In the final chapter of our book How Learning Happens: Seminal Works in Educational Psychology and What They Mean in Practice, Carl Hendrick and I briefly describe ten deadly sins of education. Giving in to sins is often tempting, but if you do you’ll be implementing evidence-UNinformed education and flying in the face of evidence. What follows is a very abridged version of that chapter.
The learning pyramid supposedly reflects the effectiveness of different forms of teaching. According to the pyramid, pupils only remember 5% of what the teacher says, 10% of what they read, 20% of an audio-visual presentation, etc. The percentages vary in different pyramids, but that’s not important. What is important is that it’s nonsense.
Why? First, there’s no basis for such percentages. Even the institution that everyone quotes (National Training Laboratories) says they don’t have data to support them. Furthermore, the pyramid is simply a corruption of Edgar Dale’s cone of experience (1954), in which he indicated how media differ along a continuum from abstract (language, letters) to concrete (direct experience). Finally, even if the percentages were correct, you can’t do anything with it. No lesson is purely one or the other and just adding these percentages up teaches us that you could learn more than 100%!
People are all different and just as they prefer different foods, they also prefer different ways of learning. While it sounds and even feels logical that some children are visual learners (give them pictures, diagrams, charts), others auditory (give them a lecture or discussion), readers/writers (let them read and write), or kinaesthetic (give them physical experiences), there’s no evidence whatsoever for this. Looking this way at how children learn, and therefore how teachers should teach, has at least four problems. First, in most studies learning styles are determined based on what people say they prefer. It’s therefore about learning preferences and not learning styles. Second, there’s a big difference between what you prefer and what leads to better learning. Most of us prefer eating fatty and/or salty and/or sugary things. I think that we can all agree that succumbing to these preferences doesn’t constitute a healthy diet. Third, most so-called learning styles are based on specific types: people are classified into different groups. However, there’s no evidence for the existence of these distinct groups. But possibly the most important problem is that if we put learners in different boxes and teach accordingly (i.e., pigeonhole them), we create situations that instead of promoting learning, often hinder it.
There’s a new type of learner with specific competencies that enable them to use ICT effectively and efficiently; the digital native. Marc Prensky introduced this term in 2001 to denote a generation that has exceptional and unique thinking and learning characteristics that distinguish it from all previous generations. He concluded that we must design and introduce new forms of education that focus on their special gifts. Unfortunately, he based all of this on personal observations and not on research. Based on these claims we hear things like “Let’s Googlify education” and/or “Knowledge acquisition isn’t necessary”.
Don’t! There’s no evidence that young people today have any special skills (other than very fast-moving thumbs) that would allow them to learn differently. Any ‘evidence’ is purely experiential and/or anecdotal.
One of the competencies attributed to non-existent digital natives is multitasking. The problem is that we only have one brain and it can’t carry out more than one information processing (not automated) activity at a time. What we actually do is switch between tasks (i.e., task switching). But when we switch, we lose time and make mistakes. If we switch tasks, we (unconsciously) make a ‘decision’ to shift our attention from one task to another. Our brain then activates a rule to terminate processing of one task whereby we exit the cognitive schema we were using, and initiate another rule to enable processing of the other task with its concomitant schema. Switching between tasks takes time and distributing attention between these two tasks requires space in our working memory. The two tasks therefore interfere with each other. In short, we simply can’t multitask. If we try to do two or more things requiring thought and attention at the same time, we do things worse and take more time in total than if we had done them one after the other (i.e., serial monotasking).
We hear that just about all the “knowledge” we need can be Googled and, thus, we no longer need to know as much as we used to; we can look it up. But there wait! First, there’s no knowledge on the Internet; only information, of which a great deal is non-information or outright nonsense from questionable sources. Without a solid knowledge base we can do little with what we find on the Internet. Second, what we see and understand is determined by what we already know and not the other way around. Prior knowledge and experiences determine how we see, understand, and interpret the world around us. They also determine how well we can look up, find, select, and process (or evaluate) information available on the Internet. Unfortunately, in the best case, students only have minimal prior knowledge of a subject (they’re students; if they already had the knowledge, they’d be experts).
Related to this is the myth that knowledge has a limited expiration date (as perishable as fresh fish). This is nonsense too. The vast majority of what we have learned is still correct. There is a huge increase in information. But as said, without knowledge we can do little with it.
The premise behind problem-based learning is that the best way to learn to solve problems is to solve them. Wrong! To solve problems, we must first have knowledge of and skills in the problem domain. We can’t solve a chess problem without knowing how to play chess, just as we can’t solve a math problem without math knowledge. In other words, skills are domain specific. Also, we need a set of possible solution strategies plus knowledge of when it’s best use each strategy. This is called procedural knowledge (knowing what the steps are) and is very similar to computational thinking skills; analysing and decomposing a problem in smaller steps in order to solve it.
Finally, without domain-specific and procedural knowledge, problem-solving becomes an exercise in trial and error. This is neither effective nor efficient, especially since we’re constantly hitting walls because we’re doing it wrong (which can be quite frustrating).
Discovery learning doesn’t take into account the limitations of the working memory of learners. To learn by discovery, we must look for links between things and the principles that apply in the domain. Beginners, however, lack domain knowledge and have no systematic approach to finding it. This, therefore, requires a great deal of their working memory. The learner is faced with an explosion of combinations without knowledge to keep them under control. Moreover, this load on working memory doesn’t result in more knowledge in long-term memory as it was used to discover and not to learn.
In addition, this approach sees a child is a kind of miniature scientist. But children not only have less knowledge than a scientist, they also see and interpret the world differently (much more naively), think differently (concretely and not abstractly) and therefore experience the world differently. This working method/epistemology of the scientist isn’t an educational approach for the learner!
We often hear that pupils find learning situations boring and unattractive and therefore don’t learn well. People often use concepts such as motivation and engagement as keys to better education and as proxies for learning; as if being hyped about or engaged with something means that you’ve also learned something. The idea is that the more we motivate learners, the better they’ll learn. Unfortunately this isn’t the case. Don’t get us wrong. Of course motivation is great and motivated students will start on something sooner than if they aren’t motivated, but this is no guarantee for learning. In fact, if a student starts out motivated but doesn’t succeed, that motivation fades away very quickly and we’re worse off than if the learner was only lukewarm to begin with.
Research has shown that there’s neither a causal (motivation does not lead to better learning and performance) nor a reciprocal relationship (in the sense that motivation leads to learning and learning leads to motivation) between motivation and learning. It’s learning that leads to motivation. When we experience success, no matter how small that success is, it feeds our motivation to continue.
It’s weird. On the one hand, we hear that learning is boring and hard and should be fun, but on the other hand, everyone is talking about grit; putting your shoulders to the wheel and noses to the grindstone. According to the creator of the term Angela Lee Duckworth, grit a combination of perseverance, dedication, efficacy, and resilience. Researchers, however, have shown that grit is just old wine in new bottles and is nothing more than perseverance. They also found poor correlations between grit and learning performance and grit and remembering, while finding strong correlations between learning and cognitive ability (IQ), study habits, and skills. Even perseverance alone, without all the extra trimmings from Duckworth, was more strongly correlated with learning than grit!
The man who claimed that schools kill creativity - Sir Ken Robinson - presented a ‘strawman’ of the school as a place where teachers do nothing but preach from the pulpit and where students do nothing but listen obediently and do their homework. Shame on Sir Ken!
Strange here is that Sir Ken defines creativity as ”… the process of having / coming up with original ideas that have value - usually the result of the interaction of different disciplinary ways of seeing things.” In other words, based on domain specific knowledge! Without knowledge and skills which we acquire at school it’s impossible - except in the case of a luck - to come up with something of value. To put it simply: to think outside-of-the-box you have to know what’s in the box.
If you want to read in more depth what you shouldn’t do, but also would like to read about what the giants of our field (learning, education, instructional design) say that you should do, the we suggest going to the publisher or Amazon in your area and buying the book: