can’t guarantee that I can explain it fully, because I (and others) don’t completely understand it! The short answer is that most myths explain aspects of the world that we’ve observed, and thus align with our own experience. As H. L. Mencken once said: “”For every complex problem there is an answer that is clear, simple, and wrong.” In many cases, the myths also reflect our preferences as well, so not only are they explanatory, they’re appealing. For example, we know that learners differ, and we also have noticed our own preferences for particular media, so the ‘learning styles’ story aligns. I’ll also note that we like to know about ourselves (see the perennial interest in the ‘Cosmo quiz’), and if they’re written so that we can see ourselves (c.f. astrology), it makes it easy to want to believe in it. And it’s not that they’re permanently wrong, but we haven’t yet found a reliable instrument. Also, there’s no evidence that we should teach to the style, it’s just the associated inference.

2. Just playing the devil’s advocate here. The claim that we can reliably categorize individuals by their learning styles sounds quite credible too.

For instance, a parent observes that one of her children spends a lot of time with books while the other loves to ‘put things together’. So, she gets more books for the former and lots of assembling kits for the latter. In a way, she is catering to their ‘learning styles’. Isn’t she? 

If this is as basic as this, why is there so much war-cry against learning styles?

Clark: Several things here. First, if she’s catering to their styles, is she reinforcing stereotypes? Let’s go extreme: what if she gives her daughter a kitchen set, and her son toy soldiers? What will that do in the long run? And second, what if she’s limiting their abilities? What if the former child found, upon getting kits, a natural aptitude? And would the child not getting books be hampered in reading? We don’t fully know when to challenge and when to align. There’s nothing wrong with giving them what they want, but there should be a mix as well. As parents, we need to challenge as well as coddle; our responsibility is to prepare our children for success on their own. And certainly in situations like school and the workplace, we need to apply what’s known, not what’s believed.

3. You say that the attention span is largely under our volitional control and we decide what we want to attend to. This means that we can retain our attention for a considerably long time if we want to, as for instance, while watching a favorite movie or playing a video game.

That said, the onus of ‘reducing attention spans’ doesn’t rest with our personality changes (claimed to have been induced by the so-called technological influences) as much as it does with ‘un-engaging’ stuff.

So, the general practice of designing learning experiences that solely cater to learners’ short attention spans is misguided at best.

Could you please throw some light on this?

Clark: We are susceptible to attentional distraction. It’s in our cognitive architecture: for instance, something moving in the periphery of our vision will make it hard not to attend to it (remember how annoying flashing gifs on web pages were?). And high saliency events (such as someone mentioning our name in a neighboring conversation, aka the ‘cocktail party’ phenomenon) can draw our attention. Yet in general we need focused attention for learning to occur. We have limited working memory, and attentional distractions can overwhelm our capability, as the cognitive load research has shown. Our goal as designers is to manage memory load by reducing attentional distractions and supporting attention to the right elements at the right time. Making learning more minimal is a good thing (c.f. spaced learning), but we have to account for forgetting as well. It needs to be as minimal as possible and no more.

4. You mention that to multitask, we must switch our conscious attention between tasks but switching, by its very nature, deteriorates performance. You further add that it makes sense for us to design working systems that enable total focus and minimize cognitive load. Sounds idealistic.

But, in reality, just the opposite seems to work… especially in corporate contexts. Those who command the agility to multitask, finding their way through a maze of things all at almost the same time, become the most wanted. And, when they add cognitive tasks to their work repertoire and successfully accomplish them along with others, they become super stars.

Against this all-too-obvious backdrop, the ‘switching deteriorates performance’ argument seems to pale a bit.

Could you elucidate this point please?

Clark: I’ll challenge your assumption; it’s not the people who do the most multi-tasking that are successful, it’s the ones who do the best multi-tasking. And I’ll suggest that it’s knowing when to multi-task, and when not to, that makes the difference. Life indeed forces us to multi-task. We have emergencies, and even expected distractions. For instance, we may be working on a proposal but have a scheduled meeting that we need to take a break for. And phone calls come, and emails, and texts, and… However, multi-tasking is more inefficient. As a consequence, we need to know when to focus and when we can, and then shut off the distractions to do our best work.

5. Evidence, as you highlight in the book, states, ”While each generation perceives itself as being more different from other generations, that perception did not reflect how much they actually differed. People’s perceptions often don’t match reality, and that appears to be the case here.”

How does this explanation nullify the fact that millennials are more informal and ‘on their feet’ with learning and work? Technological advancements have literally molded their personalities into the way they are. Their learning methods – using portable devices to learn while on the move, for instance – are totally different from those of Generation X.

