Study

Visual Language, a term introduced by Robert Horn, refers to "language based on tight integration of words and visual elements". The visual elements include shapes, as well as images (e.g., icons, clip art).

What does this have to do with memory? Well, partly of course, because the appropriate use of images usually makes information more memorable, but visual language has considerably more to offer than that. To appreciate what it is, Horn has examples at http://web.stanford.edu/~rhorn/

To truly appreciate these examples, you really need a full-text version of the same information, but hopefully you can imagine a prose text dense with the same information (realizing that much of the information is contained in connections and juxtapositions as well as in the emotional connotations of particular images, all of which would, in a purely prose text, require explicit words to articulate).

There are many advantages in integrating word and image, such as:

• clarifying meaning
• reinforcing meaning
• providing focus
• facilitating comparisons
• providing context

and many more ...

but I believe the great benefit of this approach is its power to SELECT and CONNECT.

Those who have read my book, The Memory Key, will be aware that I see these processes as absolutely fundamental to understanding and remembering new information. While there are many tools to help teachers and writers portray the information selected as most important (such as highlighting, and summarising), visual language stands out as offering a tool-bag of particular power. It also, of course, offers powerful tools for demonstrating connections between bits of information.

Look at Bob Horn's representation of an academic debate (can computers think http://web.stanford.edu/~rhorn/a/kmap/arg/CCT/CCTGeneralInfo.html) and you will readily see the power of visual language to organize complex information and show connections.

Visual language thus offers a powerful set of strategies for studying.

Some principles of visual language

There are six principles known as Gestalt principles, which are useful to know if you wish to draw effective visual representations:

1. People tend to group together elements that are physically close to each other
2. People tend to group together elements that are similar in some way (e.g., same color or size)
3. People tend to see elements enclosed by lines as one unit
4. People tend to see connected elements as a single unit
5. People tend to group together elements that appear to be continuations of each other
6. People tend to make figures "complete" when some elements are missing

This list is also a demonstration of the need for visual language - it's hard to describe these principles without visual examples; similarly, visual examples on their own would not be enough either. You can see examples of these principles at http://etad.usask.ca/skaalid/theory/gestalt/similar.htm and http://etad.usask.ca/skaalid/theory/gestalt/closure.htm. [ note: the examples here don't precisely match those I give, which are taken from Horn]

The way I have chosen to describe these principles points to another principle that's important for visual language: one we might call the naming principle. Isn't it easier to grasp the principles, and most particularly, remember them, if you have names for these principles? Here are the names of the 6 Gestalt principles:

1. Proximity
2. Similarity
3. Common region
4. Connectedness
5. Continuation
6. Closure

It is always worth trying to find a one or two word label for any bit of information you wish to remember. For one thing, the very act of so doing will help cement the information in your memory. And for another, the label will help you find the information again.

This article originally appeared in the January 2004 newsletter.

In the last part I talked about retrieval structures and their role in understanding what you’re reading. As promised, this month I’m going to focus on understanding scientific text in particular, and how it differs from narrative text.

First of all, a reminder about situation models. A situation, or mental, model is a retrieval structure you construct from a text, integrating the information in the text with your existing knowledge. Your understanding of a text depends on its coherence; it’s generally agreed that for a text to be coherent it must be possible for a single situation model to be constructed from it (which is not to say a text that is coherent is necessarily coherent for you —that will depend on whether or not you can construct a single mental model from it).

There are important differences in the situation models constructed for narrative and expository text. A situation model for a narrative is likely to refer to the characters in it and their emotional states, the setting, the action and sequence of events. A situation model for a scientific text, on the other hand, is likely to concentrate on the components of a system and their relationships, the events and processes that occur during the working of the system, and the uses of the system.

Moreover, scientific discourse is rooted in an understanding of cause-and-effect that differs from our everyday understanding. Our everyday understanding, which is reflected in narrative text, sees cause-and-effect in terms of goal structures. This is indeed the root of our superstitious behavior — we (not necessarily consciously) attribute purposefulness to almost everything! But this approach is something we have to learn not to apply to scientific problems (and it requires a lot of learning!).

This is worth emphasizing: science texts assume a different way of explaining events from the way we are accustomed to use — a way that must be learned.

In general, then, narrative text (and ‘ordinary’ thinking) is associated with goal structures, and scientific text with logical structures. However, it’s not quite as clear-cut a distinction as all that. While the physical sciences certainly focus on logical structure, both the biological sciences and technology often use goal structures to frame their discussions. Nevertheless, as a generalization we may say that logical thinking informs experts in these areas, while goal structures are what novices focus on.

This is consistent with another intriguing finding. In a comparison of two types of text —ones discussing human technology, and ones discussing forces of nature — it was found that technological texts were more easily processed and remembered. Indications were that different situation models were constructed — a goal-oriented representation for the technological text, and a causal chain representation for the force of nature text. The evidence also suggested that people found it much easier to make inferences (whether about agents or objects) when human agents were involved. Having objects as the grammatical subject was clearly more difficult to process.

Construction of the situation model is thus not solely determined by comprehension difficulty (which was the same for both types of text), but is also affected by genre and surface characteristics of the text.

There are several reasons why goal-oriented, human-focused discourse might be more easily processed (understood; remembered) than texts describing inanimate objects linked in a cause-effect chain, and they come down to the degree of similarity to narrative. As a rule of thumb, we may say that to the degree that scientific text resembles a story, the more easily it will be processed.

Whether that is solely a function of familiarity, or reflects something deeper, is still a matter of debate.

Inference making is crucial to comprehension and the construction of a situation, because a text never explains every single word and detail, every logical or causal connection. In the same way that narrative and expository text have different situation models, they also involve a different pattern of inference making. For example, narratives involve a lot of predictive inferences; expository texts typically involve a lot of backward inferences. The number of inferences required may also vary.

One study found that readers made nine times as many inferences in stories as they did in expository texts. This may be because there are more inferences required in narratives — narratives involve the richly complex world of human beings, as opposed to some rigidly specified aspect of it, described according to a strict protocol. But it may also reflect the fact that readers don’t make all (or indeed, anywhere near) the inferences needed in expository text. And indeed, the evidence indicates that students are poor at noticing coherence gaps (which require inferences).

In particular, readers frequently don’t notice that something they’re reading is inconsistent with something they already believe. Moreover, because of the limitations of working memory, only some of the text can be evaluated for coherence at one time (clearly, the greater the expertise in the topic, the more information that can be evaluated at one time — see the previous newsletter’s discussion of long-term working memory). Less skilled (and younger) readers in particular have trouble noticing inconsistencies within the text if they’re not very close to each other.

Let’s return for a moment to this idea of coherence gaps. Such gaps, it’s been theorized, stimulate readers to seek out the necessary connections and inferences. But clearly there’s a particular level that is effective for readers, if they often miss them. This relates to a counter-intuitive finding — that it’s not necessarily always good for the reader if the text is highly coherent. It appears that when the student has high knowledge, and when the task involves deep comprehension, then low coherence is actually better. It seems likely that knowledgeable students reading a highly coherent text will have an “illusion of competence” that keeps them from processing the text properly. This implies that there will be an optimal level of coherence gaps in a text, and this will vary depending on the skills and knowledge base of the reader.

Moreover, the comprehension strategy generally used with simple narratives focuses on referential and causal coherence, but lengthy scientific texts are likely to demand more elaborate strategies. Such strategies are often a problem for novices because they require more knowledge than can be contained in their working memory. Making notes (perhaps in the form of a concept map) while reading can help with this.

Next month I’ll continue this discussion, with more about the difficulties novices have with scientific texts and what they or their teachers can do about it, and the problems with introductory textbooks. In the meantime, the take-home message from this is:

Understanding scientific text is a skill that must be learned;

Scientific text is easier to understand the more closely it resembles narrative text, with a focus on goals and human agents;

How well the text is understood depends on the amount and extent of the coherence gaps in the text relative to the skills and domain knowledge of the reader.

Transfer refers to the ability to extend (transfer) learning from one situation to another. For example, knowing how to play the piano doesn’t (I assume) help you play the tuba, but presumably is a great help if you decide to take up the harpsichord or organ. Similarly, I’ve found my knowledge of Latin and French a great help in learning Spanish, but no help at all in learning Japanese.

Transfer, however, doesn’t have to be positive. Your existing knowledge can hinder, rather than help, new learning. In such a situation we talk about negative transfer. We’ve all experienced it. At the moment I’m experiencing it with my typing -- I've converted my standard QWERTY keyboard to a Dvorak one (you can hear about this experience in my podcast, if you're interested).

Teachers and students do generally hope that learning will transfer to new contexts. If we had to learn how to deal with every single possible situation we might come across, we’d never be able to cope with the world! So in that sense, transfer is at the heart of successful learning (and presumably the ability to transfer new learning is closely tied to that elusive concept, intelligence).

Here’s an example of transfer (or lack of it) in the classroom.

A student can be taught the formula for finding the area of a parallelogram, and will then be capable of finding the area of any parallelogram. However, if given different geometric figures, they won’t be able to apply their knowledge to calculate the area, because the formula they have memorized applies only to one specific figure — the parallelogram.

However, if the student is instead encouraged to work out how to calculate the area of a parallelogram by using the structural relationships in the parallelogram (for example, by rearranging it into a rectangle by moving one triangle from one end to the other), then they are much more likely to be able to use that experience to work out the areas of a different figure.

This example gives a clue to one important way of encouraging transfer: abstraction. If you only experience a very specific example of a problem, you are much less likely to be able to apply that learning to other problems. If, on the other hand, you are also told the abstract principles involved in the problem, you are much more likely to be able to use that learning in a variety of situations. [example taken from How People Learn]

Clearly there is a strong relationship between understanding and transfer. If you understand what you are doing, you are much more likely to be able to transfer that learning to problems and situations you haven’t encountered before — which is why transfer tests are much better tests of understanding than standard recall tests.

That is probably more obvious for knowledge such as scientific knowledge than it is for skill learning, so let me tell you about a classic study [1]. In this study, children were given practice in throwing darts at an underwater object. Some of the children were also instructed in how light is refracted in water, and how this produces misleading information regarding the location of objects under water. While all the children did equally well on the task they practiced on — throwing darts at an object 12 inches under water — the children who had been given the instruction did much better when the target was moved to a place only 4 inches under water.

Understanding is helped by contrasting cases. Which features of a concept or situation are important is often only evident when you can see different but related concepts. For example, you can’t fully understand what an artery is unless you contrast it with a vein; the concept of recognition memory is better understood if contrasted with recall memory.

Transfer is also helped if transfer implications are explicitly pointed out during learning, and if problems are presented in several contexts. One way of doing that is if you use “what-ifs” to expand your experience. That is, having solved a problem, you ask “What if I changed this part of the problem?”

All of this points to another requirement for successful transfer — time. Successful, “deep”, learning requires much more time than shallow rote learning. On the other hand, because it can apply to a much wider range of problems and situations, is much less easily forgotten, and facilitates other learning, it saves a lot of time in the long run!

In general, the weight of the research evidence suggests that college students tend to have a poor sense of how prepared they are for testing, and having been tested, they have a poor sense of how well they did! (This, of course, is even more true of younger students).

Does it matter?

Well, yes, it does. Being able to accurately estimate how well you've learnt something (monitoring) allows you to better allocate your time and energy (self-regulation). You don't want to spend more time than you need on particular topics; you also don't want to short-change topics that need more work.

We tend to be better at regulating our time and effort when the material to be learned is simple.

Obviously, also, some people are much better than others at knowing how well they know something. What distinguishes those people who have a good metacognitive sense and those who don't?

Well, partly, it's about the strategies used in learning. Taking notes, for example, tends to make you more aware of what you know and what you don't know.But not only note-taking; any strategy that causes you to process the material more thoroughly should have this result.

Studies have found that your monitoring accuracy can be improved:

• when you monitor your learning after a short delay, rather than immediately after studying the material [1]
• when items are actively generated and not simply passively read [2]
• by having practice tests of the material [3]
• by summarizing the material [4]
• by generating keywords -- but only if, again, you delay a little while before generating them [5]

In general, it seems that students tend to be better at predicting their ability to recall information than their understanding -- as evidenced by their ability to apply the information and make inferences about it. It is of course easier to test your memory than your understanding, and it may well be that students tend not to clearly distinguish between these two aspects of learning. However, certain strategies, such as taking notes (although it depends on the nature of the notes!), do lend themselves to helping develop understanding more than memory.

One final thing is worth noting. It's not only about strategies. Monitoring accuracy is also affected by learner attributes — which doesn't mean you can excuse yourself on the grounds you're "not smart enough"! Studies have found that IQ rarely is a significant factor once background knowledge and other factors (such as socioeconomic status) are accounted for [6]. What looks like being of importance is the student's chronic dispositional status toward learning -- that is, their general attitude to it. For example, those who believe intelligence is malleable and can be increased are more likely to work on increasing their skills, compared to those who believe intelligence is fixed, who tend to focus more on demonstrating good performance, often by choosing only those sort of tasks at which they can do well [7].

Context is absolutely critical to successful communication. Think of the common experience of being a stranger at a family gathering or a meeting of close friends. Even familiar words and phrases may take on a different or additional meaning, among people who have a shared history. Many jokes and comments will be completely unintelligible, though you all speak the same language.

American anthropologist Edward Hall makes a useful distinction between ‘High context’ and ‘Low context’ communications. Your family gathering would be an example of a high context situation. In this setting, much of the meaning is carried in the speakers, their relationships, their knowledge of each other. In a low context situation, on the other hand, most of the meaning is carried in the actual words.

Part of the problem with email, as we all recognize, is that the context is so lacking, and the burden lies so heavily on the words themselves.

The importance of context for comprehension has, of course, profound implications for learning and memory.

I was reminded of this just the other day. I’m a fan of a TV program called NCIS. I only discovered it, however, at the beginning of the third season. After I’d watched it for some weeks, I purchased the DVDs of the earlier seasons. Most recently, I bought the DVD of season 3, which I had, of course, seen on TV. Watching the first episode of that season, which was the first episode of NCIS I ever saw, I was surprised to hear a line which I had no memory of, that was freighted with significance and led me to a much deeper understanding of the relationship between two of the characters — but which had meant absolutely nothing to me when I originally saw it, ignorant as I was of any of the characters and the back story.

The revelation meant nothing to me as a novice to the program, and so I didn’t remember it, but it meant everything to me as (dare I say it?) an expert.

Context is such a slippery word; so hard to define and pin down. But I think it’s fair to say that the difference between the novice and the expert rests on this concept. When an expert is confronted with a piece of information from her area of expertise, she knows what it means and where it belongs — even if the information is new to her. Because of this, she can acquire new information much more easily than a novice. But this advantage applies only in the expert’s area of expertise.

To take another example from the frivolous world of popular culture, a British study of fans of the long-running radio soap opera The Archers were given one of two imaginary scripts to read. One story was representative of the normal events in The Archers (a visit to a livestock market); the other was atypical (a visit to a boat show). These experts were able to remember many more details of the typical, market story than a group of subjects who knew little about the soap opera, but were no better at remembering details for the atypical story. Most importantly, this occurred even though the two stories shared many parallel features and most of the questions (and answers) used to assess their memory were the same. This indicates the specificity of expert knowledge.

Part of the advantage experts have is thought to rest on the ‘conditionalization’ of knowledge. That is, experts’ knowledge includes a specification of the contexts in which it is relevant.

It is surprising to many, this idea that it is not necessarily a lack of knowledge that is the problem — that people often have relevant knowledge and don’t apply it. In reading, for example, readers often don’t make inferences that they are perfectly capable of making, on the knowledge they have, unless the inferences are absolutely demanded to make sense of the text.

Another example comes from the making of analogies. I discuss this in my workbook on taking notes. Here’s a brief extract:

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Rutherford’s comparison of the atom to the solar system gave us a means to understand the atom. The story goes that Newton ‘discovered’ gravity when an apple fell on his head — because of the comparison he made, realizing that the motion of an apple falling from a tree was in some sense like the motion of the planets. These are comparisons called analogies, and analogy has been shown to be a powerful tool for learning.

But the problem with analogies is that we have trouble coming up with them.

Generally, when we make analogies, we use an example we know well to help us understand something we don’t understand very well. This means that we need to retrieve from memory an appropriate example. But this is clearly a difficult task; people frequently fail to make appropriate connections — even, surprisingly, when an appropriate connection has recently come their way. In a study where people were given a problem to solve after reading a story in which an analogous problem was solved, 80% didn’t think of using the story to solve the problem until the analogy was pointed out to them.

It’s thought that retrieving an appropriate analogy is so difficult because of the way we file information in memory. Certainly similarity is an important attribute in our filed memories, but it’s not the same sort of similarity that governs analogies. The similarity that helps us retrieve memories is a surface similarity — a similarity of features and context. But analogies run on a deeper similarity — a similarity of structure, of relations between objects. This will only be encoded if you have multiple examples (at least more than one) and make an explicit effort to note such relations.

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The conditionalization of knowledge is of course related to the problem of transfer. Transfer refers to the ability to extend (transfer) learning from one situation to another (read more about it here) . Transfer is frequently used as a measure of successful learning. It’s all very well to know that 399-(399*0.1) = 359.1, but how far can you be said to understand it — how much use is it — if you can’t work out how much a $3.99 item will cost you if you have a 10% discount? (In fact, the asymmetry generally works the other way: many people are skilled at working out such purchase calculations, but fall apart when the problem is transferred to a purely numerical problem).

Transfer is affected by the context in which the information was originally acquired — obviously transfer is particularly problematic if you learn the material in a single context — and this is partly where the experts achieve their conditionalization: because, spending so much time with their subject they are more likely to come across the same information in a variety of contexts. But the more important source is probably the level of abstraction at which experts can operate (see my article on transfer for examples of how transfer is facilitated if the information is framed at a higher level of abstraction).

In those with existing expertise, an abstract framework is already in place. When an expert is confronted by new information, they automatically try and fit it into their existing framework. Whether it is consistent or inconsistent with what is already known doesn’t really matter — either way it will be more memorable than information that makes no deep or important connections to familiar material.

Let’s return to this idea of high and low context. Hall was talking about communications, in the context of different cultures (interestingly, he found cultures varied in the degree to which they were context-bound), but the basic concept is a useful one in other contexts. It is helpful to consider, when approaching a topic, either as student or teacher, the degree to which understanding requires implicit knowledge. A high context topic might be thought of as one that assumes a lot of prior knowledge, that assumes a knowledge of deeper structure, that is difficult to explain in words alone. A low context topic might be thought of as one that can be clearly and simply expressed, that can largely stand alone. Learning the basics of a language — how to conjugate a verb; some simple words and phrases — might be thought of as a low context topic, although clearly mastery of a language requires the complex and diverse building up of experiences that signifies a high context topic (and also clearly, some languages will be more ‘high context’ than others).

There is nothing particularly profound about this distinction, but an awareness of the ‘contextual degree’ of a topic or situation, is helpful for students, teachers, and anyone involved in trying to communicate with another human being (or indeed, computer!). It’s also helpful to be aware that high context situations require much more expertise than low context ones.

This article first appeared as "Context, communication & learning" in the Memory Key Newsletter for April 2007

Consider our facts about blood:

• arteries are thick and elastic and carry blood that is rich in oxygen from the heart.
• veins are thinner, less elastic, and carry blood rich in carbon dioxide back to the heart.

We could, as is often advised, simply turn these into why questions. And we can answer these on the basis of the connections we’ve already made:

Why are arteries elastic?

Because they need to accommodate changes in pressure

Why are arteries thick?

Because they need to accommodate high pressure

Why do arteries carry blood away from the heart?

Because blood coming from the heart comes out at high pressure and in spurts of variable pressure

Why do arteries carry blood that is rich in oxygen?

Because the blood coming from the heart is rich in oxygen

Why are veins less elastic?

Because the blood flows continuously and evenly

Why are veins less thick?

Because the blood flows at a lower pressure

Why do veins carry blood to the heart?

Because blood going to the heart flows continuously and evenly

Why do veins carry blood that is rich in CO2?

Because the blood going to the heart is rich in CO2

What’s missing? Connections between these facts. The facts have become more meaningful, but to be really understood you need to make the connections between the facts explicit.

Look again at our original questions. See how they relate the facts to each other? They don’t ask: why are arteries elastic? They ask: Why do arteries need to be more elastic than veins? They don’t ask: why do arteries carry blood that is rich in oxygen? They ask: why do vessels carrying blood from the heart need to be rich in oxygen?

By answering these questions, we have built up an understanding of the facts that ties them together in a multi-connected cluster:

For simplicity, I’ve just focused on the arteries. See how the four facts about arteries are connected together. Meaningfully connected. In a perfect world we’d be able to close the circle with a direct connection between the facts “Arteries carry blood rich in oxygen” and “Arteries are thick”, but as far as I know, the only connection between them is indirect, through the fact that “Arteries carry blood from the heart”.

So … the world isn’t perfect, and information doesn’t come in neatly wrapped bundles where every fact connects directly to every other fact. But the more connections you can make between related facts — the stronger a cluster you can make — the more deeply you will understand the information, and the more accessible it will be. That is, you will remember it more easily and for longer.

If it’s well enough connected

If it’s connected to strong anchor points

You will simply 'know' it.

You’re never going to forget that you breathe in oxygen and that your heart pumps out blood. These are strong anchor points. If the facts about arteries are strongly connected to these anchor points, you will never forget them either.

When the subject is new to you

When you don’t have enough prior knowledge about a subject to ask effective questions, you are better off forming connections using mnemonics — either through verbal elaboration, as in our sentence about “Art (ery) being thick around the middle so he wore trousers with an elastic waistband” or by creating interactive images.

However, mnemonics such as these — while perfectly effective — are only good for rote learning. Sometimes that’s all you want, of course. But if you’re going to be learning more information that relates to these facts, then you’re making a rod for your own back.

When you learn something by rote, it never gets easier. When you learn by building connections, every new fact is acquired more easily. And it’s progressive. An expert on a subject can hear a new fact in her area of expertise, and it’s there. Remembered. Without effort. Because she’s an expert. And what makes her an expert? Simply the fact that she’s built up a network of information that is so tightly connected, and that has so many strong anchor points, that the information is always retrievable.

Here are some notes on the water cycle:

In this example, the notes are neat and tidy, with headings and indentations showing a degree of organization. Terms are defined. The notes appear to encapsulate the main ideas. A few abbreviations are used. So far so good — these are all widely cited recommendations for effective note-taking.

Here's a different approach.

(If you click on the links at the bottom, you'll be able to see better images.)

This one’s a picture. What is called in the trade a multimedia summary: a concise summary combining words and pictures. This has an advantage over the first example in that we can actually see the cycle, we can see the connection between the elements of the water cycle.

In the first example (a topical summary), we had the main points, but it didn’t go beyond the information presented in the text. Similarly, the above example (a multimedia summary), shows more connection but less detail, but also doesn’t go beyond the points given.

Now look at this one

There’s no more detail in this one, but it not only connects the ideas, it has taken the information another step. To the principle beneath the connection. To a higher level of abstraction.

You may think of summarizing strategies in terms of a matrix weighing amount of detail against degree of abstraction:

The best type of summary is one that combines a high degree of abstraction with a high amount of detail. Our third water cycle example has a high level of abstraction but little detail — rather vacuous.

This one has the details. It also has a mnemonic, to help prompt my memory for the elements of the cycle and remember their order. This information could equally well have been presented in a linear format.

Together, these two examples combine detail and abstraction to form an effective summary.

Elaborative interrogation is a strategy to help you remember meaningful information. The idea behind the strategy is that relevant prior knowledge is not always readily activated when you are trying to learn new information, and sometimes help is needed to make the right connections. The strategy requires you to go beyond the information given to you and to construct reasons for the relationships between bits of information.

Because elaborative techniques help your understanding by relating new information to codes already stored and familiar to you, elaborative interrogation is a strategy best suited to a situation where the information you wish to learn relates to a rich network of information in your database₁.

An example:

Some facts:

arteries are thick and elastic and carry blood that is rich in oxygen from the heart.

veins are thinner, less elastic, and carry blood rich in carbon dioxide back to the heart.

Now if you know nothing else about veins and arteries and the circulation of blood, this is a set of facts with little meaning. You can learn this information

• by rote (through simple repetition), or
• by using a mnemonic aid (for example, you could make up a sentence such as “Art (ery) was thick around the middle so he wore trousers with an elastic waistband”), or
• by understanding the connections between the facts.

Guess which way will help you remember the information much better for longer?

Let's ask some why questions.

Why do arteries need to be more elastic than veins?

Why do arteries need to be thicker than veins?

Why do arteries carry blood away from the heart?

Why do arteries carry the blood that is rich in oxygen?

These four questions lead directly from the facts as they are given. But we can also reinterpret these questions in a way that integrates the facts at a deeper level.

When we ask: Why do arteries carry blood away from the heart? it may be that the right question really is: Why do the vessels carrying blood from the heart need to be thicker and more elastic?

When we ask: Why do arteries carry the blood that’s rich in oxygen, it may be that the right question actually is: Why do the vessels carrying oxygen-rich blood need to be thicker and more elastic?

Or it may be that the right question is: Why do the vessels carrying blood from the heart need to be rich in oxygen? That question takes us another step: Why should blood be rich in oxygen? Why is blood sometimes rich in oxygen and sometimes rich in carbon dioxide?

Why do arteries carry the blood that is rich in oxygen?

? = Why do the vessels carrying blood rich in oxygen need to be thicker and more elastic?

? or = Why do the vessels carrying blood from the heart need to be rich in oxygen?

Why should blood be rich in oxygen?

Why is blood sometimes rich in oxygen and sometimes rich in carbon dioxide?

Why are arteries thicker and more elastic than veins?

(You can see better images if you click the links at the bottom.)

Arteries are thick and elastic because they carry blood from the heart, which pumps blood out in spurts.

Veins are thin and less elastic because they carry blood to the heart in an even flow.

So all these facts — arteries are thick and elastic and carry blood from the heart; veins are thinner, less elastic, and carry blood back to the heart — are connected. So far so good.

But we still have a couple of loose facts. What has any of this got to do with the fact that arteries carry blood rich in oxygen and veins carry blood rich in carbon dioxide?

So the fact that arteries carry oxygen-rich blood is connected to the fact that arteries carry blood from the heart; and veins carry carbon dioxide-rich blood because they carry blood to the heart — and all of these are connected to the well-known fact that we breathe in oxygen and breathe out carbon dioxide. Just as our earlier cluster of facts — that arteries are thick and elastic and carry blood from the heart — was connected to the well-known fact that the heart is a pump.

Remember:
Facts that you already know very well and have no trouble remembering act as anchor points. The more anchor points you can connect to, the more meaningful the new information becomes, and the more easily you will remember it.

Graphic summaries are summaries that reorganize the text. Two examples of graphic summaries are outlines and graphic organizers.

In an outline, topics are listed with their subtopics in a linear format, like this:

Graphic organizers show the same sort of information, but in a more visual format, like this:

This is a tree diagram. Although graphic organizers can come in many forms, most commonly they are either tree diagrams or matrices. Here is a matrix of the same information:

Basically, graphic organizers are visual outlines showing relationships. Both outlines and graphic organizers are useful strategies for hierarchical information. However, while an outline does pick out the most important information and does show hierarchical relations (and, as you may have noticed, can include more detail more easily), it is not as effective in showing the relationships between concepts.

Compare the examples. In the outline, the clusters within a topic are clear, but the relations between topics — between the clusters — are not. The graphic organizer, on the other hand, allows connections between clusters to be more readily seen. Notice how much easier it is to grasp the similarities and differences between the different branches of the U.S. Government when looking at the tree diagram or the matrix, compared to looking at the outline.

In general, graphic organizers are more effective than outlines — but not invariably. In a study involving text summaries, graphic organizers were superior only when the students had enough time to study them properly — but where the students did have enough time, those who had studied the graphic organizer tested just as well after two days as they had when tested immediately, while those who had studied the outline performed more poorly (and those who had only read the text were worst of all). In other words, graphic organizers are much better for long-term recall (which is, after all, what you usually want!). This appears even more true when the text is longer.

But graphic organizers can be less effective than outlines, and this may be because graphic organizers can make it too easy to see the relations, and the reader doesn’t need to work as hard to understand the material, with the consequence that the material isn’t processed to the extent that it needs to be for lasting memory. This doesn’t apply, of course, if you’re constructing the graphic organizer yourself.

Graphic organizers have an advantage over outlines in terms of cognitive load. Working memory is thought to have two sub-systems — one that is essentially visual, and one essentially auditory. When we read text, notwithstanding we are receiving the information through our visual sense, we tend to encode it through the auditory working memory (words are fundamentally sound-based). There is evidence that graphic organizers use visual working memory more than auditory, while outlines use auditory more than visual. The advantage of a graphic organizer, therefore, may lie partly in its reduction of cognitive load — that is, by spreading the load on working memory between both systems.

Additionally, of course, the use of visual information in addition to verbal information creates more retrieval paths, increasing the chances of finding the information again.

All of this means that if outlines or graphic organizers are provided for you, even if the same information is also provided in the text, it’s worth spending time studying the outline/graphic. If an outline is provided, consider re-drawing the information as a graphic organizer.

As far as producing these yourself is concerned, outlines are easier to produce than graphic organizers, which is why they are much more popular. Although outlines are in general less effective than graphic organizers, both are generally more effective than conventional notes.

In two studies comparing note-taking formats in a ecture, both outlines and matrix notes were usually more detailed, better organized, and contained more ideas. Matrix notes were also slightly more coherent. But of course, the material was compatible with a matrix format, which is not always the case.

Although a graphic organizer is more effective, an outline is certainly sufficient in the right circumstances. Because it is easier to construct than a graphic organizer, if the material can be adequately described in an outline, you should use it. This will depend partly on the material itself, and partly on your goal. If you’re simply aiming to learn the “facts” (i.e., you’re not trying to develop your understanding), then research indicates an outline will be just as productive as a graphic organizer. If the text is short (1000 words or less), an outline is probably better. But with longer and more complex material, it would seem that graphic organizers are worth the trouble. In such cases, research also suggests that several graphic organizers are most effective — a warning that we shouldn’t try to cram too much information into a graphic organizer.

Remember, too, that graphic organizers, like outlines, are not designed to provide full notes — so you shouldn’t be trying to include everything. It’s all about selecting what’s important.

This article is taken from my book Effective note-taking

The use of worked examples

We're all familiar, I'm sure, with the use of worked-out examples in mathematics teaching. Worked-out examples are often used to demonstrate problem-solving processes. They generally specify the steps needed to solve a problem in some detail. After working through such examples, students are usually given the same kind of problems to work through on their own. The strategy is generally helpful in teaching students to solve problems that are the same as the examples.

Worked-out examples are also used in small-group settings, either by working on the example together, or by studying the example individually and then getting together to enable those who understood to explain to those who didn't. Explaining something to another person is well-established as an effective method of improving understanding (for the person doing the explaining -- and presumably the person receiving the explanation gets something out of it also!).

Metacognitive differences between high and low achievers

An interesting study comparing the behavior of high and low achieving students who studied worked-out examples cooperatively found important differences.

High achievers:

• explained things to themselves as they worked through the examples
• tried to construct relationships between the new process and what they already knew
• tended to infer additional information that wasn't directly given

Low achievers on the other hand:

• followed the examples step-by-step without relating it to anything they already knew
• didn't try to construct any broader understanding of the procedure that would enable them to generalize it to new situations

Other studies have since demonstrated that students taught to ask questions that focus on relating new learning to old show greater understanding than students taught to ask different questions, and both do better than students who ask no questions at all.

Learning to ask the right questions

An instructional method for teaching mathematics that involves training students to ask metacognitive questions has been found to produce significant improvement in students' learning. The method is called IMPROVE -- an acronym for the teaching steps involved:

• Introduce new concepts
• Metacognitive questioning
• Practise
• Review
• Obtain mastery on lower and higher cognitive processes
• Verify
• Enrich

There are four kinds of metacognitive questions the students are taught to ask:

1. Comprehension questions (e.g., What is this problem all about?)
2. Connection questions (e.g., How is this problem different from/ similar to problems that have already been solved?)
3. Strategy questions (e.g., What strategies are appropriate for solving this problem and why?)
4. Reflection questions (e.g., does this make sense? why am I stuck?)

A study that compared the effects of using worked-out examples or metacognitive questioning (both in a cooperative setting) found that students given metacognitive training performed significantly better than those who experienced worked-out examples (the participants were 8th grade Israeli students). Lower achievers benefited more from the metacognitive training (not surprising, because presumably the high achievers already used this strategy in the context of the worked-out examples).