Making Parallelepipeds

We have talked about the cross product (here and here), so let’s move on to doing something marginally interesting with it: we’ll make a rectangular prism, or the more general parallelepiped.

A parallelepiped is shown at the right. It is defined by three vectors: u, v, and w. The cross product vector \(\mathtt{v \wedge w}\) is perpendicular to both v and w, and its magnitude, \(\mathtt{||v \wedge w||}\), is equal to the area of the parallelogram formed by the vectors v and w (something I didn’t mention in the previous two posts).

The perpendicular height of the skewed prism, or parallelepiped, is given by \(\mathtt{||u||cos(θ)}\).

The volume of the parallelepiped can thus be written as the area of the base times the height, or \(\mathtt{V = (||v \wedge w||)(||u||\text{cos}(θ))}\).

We can write \(\mathtt{\text{cos}(θ)}\) in this case as \[\mathtt{\text{cos}(θ) = \frac{(v \wedge w) \cdot u}{(||v \wedge w||)(||u||)}}\]

Which means that, after simplifying the volume equation, we’re left with \(\mathtt{V = (v \wedge w) \cdot u}\): so, the dot product of the vector perpendicular to the base and the slant height of the prism. The result is a scalar value, of course, for the volume of the parallelepiped, and, because it is a dot product, it is a signed value. We can get negative volumes, which doesn’t mean the volume is negative but tells us something about the orientation of the parallelepiped.

Creating Some Parallelepipeds

Creating prisms and skewed prisms can be done in Geogebra 3D, but here I’ll show how to create these figures from scratch using Three.js. Click and drag on the window to rotate the scene below. Right click and drag to pan left, right, up, or down. Scroll to zoom in and out.

Click on the Pencil icon above in the lovely Trinket window and navigate to the parallelepiped.js tab to see the code that makes the cubes. You can see that vectors are used to create the vertices (position vectors, so just points). Each face is composed of 2 triangles: (0, 1, 2) means to create a face from the 0th, 1st, and 2nd vertices from the vertices list. Make some copies of the box in the code and play around!

To determine the volume of each cube: \[\left(\begin{bmatrix}\mathtt{1}\\\mathtt{0}\\\mathtt{0}\end{bmatrix} \wedge \begin{bmatrix}\mathtt{0}\\\mathtt{1}\\\mathtt{0}\end{bmatrix}\right) \cdot \begin{bmatrix}\mathtt{0}\\\mathtt{0}\\\mathtt{1}\end{bmatrix} \mathtt{= 1}\]

Spooky Action at a Distance

I really like this recent post, called Tell Me More, Tell Me More, by math teacher Dani Quinn. The content is an excellent analysis of expert blindness in math teaching. The form, though, is worth seeing as well—it is a traditional educational syllogism, which Quinn helpfully commandeers to arrive at a non-traditional conclusion, that instructional effects have instructional causes, on the right:

The Traditional Argument An Alternative Argument
There is a problem in how we teach: We typically spoon-feed students procedures for answering questions that will be on some kind of test.

“There is a problem in how we teach: We typically show pupils only the classic forms of a problem or a procedure.”

This is why students can’t generalize to non-routine problems: we got in the way of their thinking and didn’t allow them to take ownership and creatively explore material on their own. “This is why they then can’t generalise: we didn’t show them anything non-standard or, if we did, it was in an exercise when they were floundering on their own with the least support.”

Problematically for education debates, each of these premises and conclusions taken individually are true. That is, they exist. At our (collective) weakest, we do sometimes spoon-feed kids procedures to get them through tests. We do cover only a narrow range of situations—what Engelmann refers to as the problem of stipulation. And we can be, regrettably in either case, systematically unassertive or overbearing.

Solving equations provides a nice example of the instructional effects of both spoon-feeding and stipulation. Remember how to solve equations? Inverse operations. That was the way to do equations. If you have something like \(\mathtt{2x + 5 = 15}\), the table shows how it goes.

Equation Step
\(\mathtt{2x + 5 \color{red}{- 5} = 15 \color{red}{- 5}}\) Subtract \(\mathtt{5}\) from both sides of the equation to get \(\mathtt{2x = 10}\).
\(\mathtt{\color{white}{+ 5 \,\,} 2x \color{red}{\div 2} = 10 \color{red}{\div 2}}\) Divide both sides of the equation by 2.
\(\mathtt{\color{white}{+ 5 \,\,}x = 5}\) You have solved the equation.

Do that a couple dozen times and maybe around 50% of the class freezes when they encounter \(\mathtt{22 = 4x + 6}\), with the variable on the right side, or, even worse, \(\mathtt{22 = 6 + 4x}\).

That’s spoon-feeding and stipulation: do it this one way and do it over and over—and, crucially, doing that summarizes most of the instruction around solving equations.

Of course, the lack of prior knowledge exacerbates the negative instructional effects of stipulation and spoon-feeding. But we’ll set that aside for the moment.

The Connection Between Premises and Conclusion

The traditional and alternative arguments above are easily (and often) confused, though, until you include the premise that I have omitted in the middle for each. These help make sense of the conclusions derived in each argument.

The Traditional Argument An Alternative Argument
There is a problem in how we teach: We typically spoon-feed students procedures for answering questions that will be on some kind of test.

“There is a problem in how we teach: We typically show pupils only the classic forms of a problem or a procedure.”

Students’ success in schooling is determined mostly by internal factors, like creativity, motivation, and self-awareness.

Students’ success in schooling is determined mostly by external factors, like amount of instruction, socioeconomic status, and curricula.

This is why students can’t generalize to non-routine problems: we got in the way of their thinking and didn’t allow them to take ownership and creatively explore material on their own. “This is why they then can’t generalise: we didn’t show them anything non-standard or, if we did, it was in an exercise when they were floundering on their own with the least support.”

In short, the argument on the left tends to diagnose pedagogical illnesses and their concomitant instructional effects as people problems; the alternative sees them as situation problems. The solutions generated by each argument are divergent in just this way: the traditional one looks to pull the levers that mostly benefit personal, internal attributes that contribute to learning; the alternative messes mostly with external inputs.

It’s Not the Spoon-Feeding, It’s What’s on the Spoon

I am and have always been more attracted to the alternative argument than the traditional one. Probably for a very simple reason: my role in education doesn’t involve pulling personal levers. Being close to the problem almost certainly changes your view of it—not necessarily for the better. But, roles aside, it’s also the case that the traditional view is simply more widespread, and informed by the positive version of what is called the Fundamental Attribution Error:

We are frequently blind to the power of situations. In a famous article, Stanford psychologist Lee Ross surveyed dozens of studies in psychology and noted that people have a systematic tendency to ignore the situational forces that shape other people’s behavior. He called this deep-rooted tendency the “Fundamental Attribution Error.” The error lies in our inclination to attribute people’s behavior to the way they are rather than to the situation they are in.

What you get with the traditional view is, to me, a kind of spooky action at a distance—a phrase attributed to Einstein, in remarks about the counterintuitive consequences of quantum physics. Adopting this view forces one to connect positive instructional effects (e.g., thinking flexibly when solving equations) with something internal, ethereal and often poorly defined, like creativity. We might as well attribute success to rabbit’s feet or lucky underwear or horoscopes!

Teaching and Learning Coevolved?

Just a few pages in to David Didau and Nick Rose’s new book What Every Teacher Needs to Know About Psychology, and I’ve already come across what is, for me, a new thought—that teaching ability and learning ability coevolved:

Strauss, Ziv, and Stein (2002) . . . point to the fact that the ability to teach arises spontaneously at an early age without any apparent instruction and that it is common to all human cultures as evidence that it is an innate ability. Essentially, they suggest that despite its complexity, teaching is a natural cognition that evolved alongside our ability to learn.

Or perhaps this is, even for me, an old thought, but just unpopular enough—and for long enough—to seem like a brand new thought. Perhaps after years of exposure to the characterization of teaching as an anti-natural object—a smoky, rusty gearbox of torture techniques designed to break students’ wills and control their behavior—I have simply come to accept that it is true, and have forgotten that I had done so.

Strauss, et. al, however, provide some evidence in their research that it is not true. Very young children engage in teaching behavior before formal schooling by relying on a naturally developing ability to understand the minds of others, known as theory of mind (ToM).

Kruger and Tomasello (1996) postulated that defining teaching in terms of its intention—to cause learning, suggests that teaching is linked to theory of mind, i.e., that teaching relies on the human ability to understand the other’s mind. Olson and Bruner (1996) also identified theoretical links between theory of mind and teaching. They suggested that teaching is possible only when a lack of knowledge can be recognized and that the goal of teaching then is to enhance the learner’s knowledge. Thus, a theory of mind definition of teaching should refer to both the intentionality involved in teaching and the knowledge component, as follows: teaching is an intentional activity that is pursued in order to increase the knowledge (or understanding) of another who lacks knowledge, has partial knowledge or possesses a false belief.

The Experiment

One hundred children were separated into 50 pairs—25 pairs with a mean age of 3.5 and 25 with a mean age of 5.5. Twenty-five of the 50 children in each age group served as test subjects (teachers); the other 25 were learners. The teachers completed three groups of tasks before teaching, the first of which (1) involved two classic false-belief tasks. If you are not familiar with these kinds of tasks, the video at right should serve as a delightfully creepy precis—from what appears to be the late 70s, when every single instructional video on Earth was made. The second and third groups of tasks probed participants’ understanding that (2) a knowledge gap between teacher and learner must exist for “teaching” to occur and (3) a false belief about this knowledge gap is possible.

Finally, children participated in the teaching task by teaching the learners how to play a board game. The teacher-children were, naturally, taught how to play the game prior to their own teaching, and they were allowed to play the game with the experimenter until they demonstrated some proficiency. The teacher-learner pair was then left alone, “with no further encouragement or instructions.”

The Results

Consistent with the results from prior false-belief studies, there were significant differences between the 3- and 5-year-olds in Tasks (1) and (3) above, both of which relied on false-belief mechanisms. In Task (3), when participants were told, for example, that a teacher thought a child knew how to read when in fact he didn’t, 3-year-olds were much more likely to say that the teacher would still teach the child. Five-year-olds, on the other hand, were more likely to recognize the teacher’s false belief and say that he or she would not teach the child.

Intriguingly, however, the development of a theory of mind does not seem necessary to either recognizing the need for a special type of discourse called “teaching” or to teaching ability itself—only to a refinement of teaching strategies. Task (2), in which participants were asked, for instance, whether a teacher would teach someone who knew something or someone who didn’t, showed no significant differences between 3- and 5-year-olds in the study. But the groups were significantly different in the strategies they employed during teaching.

Three-year-olds have some understanding of teaching. They understand that in order to determine the need for teaching as well as the target learner, there is a need to recognize a difference in knowledge between (at least) two people . . . Recognition of the learner’s lack of knowledge seems to be a necessary prerequisite for any attempt to teach. Thus, 3-year-olds who identify a peer who doesn’t know [how] to play a game will attempt to teach the peer. However, they will differ from 5-year-olds in their teaching strategies, reflecting the further change in ToM and understanding of teaching that occurs between the ages of 3 and 5 years.

Coevolution of Teaching and Learning

The study here dealt with the innateness of teaching ability and sensibilities but not with whether teaching and learning coevolved, which it mentions at the beginning and then leaves behind.

It is an interesting question, however. Discussions in education are increasingly focused on “how students learn,” and it seems to be widely accepted that teaching should adjust itself to what we discover about this. But if teaching is as natural a human faculty as learning—and coevolved alongside it—then this may be only half the story. How students (naturally) learn might be caused, in part, by how teachers (naturally) teach, and vice versa. And learners perhaps should be asked to adjust to what we learn about how we teach as much as the other way around.

Those seem like new thoughts to me. But they’re probably not.

Providing Bad Intel

research

A really nice thing about scientific research is its transparency. Researchers write down the methods they use in their experiments—sometimes in excruciating detail—so that others can try to replicate their work if they choose. And scrutinizable methods allow us and other researchers to think about issues that the original experimenters might have overlooked—or, at least, didn’t mention in their published work.

Every once in a while we come across research which individuals themselves can simulate at home on a computer, even if they don’t have any participants, and this allows us to bring the experiment to life a little more than can be done with text descriptions.

The research I look at in this post is such a study. Students in the study (81 in all, from 7 to 10 years of age) were given an “app” very similar to the one shown below. Play with it a bit by clicking on the animal pictures to see what students were exposed to in this study.

The Method

In this study, students were presented with a question and then an explanation answering that question for the 12 animals shown above (images used in the study were different from above). Students rated the quality of explanations about animal biology on a 5-point scale. (In the version above, your ratings are not recorded. You can just click on the image of the rating system to move on.) The audio recorded in the app above use the questions and explanations from the study verbatim, though in the actual study two different people speak the questions and explanations (above, it’s just me).

As you could no doubt tell if you played around with the app above, some of the explanations are laughably bad. Researchers designated these as circular explanations (e.g., How do colugos use their skin flaps to travel? Their skin flaps help them to move from one place to another). The other, better explanations were identified as mechanistic explanations (e.g., How do thorny dragons use the grooves between their thorns to help them drink water? Their grooves collect water and send the water to their mouths). After rating the explanation, students were then given a choice to either get more information about the animal or to move on to a different animal. Here again, all you get is a screen to click on, and any click takes you back to the main screen with the 12 animals. In the actual study, students were given an even more detailed mechanistic explanation when clicking to get more information (e.g., Thorny dragons have grooves between their thorns, which are able to collect water. The water is drawn from groove to groove until it reaches their mouths, so they can suck water from all over their bodies).

The Curious Case of Curiosity

What the researchers found was that, in general, students were significantly more likely to click to get more information on an animal when the explanation given was circular. And, importantly, students were more likely to click to get more information when they rated the explanation as poor. This behavior—of clicking to get more information—was operationalized as curiosity and can be explained using the deprivation theory of curiosity.

In everyday life, children sometimes receive weak explanations in response to their questions. But what do children do when they receive weak explanations? According to the deprivation theory of curiosity, if children think that an explanation is unsatisfying, then they should sometimes feel inclined to seek out a better answer to their question to bolster their knowledge; the same is not true for explanations appraised as high in quality. To our knowledge, our research is the first to investigate this theory in regards to children’s science learning, examining whether 7- to 10-year-olds are more likely to seek out additional information in response to weak explanation than informative ones in the domain of biology.

But is that really curiosity? Do I stimulate your curiosity about colugos’ skin flaps by not really answering your questions about them? We can more easily answer no to this question if we assume that Square 1 represents students’ wanting to know something about colugos’ skin flaps. In that case, the initial question stimulates curiosity, as it were, and the non-explanation simply fails to satisfy this curiosity, or initial desire for knowledge. The circular explanation has not made them curious or even more curious. They were already curious. Not helping them scratch that itch just fails to move them to Square 2, which is where they wanted to go after hearing the question (knowing something about how colugos’ skin flaps work). The fact that students with unscratched itches were more likely to go to Square 3 is not surprising, since Square 3, for them, was actually Square 2, the square that everyone wanted to get to.

An Unavoidable Byproduct of Quality Teaching

If you are more inclined to believe the above interpretation, as I am, it might seem that we still must contend with the evidence that quality explanations were indeed shown to reduce information-seeking, relative to the levels of information-seeking shown for circular explanations. But this is not necessarily the case. What we see, from this study at least, is that not scratching the initial itch likely caused a different behavior in students than did scratching it. A clicking behavior did increase for students who still had itches, but this does not mean that it decreased for students who had no itch. We have evidence here that bad explanations are recognizably bad. We do not have evidence suggesting that quality explanations make students incurious.

If this is the case, though—if quality explanations reduce curiosity—it seems likely to me that it is simply an unavoidable byproduct of quality teaching. One that can be anticipated and planned for. Explanations are, after all, designed to reduce curiosity, in some sense. What high quality explanations do—in every scientific field and likely in our everyday lives—is move us on to different, better things to be curious about.


Thinking About and Thinking With

I have a tendency, when writing blog posts, to leave important things unsaid. So, let me fix that up front before I forget. What I wanted to say here was that, in my view, learning doesn’t happen unless we tick off all three boxes: encoding, consolidation, and retrieval.

It’s not that learning gets better or stronger when more of those boxes are ticked off. Learning isn’t possible—to some degree of certainty—in the first place without all three. And it’s not the case that focusing on just retrieval instead of just encoding or just consolidation represents some kind of revolution in pedagogical thinking. You’re simply ignoring one or two vital components of learning when before you were ignoring one or two other ones. Learning can still happen even when we don’t think about one or two (or all three) of the above components, but then it’s haphazard, random, implicit, and/or incidental. (In that case, learning comes down to mental horsepower and genes rather than processes over which we have some control.) All three components still must be addressed for learning to occur; it’s just that we can decide to not be in control of one or all of them (to students’ detriment).

But even then we’re not done. We’ve covered the components of the process of learning, but all three of those components intersect with another dimension of learning, which describes the products of learning: thinking about and thinking with.

Thinking With

In the previous post linked above, the examples of slope could all be categorized as “thinking about” slope. Put too simply, encoding the concept of slope means absorbing information about slope, retrieving knowledge about slope means remembering the slope concept and saying your knowledge out loud or writing it down, and consolidating knowledge about slope means practicing, such that what is encoded stays encoded and what is known can be retrieved.

All of this—encoding, consolidation, and retrieval—must happen with “thinking with” as well as with “thinking about.” Encoding–Thinking With, for example, would involve absorbing information about how slope can be applied to do other things, whether mathematically or in the real world. Common examples include designing wheelchair ramps (which have ADA-recommended height-length ratios of 1 : 12), measuring and comparing the steepnesses of things, and determining whether two lines are parallel or perpendicular (or not either). Consolidating–Thinking With would involve practice with that encoded knowledge—solving word problems is a typical example. Finally, Retrieving–Thinking With would involve remembering that encoded knowledge, particularly after some time has passed, say by using slope to solve a programming problem or a problem on a test.

All six boxes have to be checked off for learning to occur (such that it is within our control).

Teach Thinking With

In education, we have difficulties—again, in my view—with Encoding–Thinking With, and Consolidating–Thinking With. As far as these two are concerned, it is rare in my experience to see guidance and practice on a wide variety of different problems involving thinking with (for example) slope to answer questions that aren’t about slope. We tend to think that all we should do is give students a bunch of think-about slope facts and then hope for them to magically retrieve and apply those to thinking-with situations. We misunderstand transfer as some kind of conjuring out of thin air, so that’s what we give students—thin air. Then we stand back and hope to see some conjuring. When this doesn’t produce results that we’d like, we—for some unimaginably stupid reason—blame knowing facts for the problem, and instead of supplementing that knowledge with thinking-with teaching, we swap them. Which is much worse than what it tries to replace.

Instead, it is necessary to teach students how to think with slope and many other mathematical concepts, and to provide them with practice in thinking with these concepts. Thinking with is as much knowledge as thinking about is.

One of my favorite examples of thinking with slope—and one which I have, admittedly, not yet written a lesson about—has to do with drawing convex or concave quadrilaterals. Given 4 coordinate pairs for points that can form a convex or concave quadrilateral (no three points lie on the same line, etc.), how can I decide, somewhat algorithmically, on the order in which I should connect the points, such that the line segments I actually draw do create a quadrilateral and not the image on the far right?

One way to go about it is to first select the leftmost point—the point with the lowest x-coordinate (there could be two, with equal x-coordinates, but I’ll leave that to the reader to figure out). Then calculate the slope of each line connecting the leftmost point with each other point. The order in which the points can be connected is the order of the slopes from least to greatest. This process would create a different proper quadrilateral than the one shown in the middle above.

Checking Off All the Boxes

Students’ minds are not magical. They don’t turn raw facts magically into applied understanding (the extreme traditionalist view), and they don’t magically vacuum up knowledge hidden in applied contexts (the extreme constructivist view). Put more accurately, this kind of magic does happen (which is why we believe in it), but it happens outside of our control, as a result of genetic and socioeconomic differences, so we can take no credit for it.

Importantly, ignoring components of students’ learning, for whatever reason, subjects them to a roll of the dice. Those students who start behind stay behind, and those who are underserved stay so. We seem to have enough leftover energy to try our hand at amateur psychology and social-emotional learning. Why not take a fraction of that energy and channel it into, you know, plain ol’ teaching?

Intuition and Domain Knowledge

Can you guess what the graphs below show? I’ll give you a couple of hints: (1) each graph measures performance on a different task, (2) one pair of bars in each graph—left or right—represents participants who used their intuition on the task, while the other pair of bars represents folks who used an analytical approach, and (3) one shading represents participants with low domain knowledge while the other represents participants with high domain knowledge (related to the actual task).

It will actually help you to take a moment and go ahead and guess how you would assign those labels, given the little information I have provided. Is the left pair of bars in each graph the “intuitive approach” or the “analytical approach”? Are the darker shaded bars in each graph “high knowledge” participants or “low knowledge” participants?

When Can I Trust My Gut?

A 2012 study by Dane, et. al, published in the journal Organizational Behavior and Human Decision Processes, sets out to address the “scarcity of empirical research spotlighting the circumstances in which intuitive decision making is effective relative to analytical decision making.”

To do this, the researchers conducted two experiments, both employing “non-decomposable” tasks—i.e., tasks that required intuitive decision making. The first task was to rate the difficulty (from 1 to 10) of each of a series of recorded basketball shots. The second task involved deciding whether each of a series of designer handbags was fake or authentic.

Why these tasks? A few snippets from the article can help to answer that question:

Following Dane and Pratt (2007, p. 40), we view intuitions as “affectively-charged judgments that arise through rapid, nonconscious, and holistic associations.” That is, the process of intuition, like nonconscious processing more generally, proceeds rapidly, holistically, and associatively (Betsch, 2008; Betsch & Glöckner, 2010; Sinclair, 2010). [Footnote: “This conceptualization of intuition does not imply that the process giving rise to intuition is without structure or method. Indeed, as with analytical thinking, intuitive thinking may operate based on certain rules and principles (see Kruglanski & Gigerenzer, 2011 for further discussion). In the case of intuition, these rules operate largely automatically and outside conscious awareness.”]

As scholars have posited, analytical decision making involves basing decisions on a process in which individuals consciously attend to and manipulate symbolically encoded rules systematically and sequentially (Alter, Oppenheimer, Epley, & Eyre, 2007).

We viewed [the basketball] task as relatively non-decomposable because, to our knowledge, there is no universally accepted decision rule or procedure available to systematically break down and objectively weight the various elements of what makes a given shot difficult or easy.

We viewed [the handbag] task as relatively non-decomposable for two reasons. First, although there are certain features or clues participants could attend to (e.g., the stitching or the style of the handbags), there is not necessarily a single, definitive procedure available to approach this task . . . Second, because participants were not allowed to touch any of the handbags, they could not physically search for what they might believe to be give-away features of a real or fake handbag (e.g., certain tags or patterns inside the handbag).

Results

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As you can see in the graphs at the right (hover for expertise labels), there was a fairly significant difference in both tasks between low- and high-knowledge participants when those participants approached the task using their intuition. In contrast, high- and low-knowledge subjects in the analysis condition in each experiment did not show a significant difference in performance. (The decline in performance of the high-knowledge participants from the Intuition to the Analysis conditions was only significant in the handbag experiment.)

It is important to note that subjects in the analysis conditions (i.e., those who approached each task systematically) were not told what factors to look for in carrying out their analyses. For the basketball task, the researchers simply “instructed these participants to develop a list of factors that would determine the difficulty of a basketball shot and told them to base their decisions on the factors they listed.” For the handbag task, “participants in the analysis condition were given 2 min to list the features they would look for to determine whether a given handbag is real or fake and were told to base their decisions on these factors.”

Also consistent across both experiments was the fact that low-knowledge subjects performed better when approaching the tasks systematically than when using their intuition. For high-knowledge subjects, the results were the opposite. They performed better using their intuition than using a systematic analysis (even though the ‘system’ part of ‘systematic’ here was their own system!).

In addition, while the combined effects of approach and domain knowledge were significant, the approach (intuition or analysis) by itself did not have a significant effect on performance one way or the other in either experiment. Domain knowledge, on the other hand, did have a significant effect by itself in the basketball experiment.

Any Takeaways for K–12?

The clearest takeaway for me is that while knowledge and process are both important, knowledge is more important. Even though each of the tasks was more “intuitive” (non-decomposable) than analytical in nature, and even when the approach taken to the task was “intuitive,” knowledge trumped process. Process had no significant effect by itself. Knowing stuff is good.

Second, the results of this study are very much in line with what is called the ‘expertise reversal effect’:

Low-knowledge learners lack schema-based knowledge in the target domain and so this guidance comes from instructional supports, which help reduce the cognitive load associated with novel tasks. If the instruction fails to provide guidance, low-knowledge learners often resort to inefficient problem-solving strategies that overwhelm working memory and increase cognitive load. Thus, low-knowledge learners benefit more from well-guided instruction than from reduced guidance.

In contrast, higher-knowledge learners enter the situation with schema-based knowledge, which provides internal guidance. If additional instructional guidance is provided it can result in the processing of redundant information and increased cognitive load.

Finally, one wonders just who it is we are thinking about more when we complain, especially in math education, that overly systematized knowledge is ruining the creativity and motivation of our students. Are we primarily hearing the complaints of the 20%—who barely even need school—or those of the children who really need the knowledge we have, who need us to teach them?




ResearchBlogging.org Dane, E., Rockmann, K., & Pratt, M. (2012). When should I trust my gut? Linking domain expertise to intuitive decision-making effectiveness Organizational Behavior and Human Decision Processes, 119 (2), 187-194 DOI: 10.1016/j.obhdp.2012.07.009

Telling vs. No Telling

So, with that in mind, let’s move on to just one of the dichotomies in education, that of “telling” vs. “no telling,” and I hope the reader will forgive my leaving Clarke’s paper behind. I recommend it to you for its international perspective on what we discuss below.

“Reports of My Death Have Been Greatly Exaggerated”

We should start with something that educators know but people outside of education may not: there can be a bit of an incongruity, shall we say, between what teachers want to happen in their classrooms, what they say happens in their classrooms, and what actually happens there. Given how we talk and what we talk about on social media—and even in face-to-face conversations—and the sensationalist tendencies of media reports about education, an outsider could be forgiven, I think, for assuming that teachers have been moving en masse away from the practice of explicit instruction.

There is a large body of research which would suggest that this assumption is almost certainly “greatly exaggerated.”

Typical of this research is a small 2004 study (PDF download) in the U.K. which found that primary classrooms in England remained places full of teacher talk and “low-level” responding by students, despite intentions outlined in the 1998–1999 National Literacy and National Numeracy Strategies. The graph at the right, from the study, shows the categories of discourse observed and a sense of their relative frequencies.

John Goodlad made a similar and more impactful observation in his much larger study of over 1,000 classrooms across the U.S. in the mid-80s (I take this quotation from the 2013 edition of John Hattie’s book Visible Learning, where more of the aforementioned research is cited):

In effect, then, the modal classroom configurations which we observed looked like this: the teacher explaining or lecturing to the total class or a single student, occasionally asking questions requiring factual answers; . . . students listening or appearing to listen to the teacher and occasionally responding to the teacher’s questions; students working individually at their desks on reading or writing assignments.

Thus, despite what more conspiracy-oriented opponents of “no telling” sometimes suggest, the monotonic din of “understanding” and “guide on the side” and “collaboration” we hear today—and have heard for decades—is not the sound of a worldview that has, in practice, taken over education. Rather, it is one of a seemingly quixotic struggle on the part of educators to nudge each other—to open up more space for students to exercise independent and critical thinking. This a finite space, and something has to give way.

Research Overwhelmingly Supports Explicit Instruction

Teacher as
Activator
dTeacher as Facilitatord
Teaching students self-verbalization0.76Inductive Teaching0.33
Teacher clarity0.75Simulation and gaming0.32
Reciprocal teaching0.74Inquiry-based teaching0.31
Feedback0.74Smaller classes0.21
Metacognitive strategies0.67Individualised instruction0.22
Direct instruction0.59Web-based learning0.18
Mastery learning0.57Problem-based learning0.15
Providing worked examples0.57Discovery method (math)0.11

On the other hand, it is manifestly clear from the research literature that, when student achievement is the goal, explicit instruction has generally outperformed its less explicit counterpart.

The table at the left, taken from Hattie’s book referenced above, directly compares the effect sizes of various explicit and indirect instructional protocols, gathered and interpreted across a number of different meta-analyses in the literature.

Results like these are not limited to the K–12 space, nor do they involve only the teaching of lower-level skills or teaching in only in well-structured domains, such as mathematics. These are robust results across many studies and over long periods of time.

And while research supporting less explicit instructional techniques is out there (as obviously Hattie’s results also attest), there is much less of it—and certainly far less than one would expect given the sheer volume of rhetoric in support of such strategies. On this point, it is worth quoting Sigmund Tobias at some length, from his summarizing chapter in the 2009 book Constructivist Instruction: Success or Failure?:

When the AERA 2007 debate was organized, I described myself as an eclectic with respect to whether constructivist instruction was a success or failure, a position I also took in print earlier (Tobias, 1992). The constructivist approach of immersing students in real problems and having them figure out solutions was intuitively appealing. It seemed reasonable that students would feel more motivated to engage in such activities than in those occurring in traditional classrooms. It was, therefore, disappointing to find so little research documenting increased motivation for constructivist activities.

A personal note may be useful here. My Ph.D. was in clinical psychology at the time when projective diagnostic techniques in general, and the Rorschach in particular, were receiving a good deal of criticism. The logic for these techniques was compelling and it seemed reasonable that people’s personality would have a major impact on their interpretation of ambiguous stimuli. Unfortunately, the empirical evidence in support of the validity of projective techniques was largely negative. They are now a minor element in the training of clinical psychologists, except for a few hamlets here or there that still specialize in teaching about projective techniques.

The example of projective techniques seems similar to the issues raised about constructivist instruction. A careful reading and re-reading of all the chapters in this book, and the related literature, has indicated to me that there is stimulating rhetoric for the constructivist position, but relatively little research supporting it. For example, it is encouraging to see that Schwartz et al. (this volume) are conducting research on their hypothesis that constructivist instruction is better for preparing individuals for future learning. Unfortunately, as they acknowledge, there is too little research documenting that hypothesis. As suggested above, such research requires more complex procedures and is more time consuming, for both the researcher and the participants, than procedures advocated by supporters of explicit instruction. However, without supporting research these remain merely a set of interesting hypotheses.

In comparison to constructivists, advocates for explicit instruction seem to justify their recommendations more by references to research than rhetoric. Constructivist approaches have been advocated vigorously for almost two decades now, and it is surprising to find how little research they have stimulated during that time. If constructivist instruction were evaluated by the same criterion that Hilgard (1964) applied to Gestalt psychology, the paucity of research stimulated by that paradigm should be a cause for concern for supporters of constructivist views.

Both the Problem and the Solution

So, it seems that while a “telling” orientation is better supported by research, it is also identified as a barrier, if not the barrier, to progress. And it seems that a lot of our day-to-day struggle with the issue centers around the negative consequences of continued unsuccessful attempts at resolving this paradox.

Yet perhaps we should see that this is not a paradox at all. Of course it is a problem when students learn to rely heavily on explicit instruction to make up their thinking, and it is perfectly appropriate to find ways of punching holes in teacher talk time to reduce the possibility of this dependency. But we could also research ways of tackling this explicitly—differentiating ways in which explicit instruction can solicit student inquiry or creativity and ways in which it promotes rule following, for example.

It is at least worth considering that some of our problems—particularly in mathematics education—have less to do with explicit instruction and more to do with bad explicit instruction. If dealing with instructional problems head on is more effective (even those that are “high level,” such as creativity and critical thinking), then we should be making the sacrifices necessary to give teachers the resources and training required to meet those challenges, explicitly.

Learn: You Keep Using That Word

Sometimes kids say “nothing” when their parents ask them what they learned in school today. And, although that response is something we don’t want to hear, it is probably closer to the truth than we want to believe, because, as we all most certainly know, learning doesn’t really happen in a single class period. And, when it does, it’s not learning per se, but encoding, consolidation, or retrieval—or some mixture of the three.

Encoding

The encoding stage involves introducing you to some knowledge pattern in the natural, social, or academic environment. For example, you may know ratios and rates, how to graph lines on the coordinate plane, and what steepness is, but at some point you are completely new to the concept of slope—which packages those former concepts into a unique bundle—so encoding is what happens when you are first introduced to slope.

There are a few important things to note here. First, slope could have been introduced, or encoded, as an isolated dot. (Well, not exactly. Nothing is ever completely “isolated.” But you get the idea.) Second, regardless whether it is encoded as a standalone concept or as a package of concepts, slope is a new object of knowledge. It is perhaps possible now for the slope blob above to interact or connect with the green blob of content knowledge, whereas none of the individual items can do so. And, third, whatever we mean by slope above, we cannot mean the entire concept of slope (whatever that means anyway).

Consolidation

The new concept of slope on the right is a little too complete to represent encoding, plus any structure created there fades quickly over time like pictures in Back to the Future (forgetting). This is where consolidation comes in. Consolidation solidifies and maintains the arrangements of knowledge components assembled by encoding.

Generally, consolidation is associated with simple practice—i.e., practicing the concept you have encoded rather than extending or altering the concept in any way. But it is as true to say that you are learning slope via simple practice as it is to say that you are doing so by encoding the concept in an introductory lesson.

Retrieval

Finally, there is retrieval, which is the process of reconstructing an encoded concept from memory in response to a natural or artificial stimulus. What is the slope of a horizontal line? The answer to this question requires triggering the slope concept, where the answer may be directly stored, or you may have to drill down into the slope package above—into the ratios and rates concepts—to figure out that the slope of a horizontal line is a 0 rise over some nonzero run, so the answer is 0. Or, the fact that the slope of a horizontal line is 0 can be stored together with the concept package shown above, giving you two ways to figure out the answer.

Why should retrieving a concept to answer questions be considered a part of learning that concept? Because, at minimum, retrieving strengthens an encoded concept.

Making New Functions

Algebra students usually learn at some point in their studies that you can dilate a function like \(\mathtt{f(x)=x^2}\) by multiplying it by a constant value. Usually the multiplier is written as \(\mathtt{A}\), so you get \(\mathtt{f(x)=Ax}\), which can be \(\mathtt{f(x)=2x^2}\) or \(\mathtt{f(x)=3x^2}\) or \(\mathtt{f(x)=\frac{1}{2}x^2}\), and so on.

What we focus on at the beginning is how this change affects the graph of the function—and, importantly, how we can consistently describe that change as it applies to any function.

So, for example, the brightest (orange-brown) curve in the image at the right represents \(\mathtt{f(x)=x^2}\), or \(\mathtt{f(x)=1x^2}\). And when we increase the A-value, from 2 to 3, the curve gets narrower. Decreasing the A-value, to \(\mathtt{\frac{1}{2}}\) for example, causes the curve to get wider. The same kind of “squishing” happens with every function type. (Check out this article by Better Explained for a really nice explanation.)

Making New Functions

We can also make higher-degree functions from lower-degree functions using dilations. The only change we make to the process of dilation shown above is that now we multiply each point of a function by a non-constant value. More specifically, we can multiply the y-coordinate of each point by its x-coordinate to get its new y-coordinate.

At the left, we transform the constant function \(\mathtt{f(x)=5}\) into a higher-order function in this way. By multiplying the y-coordinate of each original point by its x-coordinate, we change the function from \(\mathtt{f(x)=5}\) to \(\mathtt{f(x)=(x)(5)}\), or \(\mathtt{f(x)=5x}\). Another multiplication of all the points by x would get us \(\mathtt{f(x)=5x^2}\). In that case, you can see that all the points to the left of the y-axis have to reflect across the x-axis, since each y-coordinate would be a negative times a negative.

Another idea that becomes clearer when working with non-constant dilations in this way is that zeros start to make a little more sense.

Try it with other dilations (say, \(\mathtt{x \cdot (x+3)}\) or even \(\mathtt{x \cdot (x-1)^2)}\)) and pay attention to what happens to those points that wind up getting multiplied by 0.

Explicitation

research

I came across this case study recently that I managed to like a little. It focuses on an analysis of a Singapore teacher’s practice of making things explicit in his classroom. Specifically, the paper outlines three ways the teacher engages in explicitation (as the authors call it): (1) making ideas in the base materials (i.e., textbook) explicit in the lesson plan, (2) making ideas within the plan of the unit more explicit, and (3) making ideas explicit in the enactment of teaching the unit(s). These parts are shown in the diagram below, which I have redrawn, with minor modifications, from the paper.

The teacher interviewed for this case study, “Teck Kim,” taught math to Year 11 (10th grade) students in the “Normal (Academic)” track, and the work focus of the case study was on a unit the teacher called “Vectors in Two Dimensions.”

Explicit From

The first category of explicitation, Explicit From, involves using base materials such as a textbook as a starting point and adapting these materials to make more explicit what it is the teacher wants students to learn. The paper provides an illustration of some of the textbook content related to explaining column vectors, along with Kim’s adaptation. I have again redrawn below what was provided in the paper. Here I also made minor modifications to the layout of the textbook example and one small change to fix a possible translation error (or typo) in the teacher’s example. The textbook content is on the left, and the teacher’s is on the right (if it wasn’t painfully obvious).

There are many interesting things to notice about the teacher’s adaptation. Most obviously, it is much simpler than the textbook’s explanation. This is due, in part, to the adaptation’s leaving magnitude unexplained during the presentation and instead asking a leading question about it.

The textbook presented the process of calculating the magnitudes of the given vectors, leading to a ‘formula’ of \(\mathtt{\sqrt{x^2+y^2}}\) for column vector (\(\mathtt{x y}\)). In its place, Teck Kim’s notes appeared to compress all these into one question: “How would you calculate the magnitude?” On the surface, it appears that Teck Kim was less explicit than the textbook in the computational process of magnitude. But a careful examination into the pre-module interview reveals that the compression of this section into a question was deliberate . . . He meant to use the question to trigger students’ initial thoughts on the manner—which would then serve to ready their frame of mind when the teacher explains the procedure in class.

So, it is not the case that explanation has been removed—only that the teacher has moved the explication of vector magnitude into the Explicit To section of the process. We can also notice, then, in this Explicit From phase, that the teacher makes use of both dual coding and variation theory in his compression of the to-be-explained material. The text in the teacher’s work is placed directly next to the diagram as labels to describe the meaning of each component of the vector, and the vector that students are to draw varies minimally from the one demonstrated: a change in sign is the only difference, allowing students to see how negative components change the direction of a vector. All much more efficient and effective than the textbook’s try at the same material.

Explicit Within

Intriguingly, Explicit Within is harder to explain than the other two, but is closer to the work I do every day. A quote from the article nicely describes explicitation within the teacher’s own lesson plan as an “inter-unit implicit-to-explicit strategy”:

This inter-unit implicit-to-explicit strategy reveals a level of sophistication in the crafting of instructional materials that we had not previously studied. The common anecdotal portrayal of Singapore mathematics teachers’ use of materials is one of numerous similar routine exercise items for students to repetitively practise the same skill to gain fluency. In the case of Teck Kim’s notes, it was not pure repetitive practice that was in play; rather, students were given the opportunity to revisit similar tasks and representations but with added richness of perspective each time.

We saw a very small example of explicit-within above as well. The plan, following the textbook, would have delayed the introduction of negative components of vectors, but Teck Kim introduces it early, as a variational difference. The idea is not necessarily that students should know it cold from the beginning, but that it serves a useful instructional purpose even before it is consolidated.

Explicit To

Finally, there is Explicit To, which refers to the classroom implementation of explicitation, and which needs no lengthy description. I’ll leave you with a quote again from the paper.

No matter how well the instructional materials were designed, Teck Kim recognised the limitations to the extent in which the notes by itself can help make things explicit to the students. The explicitation strategy must go beyond the contents contained in the notes. In particular, he used the notes as a springboard to connect to further examples and explanations he would provide during in-class instruction. He drew students’ attention to questions spelt out in the notes, created opportunities for students to formulate initial thoughts and used these preparatory moves to link to the explicit content he subsequently covered in class.

explicitation