And, the ‘casual’ work atmosphere at organizations such as Google is designed for them and is strikingly different from that of ‘conservatively’ designed organizations.

Could you please clarify?

Clark: Your statement implies that millennials are more informal and ‘on their feet’, but what evidence do you have on that? More importantly, how do you separate out their generation from their age? Throughout history, more experienced citizens have bemoaned how youth seem to ignore the traditions and mores of society. So here the millennials test doesn’t pass Occam’s Razor, the simpler explanation is age. Younger folks have reliably been somewhat skeptical of the status quo, it’s not a generational thing. For instance, it’s claimed that the millennials are more interested in certification. However, that can be explained by the fact that young people don’t have experience to point to, so they need labels. Older people can say “I’ve done it here, and here, and….” In short, to stereotype people by the year they’re born in is still stereotyping. Instead, address a person on their behavior, not on some element out of their control.

And that statement about learning methods changing; I’m sorry, but our wetware hasn’t evolved in that short of a period. We are using technology in new ways, but it’s just more closely pursuing what we know about how the brain learns. For example, that canard about “we have to use games for this generation” is wrong. We use games because it’s better learning for all ages! Simulations are the next best learning environment to mentored live practice (and that’s problematic because individual mentoring doesn’t scale well, and live practice can be, well, costly). The same thing holds true about the workplace; Google likely is experimenting with new approaches that better reflect what’s known about workplaces that facilitate the best outcomes. And they may have some of it wrong; we’re seeing pushback on the concept of ‘open plan’, for instance. Our understanding is evolving, and things change. But our brains haven’t. Thus, we need to be careful about just what we’re claiming. We may learn different things, in new and better ways, but they’re working to better accommodate our mental architecture, because it’s not changing that fast.

6. You clarify that there is no evidence that our brains are changing as a result of using technology. But can we say the ways in which they process information are changing?

For instance, the concept of bite-sized learning is gaining currency in the current day atmosphere, considering the fact that folks are constantly bombarded with a barrage of information all at once. So, to keep cognitive overload at bay, they go for smaller nuggets of information at any point in time, so they can assimilate stuff better.

Could you please explain this a bit?

Clark: As I explained above, we are moving towards closer alignment with what learning science research says works best. That’s a good thing. And smaller nuggets (as small as possible and no more) does align more closely with what we know about our information processing capabilities. But this is more a matter of old approaches not being efficient. For instance, the traditional classroom with a teacher lecturing the class was just a bad design. Moving away from that is right, regardless of technology. Maria Montessori was doing more constructivist approaches long before we had digital technology. Technology is definitely an enabler, but as Barbara Means suggested in the SRI report on eLearning, the improvements they saw were likely to have come from the opportunity to rethink the pedagogy, not the technology per se. Let’s not misattribute the changes.

7. As you mention, Simons and colleagues found no evidence in the claim that brain training yields generalized improvements in brain function.

But, we do find that voracious readers – whose brains ‘get trained’ into quickly assimilating information by the sheer act of processing information day in and day out – exhibit superior mental agility in catching up with things in a way that non-readers can’t match. It’s not surprising then that these souls get identified as the smart lot from among their peers.

Is this not proof enough that brain training does yield generalized improvements in brain function? Could you please lead us on this?

Clark: Your use of the term ‘mental agility’ needs a clear referent. What do you mean? If you mean they can read and learning about something where someone else can’t, that’s not really a good test of ‘brain training’. Reading is an ‘enablement’ skill; it’s a big key to information access. Yet being a skilled reader doesn’t mean you’re good at math, for instance. Instead, to be good at math, do math! (With all the nuances: deliberate, varied, and spaced practice; cognitively annotated examples; useful models, etc.) Which is true for all things you need to learn. If you practice what you need to be able to do, you can do it. If you do something similar, it will transfer to the extent it’s similar. But general transfer, despite our desires for it to be so, haven’t been reliably demonstrated.

8. You confirm that the neuro-linguistic programming (NLP) approach fails on both empirical and conceptual grounds. And, you further prescribe that approaches to changing behavior tend to work better on other bases such as cognitive behavioral therapy (CBT).

If cognitive behavioral therapy works along similar lines as NLP, in looking at a person’s beliefs versus what’s real, and reframing those beliefs for better, how is it different from (or better than) NLP?

Clark: The important difference is that CBT has been validated in clinical trials, while NLP has been refuted! Don’t confuse the fact that CBT addresses some of the same problems that NLP addresses to lead you believe the methods are the same. While both use language, CBT is doing something specific around your own framing about yourself. NLP instead talks about your interpretation of the world. If NLP does the same as some small part of the overall approach (which includes a lot more), it’s more a case of “even a blind pig finds a truffle once in a while”. That is, it’s by circumstance. But the body of NLP practices includes touch, claims about non-verbal perception and instant remedies, and more, that don’t withstand rigorous scrutiny.

9. You wonderfully explain that the ability to learn from mistakes carries over beyond the specific domain.

You further add that cognitive skills fostering resilience while learners process the difference between their (wrong) choices and the right answers are a great value add learning experiences can bring about. You also say, the ability they cultivate to accept that mistakes are okay as long as the lesson is learned is another brownie point that comes along with learning experiences.

But, interestingly, these intangibles contributing to learners’ personality growth (over and above their subject specific learning) just do not surface as salient features of the mistake-induced learning experiences.

Would you subscribe to the view that we should highlight these intangibles as the expected positive outcomes while scripting learning experiences?

Clark: I’m a strong believer in facilitating meta-cognition on top of learning experiences. We want these traits and skills, and yet we tend not to develop them! Further, they can’t be learned in isolation; they need to be learned in the course of learning other things. So yes, I think finding opportune times to make the elements of learning to learn ‘visible’ is a valuable addition we could and should be adding to our learning experience design. We shouldn’t have to, if our education system addressed this properly, but lieu of that, taking the onus upon ourselves is a valuable contribution. Note that there are nuances to making it work, but they’re worth wrestling with.

10. You highlight the wrong claim that people process images 60,000 times faster than text and therefore images should be preferred to text for effective communication. Looking at it from a slightly different perspective, we also see that it falls totally short of conviction when we expose it to the novel scenario.

Here, words – just them – trigger such vivid and sometimes surrealistic imagination that their visual counterparts (rendered in the form of movies) many a time pale in comparison.

Why do you think people so easily miss out on this axiom you’ve reinforced: Using the right media for the job is a better principle than simply using images indiscriminately?

Clark: There are a variety of reasons people converge on particular approaches. Familiarity is one, ease and cost of production is another. And we’re heavily influenced by what’s currently hot; so YouTube videos make everyone suddenly sure that video is the answer to eLearning! And they may also be unaware of the cost: over-production can add unnecessary cognitive load and undermine our own instructional intentions! Knowing the nuances of media may be a bit more work, but the alternative is doing things that don’t best expend our precious resources to achieve learning impact.

11. The stigma attached to learning designs focusing too much on knowledge (without making provision for application) is now slowly extending to blemish knowledge itself.

There is so much insistence on the DOING part that learning designers themselves have started believing that by making learners DO they help them gain the required knowledge automatically and therefore there’s no need for them to use the word ‘learn’ itself.

Could you please explain how too much practice without knowledge development can lead to insufficient flexibility in application?

Clark: Let’s be clear: practice with feedback, alone, can lead to learning. Kathy Sierra, in Badass, tells the story of chicken-sexing (determining the gender of newborn chicks). Nobody, apparently, can describe the necessary process. The only way people learn to do this is to make a guess and get feedback from an expert. Over time, they develop the ability! But it takes a lot of practice. And it’s a pretty simple outcome.

For more complex skills, however, and to shorten the learning curve, knowledge helps. Explicit models provide a framework for feedback and continual improvement. Similarly, examples help learners understand how the concepts are applied in context. And introductions can help learners understand viscerally why this is important. All these forms of knowledge make learning more efficient. In general, we want to give people frameworks that minimize the time to minimum competency and give them a structure to self-improve over time. Just as knowledge without practice is unlikely to lead to any meaningful change, learning without knowledge takes a very long time and very specific practice for any domain more complex than ‘male/female’.

12. Micro learning would probably top the list of wonderful concepts falling prey to popular misconceptions and misinterpretations.

Recalling some of the misconceptions you’ve mentioned: Chunk the content in smaller pieces so employees can learn in bits. Give them just-in-time videos as performance support, so they can get things easily done and also learn in the process.

You add that folks fail to realize learning takes constant, repeated practice over time, supported by periodical retrieval practice. In other words, an effective micro learning program has to be founded on sound instructional design principles.

Could you please cite a few examples of organizational contexts that would justifiably require a micro learning curricula design?

Clark: As you point out, when microlearning making sense depends on what you mean! For performance support, it could be maintaining machinery or instruments, repairing engines, or anytime something’s complex (e.g. hard to remember), contextualized, changing quickly, or is relatively infrequent in occurrence. Or just a place where there are a lot of steps to follow specifically. They could be ‘how to’ videos, checklists, or more.

For spaced learning, it gets into times when decisions must be made that are also complex, infrequent and/or important, but also unique or in situations where the context can be variable. Here, you can’t provide specific support, so instead you want to equip learners with models that they can apply to the situation. It might be dealing with customers (or potential ones). It might be making decisions under stressful situations like emergencies or with complex interactions (e.g. docking ships or rockets). You will want to develop the models, and their application, over time. Here’s when reactivating – reconceptualizing (reframing the models), recontextualizing (new examples), and reapplying (more practice) – makes a lot of sense. These are situations where varied, deliberate, and spaced practice are required.

Of course, the situations where the model I suggest for microlearning, contextualized learning support on top of performance support, would be places where you want to help them in the moment as much as possible, but also develop their understanding over time. So, you provide the performance support, but also provide a small amount of conceptualization around it, helping them develop their understanding as well. This would be for times when you want to decouple their ability from the support, or act in more flexible ways. So, a sample situation might be where you have specific requirements over what must be said, but want to be natural in how it’s said.

13. The apprehension – you have highlighted – that using humor in training could distract learners from the seriousness of the topic seems well-founded. But, you also add that ”a light mood can facilitate learning, particularly at the start of whatever learning experience we are designing”. You further advocate that learning can and should be ”hard fun”.

Could you please elucidate a bit on the “hard fun” part?

Clark: Raph Koster wrote a book called A Theory of Fun. In it, he made the case that what makes computer games fun is that they’re about learning. That comes from my own work, too, as I document in Engaging Learning. Vygotsky talked about the Zone of Proximal Development, where there’s this space in between what you can do easily and what you can’t do no matter the support you get. That space is where learning happens. And Csikszentmihalyi talked about the ‘flow’ state between what’s too easy (boring) and too hard (frustrating). In that ‘flow’ zone is where experiences are compelling. And they’re talking about the same thing! Thus, when you design it right (and it’s not just putting game and instructional designers in a room together; you have to understand the alignment), the learning should be intrinsically motivating, appropriately challenging, require exploration, and not be entirely predictable. When you do that, the learning’s effective and the experience is engaging. And that is ‘hard fun’.

14. You observe that for many, mobile learning (or mLearning) is about learning through a mobile device… however, this platform is an effective way to reach people where they are, with practical solutions – such as performance support and social & informal learning – that will help them perform better.

Could you please cite one or two examples of successful m-Learning programs that have helped positive turnarounds in organizations?

Clark: I’m not sure I know of any mLearning programs that have led to turnarounds in organizations (that’s a big ask), but there are some great success stories. For one, FedEx’s use of mobile bar code scanning has allowed them to do very accurate tracking of their package deliveries, supporting analytics and efficiency. Similarly, the Red Cross uses a mobile app when I donate blood to guide them through the process, handle things like timing, and scanning to ensure that the blood is whose it says it is. Other examples of success include your (Learnnovator’s) QR code scanning approach for the clients of a manufacturer to achieve success on installation, and BestBuy’s use of apps to scan product displays for more information, and more. Note that these are more about performance support, not learning. DuoLingo’s chunked up practice might be a mobile example of a company that’s succeeding in the more formal learning space.

15. That’s a pretty incisive clarification you’ve given on how learning designers should go about Bloom’s Taxonomy, winnowing its principles/guidelines: Focus on what learners need to do, and accompany that with the minimum information they need to be able to do it.

It really can’t get any simpler than this.

But, the surprising thing is, folks still find it hard to get to this ‘simple’ terrain. Why is that? Is it because they want to play safe or is it that they’re so conditioned to think complex that they miss the essence?

Clark: What’s nice about the taxonomy approach (and there are others) is that it implicitly provides a goal to move up the list. (Thought question: why doesn’t Kirkpatrick achieve the same?) But Bloom is entrenched; publishers do it because they believe universities want it, and universities pay attention because it’s in the materials. It’s a self-perpetuating system that makes it a de facto standard despite the problems. And it’s a misconception in my mind because it can be useful if it helps lift folks out of the ‘info dump & knowledge test’ mentality. Ultimately, however, I think we should be looking to simpler models with competencies and the associated design.

And, as a closing note, thanks for the opportunity and the challenging questions. They sometimes were difficult to wrestle with, but I think they do reflect some important questions people might have.

Thanks so much for sharing your valuable thoughts with us, Clark. We are sure the entire fraternity will stand to benefit from your insights. We wish you and your book the very best!

Check out these video bytes from Clark Quinn: