Almost Variation with Inequalities

I‘ve started thinking about Modules 0 for Grade 6. And I’ve written my first sequence for inequalities, which I’ll show below. Although I tried to design the sequence using ideas from variation theory, I found that the specific goal I had for this sequence—writing inequalities of the form x < c and c < x from number line models—did not make it easy to think of a boatload of questions I could ask, each slightly different from the previous one. Plus, I had some slightly more robust instructional goals in mind. Still, I found that it paid off to even just try thinking about variation.

So, I start with the video below, which serves as the first (and only) instructional worked example in the sequence.


I use the Silent Teacher method, wherein I essentially show the worked example twice, the second time with my voice annotating what I’m seeing, doing, and thinking as I write the inequality to represent the two models. In the lesson, I include a brief reminder to students above the video what the inequality symbols mean and what the equals sign means.

My assumptions with regard to this content are that students have seen and used inequality symbols for a long time before they get to Grade 6, though primarily with positive numbers and not variables or negatives. So, this represents a kind of “start-again” topic, which is one reason why I include the block models along with the number line model. It is a compromise between extending the concept and reviewing it: so I do a bit of both.

Another reason I include the block models is because they make a solid, albeit abstract, connection to the use of inequalities with algebraic expressions to express relative values in situations where we don’t know one of the values. We know that q above represents a number greater than x, but we can’t mark q on the number line because we don’t know its exact value. This is what the thinking question below the video is hopefully getting at. It’s numbered in case an instructor wants to assign the sequence to a student.

The Sequence

After the video, there is a sequence of a mere 8 questions. The first of these, shown at the right, is not a typical “Your Turn” type of question, where the student tries out a technique on a very similar problem. Here we unpack the other ways to express the inequalities shown in the video—it’s important to constantly make the point that there is almost always a few different ways of looking at mathematical relationships—and we include the equation, in part because research tells us that comparing the equals sign with other relational operators reinforces the correct relational view of the equals sign.

Next up is a more typical Your Turn, with a block model and number line model both closely mirroring the models shown in the video.

Students can write n or 1 to represent the single block (or the point labeled with both n and 1 on the number line). Doing so helpfully reinforces a slightly better meaning of “variable,” which is a letter that represents any quantity, known or unknown.

And here, for the first time (in a thinking question), I ask students to relate the number line model to the blocks model.

The next question in the sequence is an example of some minimal variation. What’s different here is that the m and n block towers switch sides in the illustration, and the inequality model on the number line shifts to the right. Everything else stays the way it was.

We could continue in this way, adding or subtracting blocks, switching sides, etc., but this kind of model has limitations that don’t allow for examining more of the variation space. But we can hint at the fact that adding the same number to both sides of an inequality doesn’t change the direction of the inequality.

And that’s what we do in the next exercise in the sequence. Here also, the known number is moved along the number line. The thinking question I ask here is:

Would adding 1 block to each tower change the direction of the inequality? Why or why not?

I phrase the question as a hypothetical because, strictly speaking, it’s not evident from the diagram that I added exactly 1 block to tower m.

And Now for a Big Change

Now we see how this isn’t really a sequence of minimal variation. One reason for the change-up is that I realized too late that the model I started with could only show the greater quantity as the unknown quantity. I thought about changing to a different model, one which could show the full range of variation, but I couldn’t think of a situation that worked.

This example, in which the larger quantity (the greater height) is the known, was too good to pass up. And it gave me a context to foreshadow subtracting both sides of an inequality by the same number, which is what (kind of) happens in the next exercise.

Here, though—and again—it was not plausible to hit this balance of operations idea directly (plus, it’s outside of the scope anyway). We only hint at it. But we still ask the thinking question—again, as a hypothetical—about whether subtracting the same value from both quantities changes the direction of the inequality.

The height examples, and perhaps all of the items in the sequence, lie somewhere between minimal variation and maximal variation. At some point while designing it, I had to stop searching for more perfect examples and just run with it.

The final two items in the sequence present two more (more or less abstract) situations where inequalities seem to fit.

The first, shown at the right, is the “swarm,” which contains too many items to count, though we can know for sure that the number is a greater value than 6. Here too is an example situation that better fits with the idea of a larger unknown that couldn’t be handled by the earlier block models.

In this example, I’ve switched up the labels on the number line for a small taste of minimal variation within all the macro variation going on.

Finally, there’s temperature and a quick example showing negative numbers.

What we get at here, also, is that we haven’t left the universe of comparing numbers just because we’re introducing a little algebra. Plus, I’ve eliminated the number line model here, just for a little flavor—and it’s too close in appearance to the thermometer levels. I didn’t want that confusion creeping in.

Sicklied O’er

research

My grandfather used to tell me a story about a young boy who was stuck in traffic with his family for hours because an 18-wheeler had got itself pinned under an overpass bridge ahead of them. The huge truck was wedged in so strongly and strangely that a flock of engineers had descended on the scene. They argued back and forth about their favorite physical and mathematical models that would unpin the trapped vehicle and release the miles-long stream of cars idling behind it on the freeway. This bickering went on for hours—until the boy got out of his car, walked up to the group of engineers, and shouted, “Why don’t you just let the air out the tires!”

It’s a nice story, precisely because it’s so rare and noticeable. We don’t notice unbroken strings of solved problems from experts, because that’s what we expect of experts—and, for the most part, what we get from them. We notice when they fail. And, because these failures are more noticeable than the far more boring and numerable successes, we fall prey to availability bias, and assume that expert failure occurs with much more regularity than it actually does. (In turn, we start to think that it’s maybe a good idea to keep students naive and, therefore, creative and open-minded rather than have them study things that other people have already figured out.) As Tom Nichols writes in The Death of Expertise:

At the root of all this is an inability among laypeople to understand that experts being wrong on occasion about certain issues is not the same thing as experts being wrong consistently on everything. The fact of the matter is that experts are more often right than wrong, especially on essential matters of fact. And yet the public constantly searches for the loopholes in expert knowledge that will allow them to disregard all expert advice they don’t like.

A 2008 study which put this folk notion of expert inflexibility to the test compared chess experts and novices, and measured the famous Einstellung effect in both groups across three experiments.

In the first experiment, the experts were given the board on the left and were instructed to find the shortest solution. The board on the left is designed to activate a motif familiar to chess experts (and thus activate Einstellung)—the smothered mate motif—which can be carried out using 5 moves. A shorter solution (3 moves) also exists, however.

If the experts failed to find the three-move solution, they were then given the board on the right. This board can be solved by the shorter three-move solution but not by the Einstellung motif of the smothered mate. The group of novices in the experiment were all given this second board (the one on the right) featuring the three-move mate solution without the Einstellung motif as well.

Findings

If knowledge corrupts insight, as it were, then the experts would, by and large, be fixated by the smothered mate sequence and miss the three-move solution. And this is indeed what happened—sort of. What the researchers found was that level of expertise correlated strongly with the results. Grandmasters (those with the highest levels of chess expertise) were not taken in by the Einstellung motif at all. Every one of them found the optimal three-move solution. However, experts with lower ratings, such as International Masters, Masters, and Candidate Masters, all experienced the Einstellung effect, with 50%, 18%, and 0%, respectively, finding the shorter solution on the first board, even though all of them found the optimal solution when it was presented on the second board, in the absence of the smothered mate motif.

The novices’ performance showed a positive correlation with rating also. Sixty-three percent of the highest rated (Class A) players in the novices group found the optimal solution on the right board, while 13% of Class B players and 0% of Class C players found the three-move solution. Thus, the Einstellung effect made International Masters experts perform like Class A players, Master players perform like Class B players, and Candidate Masters perform like Class C players.

Experiment 2 replicated the above finding in a slightly more naturalistic setting, and Experiment 3 did so with strategic Einstellungs instead of tactical ones.

Knowledge Is Essential for Cognitive Flexibility

While this study shows that Einstellung effects are powerful and observable in expert performance, it also demonstrates that the notion that expertise causes cognitive inflexibility is probably wrong.

The failure of the ordinary experts to find a better solution when they had already found a good one supports the view that experts can be vulnerable to inflexible thought patterns. But the performance of the super experts shows that ‘experts are inflexible’ would be the wrong conclusion to draw from this failure. The Einstellung effect is very powerful—the problem solving capability of our ordinary experts was reduced by about three SDs when a well-known solution was apparent to them. But the super experts, at least with the range of difficulty of problems used here, were less susceptible to the effect. Greater expertise led to greater flexibility, not less.

Knowledge, and the expertise inevitably linked to it, were also responsible for both forms of expert flexibility demonstrated in the experiments. The optimal solution was more likely to be noticed immediately, even before the nominally more familiar solution, among some super experts. Hence, expertise helped super experts avoid an Einstellung situation in the first place because they immediately found the optimal solution. Even when experts did not find the optimal solution immediately, expertise and knowledge were positively associated with the probability of finding the optimal solution after the non-optimal solution had been generated first. Finally, when knowledge discrepancy was minimized, as in the third experiment, super experts had sufficient resources to outperform their slightly weaker colleagues. In all three instances, knowledge was inextricably and positively related to expert flexibility. . . .

The training required to produce experts should not be seen as a source of potential problems but as a way to acquire the skill to deal effectively and flexibly with all the situations that can arise in the domain. Creativity is a consequence of expertise rather than expertise being a hindrance to creativity. To produce something novel and useful it is necessary first to master the previous knowledge in the domain. More knowledge empowers creativity rather than hurting it (e.g., Kulkarni & Simon, 1988; Simonton, 1997; Weisberg, 1993, 1999).

Makin’ Copies

research

At the heart of many calls to improve education is the taken-for-granted notion that because the world is now changing so rapidly, it is better for schools to focus on producing innovative and critical thinkers and ‘not just’ knowledgable students. The common instructional approach deployed, at all scales, to produce this effect—whether it is inquiry learning or personalized learning—is to remove or dramatically lessen the influence of knowledgable others.

Copying the effective behaviors of knowledgable others was a much more effective learning strategy than learning directly from the environment.

But important research on learning strategies in the wild shows that, at the very least, different intuitions are possible here. Researchers discovered—much to their surprise—that, in a rapidly changing environment, copying the effective behaviors of knowledgable others (social learning) could be a much more effective learning strategy than learning directly from the environment (asocial learning). This result held even when social learning was “noisy” and asocial learning was noise free.

The team has gone on to further investigate and apply their findings to other animal studies, and a book, Darwin’s Unfinished Symphony, was released just last year, detailing their work.

Social Learning Strategies Tournament

The method used for this research was a tournament in which the researchers designed a computer simulation environment and entrants to the tournament (104 in all) designed ‘agents’ that competed to survive in the generated environment by learning behaviors and applying them to receive payoffs for those behaviors. Each agent had three possible moves it could play: Observe, Innovate, or Exploit. The first two of these moves—Observe and Innovate—were learning moves, which allowed the agent to acquire new behaviors (or not in some cases), and the third move, Exploit, allowed agents to apply their acquired behaviors to receive a payoff (or not, depending on the environment and the behavior). As was mentioned above, Observe moves were “noisy,” whereas Innovate moves were noise free:

Innovate represented asocial learning, that is, individual learning stemming solely through direct interaction with the
environment, for example, through trial and error. An Innovate move always returned accurate information about the payoff of a randomly selected behavior previously unknown to the agent. Observe represented any form of social learning or copying through which an agent could acquire a behavior performed by another individual, whether by observation of or interaction with that individual. An Observe move returned noisy information about the behavior and payoff currently being demonstrated in the population by one or more other agents playing Exploit. Playing Observe could return no behavior if none was demonstrated or if a behavior that was already in the agent’s repertoire is observed and always occurred with error, such that the wrong behavior or wrong payoff could be acquired. The probabilities of these errors occurring and the number of agents observed were parameters we varied.

Some Key Findings

When the winning agent, which learned primarily by copying, was modified to learn only through Innovate moves, it placed last.

It was not effective to play a lot of learning moves. But when learning moves were played, agents which relied almost exclusively on Observe outperformed the rest, and an increase in copying was strongly positively correlated with higher payoffs. When the winning agent (called DISCOUNTMACHINE) was modified to learn only through Innovate moves, it placed last.

Even when learning by copying was made noisier—the probability and size of copying errors increased—agents which relied on it heavily still did best.

Finally, agents who combined asocial and social learning in more balanced ways (winning agents used social learning at least 95% of the time) performed worse than those who opted for social learning most of the time.

Why Copying Is Effective

It must be underscored, again, that, in more naturalistic environments there is a cost to asocial learning that copying does not have. Learning by observation is safer than learning by interacting directly with the environment, alone. But in this simulation, that cost was erased. And social learning (copying) STILL outperformed innovation, even when social learning was noisy (Observe “failed to introduce new behavior into an agent’s repertoire in 53% of all the Observe moves in the first tournament phase, overwhelmingly because agents observed behaviors they already knew”).

So, why was copying effective? The researchers boiled it down to being surrounded by rational agents, which I choose to rephrase as “knowledgable adults”:

Social learning proved advantageous because other agents were rational in demonstrating the behavior in their repertoire with the highest payoff, thereby making adaptive information available for others to copy. This is confirmed by modified simulations wherein social learners could not benefit from this filtering process and in which social learning performed poorly. Under any random payoff distribution, if one observes an agent using the best of several behaviors that it knows about, then the expected payoff of this behavior is much higher than the average payoff of all behaviors, which is the expected return for innovating. Previous theory has proposed that individuals should critically evaluate which form of learning to adopt in order to ensure that social learning is only used adaptively, but a conclusion from our tournament is that this may not be necessary. Provided the copied individuals themselves have selected the best behavior to perform from at least two possible options, social learning will be adaptive.

Any takeaways for education from this will be stretches. The research was a computer simulation, after all. But, whatever. My takeaway from all this is that, as long as there are knowledgable adults around, we should encourage students to learn directly from them. A milder takeaway (or maybe stronger, depending on your point of view): regardless of how adept you feel yourself to be in your social world, social worlds are not intuitive. What seems to make sense to you as a strong connection between ideas A and B (in this case, changing world → promote innovation) will not necessarily be effective just because a lot of people believe it and it makes intuitive sense. The way to change that is not to stop making those arguments, because few people do. The way to change it is to stop forwarding those kinds of arguments along when they are made. That way, the behavior won’t be copied. : )

Coda

I should add, by way of the quote below from Darwin’s Unfinished Symphony, that, although copying was a more successful strategy than innovating, it was not, by itself, the reason for success. What made the difference was better, more efficient, more accurate copying behaviors:

The tournament teaches us that natural selection will tend to favor those individuals who exhibit more efficient, more strategic, and higher-fidelity (i.e., more accurate) copying over others who either display less efficient or exact copying, or are reliant on asocial learning.

Variation and Example Spaces

I‘ve been thinking a lot about Craig Barton’s wonderful book How I Wish I’d Taught Maths and have been scanning three of his new websites, Variation Theory, Same Surface, Different Deep Problems, and Maths Venns, as well as some research and other books on variation, and a lot of online commentary, in anticipation of starting to implement these ideas in some way.

Writing Algebraic Expressions

As I was reading the last page of Mr Barton’s Book, I was working on instruction around writing algebraic expressions, so this is the topic kind of hovering next to me wherever I go, waiting for when I have time to dig in. This topic is a little more fraught than the purely procedural examples that have been circulating, so it’s worth exploring how variation can be applied to something a little looser.

What does writing algebraic expressions involve (for a beginner)? Well, if I force myself to ignore what other people think writing algebraic expressions involves (essentially ignoring standards and any written material on the topic), then I would say that writing algebraic expressions means to write something like s + 2 or 2 + s when presented with a question like “How old in years will Sam be in exactly two years?”1

This, then, I would call the first example in my example space. Or, rather, an example of an example in the example space—because, if this example is any good, then I will use it as an instructional example to start and leave it out of variation work, which is about PRACTICE, not instruction.2 So, something like this, with the brilliantly simple Silent Teacher method, mentioned in Barton’s book (and a few other places), though without the natural pauses and instructions for students to copy down the correct worked example used during a normal classroom implementation of this.

Try This One

Write an algebraic expression to model the situation.

How old in years was Sam exactly 10 years ago?

I would include a follow-up to this process, here involving a discussion around (a) the idea that the resulting algebraic expression represents an answer to the question of how old Sam will be—it’s just that one part of that expression is not known, (b) asking students to check that the answer makes sense, here by substituting different values for s and comparing the result to the situation, (c) the idea that any letter can be chosen for the variable, and (d) perhaps drawing a visual model of the result (an annotated number line). Some of these could be packaged into the instruction and question above, of course—or perhaps I’ll decide to split this up even more, considering how much “in addition to” I’ve now done about this—but I think that, in general, leaving room for a stepping back step at the end of this is a good idea, to catch the kind of overflow that is difficult to squeeze into expositions like this.

And Now Enters Variation

The paired problem here has opened up a dimension of variation—using addition or subtraction in the expression, so we can play with that during Intelligent Practice (really love that phrase). Technically, the instruction was open to all four operations, but I think it makes sense to focus exclusively on addition and subtraction, leaving multiplication and division expressions for another round.

Here’s what I cooked up.

  1. How much money in dollars did Sam have if he got exactly 10 dollars?
  1. How much money in dollars did Sam have if he got exactly 10 cents?
  1. How much money in dollars did Sam have if he got exactly 2 dollars?
  1. How much money in dollars did Sam have if he lost exactly 10 cents?
  1. How much money in dollars did Sam have if he got exactly 1 dollar?
  1. How much money in dollars did Sam have if he lost exactly 1 dollar?
  1. How much money in dollars did Sam have if he got exactly 50 cents?
  1. How much money in dollars did Sam have if he lost exactly 2 dollars?
  1. How much money in dollars did Sam have if he got exactly 25 cents?
  1. How much money in dollars did Sam have if he didn’t lose or gain any money?

After this, it might be good to have students cut out the strips and place them on a number line.

It’s interesting how much my experience and training rebels against this process. What I want to get to, right away, are the difficult and ambiguous situations. In particular, I started with, and then rejected, a variation sequence involving height: How tall in inches will Sam be if he grows 2 inches? The subtraction variation is bound to confuse: How tall in inches was Sam if he grew 2 inches? That’s tricky.

But knowing about and looking out for those tricky and ambiguous and interesting situations can serve you well creating instructional routines like this. It shows you where you’re going—and your example space can be richer and broader. And if you’re serious about implementing minimally different variation like this, it shows you how far away your knowledge really is from a beginner’s. You just have to learn to have more sympathy for learners who are encountering mathematics for the first time that you’ve seen a gazillion times.


  1. It’s important to me—at the moment, at least—that the examples in this example space should also involve identifying the correct unknown, rather than simply recording the unknown, as would happen with a question like, “Sam is s years old. How old will he be in 2 years?” or with an exercise of the form “2 more than a number.” In both of these cases, the unknown is entirely exposed.
  2. This is an important aspect of variation that I worry will be lost on U.S. teachers. Intelligent practice can’t happen, beneficially, until some acquisition has happened. In 20 years, I haven’t seen a robust public discussion about acquisition. The rhetoric around instruction in the States treats it as just one long assessment, though almost no one realizes that’s what it has become.








Mr Barton’s Book

It wasn’t too long ago—not even three years—that I finished reading David Didau’s terrific book (this one), so I still remember the excitement that I felt reading it, and watching all of the silly certainties of common wisdom in education being dismantled in front of my eyes, making way—I could only hope—for pedagogical practices informed by a real science of learning.

I felt a similar excitement reading Craig Barton’s book How I Wish I’d Taught Maths, because in this book, at long last, are many of those practices in one place, constructed, as readers will see, next to the debris of familiar canards and shallow reasoning that once guided parts of Barton’s teaching.

It is not a book full of proclamations about “best” practice. But you will find in this book a beautiful translation of the science of learning to the classroom. And far from the drudgery that one may imagine this to be, the joy of effective explicit instruction, for both teacher and students, comes through in every chapter of the author’s writing. It is serious, thorough, humble, and humane. And accessible: perhaps the greatest pleasure in reading it is knowing that you could turn around and start to implement many of these practices in short order—or, perhaps, that you already do these things, but don’t know why you should stick with them or how you could improve on them.

I have a lot of underlines and margin notes, but I think these three snippets together, from the chapter on problem solving and independence, are my favorites. The section starts, as they all do, with what the author used to think:

I used to love the sight of my students struggling through problems. Scratching heads, heavy sighs, and even the snap of a pencil thrown down in frustration were the soundtrack to learning. . . .

And then we are introduced to one of these problems, Question 23 from this paper (PDF), along with a deep concern for how novices will handle it. Contrast Barton’s new diagnosis below with common wisdom—that students ask why they are doing math because it is boring, tedious, procedural, or not relevant to their lives.

The task of choosing cards and calculating their totals may prove so cognitively demanding that novices do not have any spare cognitive capacity to recognise patterns. They do not realise that it is not the actual totals that matter, but whether those totals are odd or even. They just carry on regardless. Moreover, students are so consumed with the minutiae of the problem that no cognitive capacity remains to consider the global picture—why are they doing this? The result is that the novices may end up with an assortment of lists and totals, but not actually do anything with it—the fact that this is a probability question was pushed out of working memory long ago when the first set of cards was being processed.

As you might imagine, since the diagnosis is different from that received from common wisdom, the prescribed treatment is different too:

Before I set students off to work independently, I ensure they have enough domain-specific knowledge to solve problems on their own.

Although the snippets above are certainly grist for my mill, How I Wish I’d Taught Maths is not an ideological tome. It is eminently practical, taking the best ideas from all corners of the educational universe, squeezing them through the filter of cognitive science, and setting them in the right proportion to create a firm foundation that any educator—and especially any math educator—can use and build on. I highly highly recommend it to anyone who wants to strive for better in teaching and learning.


Do-It-Ourselves Education

When I lived for a year in Germany in high school as a foreign exchange student, I picked up, among many other things, a great quote from my host father: “Die Vorbereitung ist alles.” When said in my first language, it sounds fairly banal: “(The) preparation is everything.”

In both languages, though, the understood meaning of the phrase has a teleological ring: preparation is all-important for the goal you want to accomplish or some particular end you have in mind to achieve or realize. But I prefer a more extreme interpretation of the quote, in particular for education: that there is no achievement track, only a preparation track (multiple tracks in reality).

preparation

How do we get to that achievement above from, say, the middle of the preparation track? We don’t (in general). We move along the preparation track until we are in close enough proximity to the achievement to grab it. We don’t, in fact, keep our eyes on the prize. We keep our eyes on the preparation needed to move us within striking distance of the prize. Indeed, from way back in the middle of the track, the prize may look more tempting than it will appear close up (and it may be a mirage). And we may not be able to grasp it until we’re a little past it in our preparation.

Stay on the Right Track

The goal or achievement can be anything, really. So, for example, just cruise Twitter for a bit to find some quotable goal for education. The Feynman quote on the right is a good example. It is part of a quotation from a letter to a student in 1976, in which Feynman refers to himself in the third person, from The Quotable Feynman:

Just because Feynman says he is pro-nuclear power, isn’t any argument at all worth paying attention to because I can tell you (for I know) that Feynman doesn’t know what he is talking about when he speaks of such things. He knows about other things (maybe). Don’t pay attention to “authorities,” think for yourself.

Okay, great. Sincerely, that’s a great goal. I definitely would like to help students be appropriately mistrustful of authority—to the extent that it stimulates constructive thinking, not just having temper tantrums about authority. Who wouldn’t? So, let’s talk about distrusting authority as a goal for education.

The usefulness of the above interpretation about preparation is that now we must find the image of that goal somewhere along the preparation track and work out how we will connect the beginning and middle of the track to the point where that goal can be attained. Almost instantly we will see that we need to define what we (society) want for students. (For example, students have a lot of authority figures in their lives. Will they interpret the pro-skepticism message in a way that makes them start ignoring what their parents tell them? Does skepticism just mean that they have an ability to say, “I don’t think that’s right” and then never follow up?) But more importantly, we need to think about the steps along the path: What do students need to know first to understand skepticism and how to wield it appropriately? How does that ability progress over time? What knowledge is involved?

I don’t know about you, but when I deliberate on that simple Feynman quote for a while, I think of dozens of different sub-steps I would want to put in place along the preparation track from the goal back to the starting point. And these would probably break down into hundreds of smaller steps. Balancing appropriate skepticism—actionable skepticism, not armchair, consequence-free questioning—with the absolute necessity in modern life of trusting experts and authorities is lifelong work for adults who take on that challenge. If we want it to be an explicit goal for students—and not just a slogan we pass around on social media—then it will require a lot of work and technical planning.

My wish for 2018 and beyond is that, in addition to wanting these kinds of big things for students, we realize the hard, technical, scientific work involved in doing those things ourselves. Let’s leave behind the childish idea that, in order for students to achieve X, they just have to do X. That works for small things, not for anything worth having.

It is not science to know how to change centigrade to Fahrenheit. It’s necessary, but it is not exactly science. In the same sense, if you were discussing what art is, you wouldn’t say art is the knowledge of the fact that a 3-B pencil is softer than a 2-H pencil. It’s a distinct difference. That doesn’t mean an art teacher shouldn’t teach that, or that an artist gets along very well if he doesn’t know that.

–Richard Feynman