Asking better questions

Questions — especially why questions — help us make connections to existing anchor points — facts we know well. But some questions are better than others.

To decide whether a question is effective, ask:

  • does it make the information more meaningful?
  • does it make the information more comprehensible?
  • does it increase the number of meaningful connections?

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:

pictorial representation of this information

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.

Asking questions is one of the best ways of making connections,


Bad questions can be worse than no questions at all.

Rote questions that direct your attention to unimportant details are better not asked.

Effective questions prepare you to pay attention to the important details in the text.

The best questions not only direct your attention appropriately, but also require you to integrate the details in the text. Ask yourself:

  • Is this helping me to select the important information?
  • Is it helping me make connections?

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.

Why questions, like any questions, are only effective to the extent that they direct attention to appropriate information.

Research confirms that it is better to search for consistent relations than inconsistent ones. In many cases your background knowledge may include information that is consistent with the new information, and information that is inconsistent.

By asking “Why is this true?” you focus on the consistent information.



  • Woloshyn, V.E., Willoughby, T., Wood, E., & Pressley, M. 1990. Elaborative interrogation facilitates adult learning of factual paragraphs. Journal of Educational Psychology, 82, 513-524.
  • Pressley, M. & El-Dinary, P.B. 1992. Memory strategy instruction that promotes good information processing. In D. Herrmann, H. Weingartner, A. Searleman & C. McEvoy (eds.) Memory Improvement: Implications for Memory Theory. New York: Springer-Verlag.

tags strategies: 

tags study: 

About expert knowledge

Principles of expert knowledge

  • Principle 1: Experts are sensitive to patterns of meaningful information
  • Principle 2: Expert knowledge is highly organized in deeply integrated schemas.
  • Principle 3: Expert knowledge is readily accessible when needed because it contains information about when it will be useful.

Do experts simply know "more" than others, or is there something qualitatively different about an expert's knowledge compared to the knowledge of a non-expert?

While most of us are not aiming for an expert's knowledge in many of the subjects we study or learn about, it is worthwhile considering the ways in which expert knowledge is different, because it shows us how to learn, and teach, more effectively.

Experts are sensitive to patterns of meaningful information

A basic principle of perception is that it depends on the observer. What is green to you may be teal to me; a floppy disk to me may be a curious square of hard plastic to you. The observer always sees the world through her own existing knowledge.

An essential part of the difference between an expert and a novice can be seen in terms of this principle. A configuration of chess pieces on a board, seen briefly, will be bewildering and hard to remember for someone with no knowledge of chess, and even for someone with some experience of the game. But to a chess master, the configuration will be easily grasped, and easily remembered.

When chess pieces are placed randomly on a board, the chess master is no better than the novice at remembering briefly seen configurations. This is because the configuration is not meaningful. After tens of thousands of hours of playing chess, of studying the games of other masters, of memorizing patterns of moves, the master has hundreds of stored patterns in his memory. When he sees a configuration of pieces, he breaks it into meaningful elements that are related by an underlying strategy. Thus, while the novice would have to try and remember every single piece and its absolute or relative position on the board, the master only has to remember a few “chunks”.

The master can do this because he has a highly organized structure of knowledge relating to this domain. (It’s worth noting that expertise is highly specific to a domain of knowledge; a chess master will be no better than anyone at remembering, say, a shopping list.)

Expert knowledge is highly organized in deeply integrated schemas.

This sensitivity is thought to grow out of the deep conceptual schemas that experts develop in their area of expertise.

A schema is an organized body of knowledge that enables the user to understand a situation of set of facts. Schema theories include the idea of “scripts”, which help us deal with events. Thus, we are supposed to have a “restaurant script”, which we have developed from our various experiences with restaurants, and which tells us what to expect in a restaurant. Such a script would include the various activities that typically take place in a restaurant (being seated; ordering; eating; paying the bill, etc), and the various people we are likely to interact with (e.g., waiter/waitress; cashier).

Similarly, when we read or hear stories (and many aspects of our conversations with each other may be understood in terms of narratives, not simply those we read in books), we are assisted in our interpretation by “story schemas” or “story grammars”.

A number of studies have shown that memory is better for stories than other types of text; that we are inclined to remember events that didn’t happen if their happening is part of our mental script; that we find it hard to remember stories that we don’t understand, because they don’t fit into our scripts.

Schemas provide a basis for:

  • Assimilating information
  • Making inferences
  • Deciding which elements to attend to
  • Help search in an orderly sequence
  • Summarizing
  • Helping you to reconstruct a memory in which many details have been lost

(following Anderson 1984)

A schema then is a body of knowledge that provides a framework for understanding, for encoding new knowledge, for retrieving information. By having this framework, the expert can quickly understand and acquire new knowledge in her area of expertise, and can quickly find the relevant bits of knowledge when called on.

How is an expert schema different from a beginner’s one?

Building schemas is something we do naturally. How is an expert schema different from a beginner’s one?

An expert’s schema is based on deep principles; a beginner tends to organize her growing information around surface principles.

For example, in physics, when solving a problem, an expert usually looks first for the principle or law that is applicable to the problem (e.g., the first law of thermodynamics), then works out how one could apply this law to the problem. An experienced novice, on the other hand, tends to search for appropriate equations, then works out how to manipulate these equations (1). Similarly, when asked to sort problems according to the approach that could be used to solve them, experts group the problems in terms of the principles that can be used, while the novices sort them according to surface characteristics (such as “problems that contain inclined planes”) (2).

The different structure of expert knowledge is also revealed through the pattern of search times. Novices retrieve information at a rate that suggests a sequential search of information, as if they are methodically going down a list. Expert knowledge appears to be organized in a more conceptual manner, with information categorized in different chunks (mini-networks) which are organized around a central “deep” idea, and which have many connections to other chunks in the larger network.

These mini-networks, and the rich interconnections between them, help the expert look in the right place. One of the characteristics that differentiates experts from novices is the speed and ease with which experts retrieve the particular knowledge that is relevant to the problem in hand. Experts’ knowledge is said to be “conditionalized”, that is, knowledge about something includes knowledge as to the contexts in which that knowledge will be useful.

Expert knowledge contains information about when it will be useful.

Conditionalized knowledge is contrasted with “inert” knowledge. This concept is best illustrated by an example.

Gick and Holyoake (1980) presented college students with the following passage, which they were instructed to memorize:

After students had demonstrated their recall of this passage, they were asked to solve the following problem:

Although the students had recently memorized the military example, only 20% of them saw its relevance to the medical problem and successfully applied its lesson. Most of the students were unable to solve the problem until given the explicit hint that the passage they had learned contained information they could use. For them, the knowledge they had acquired was inert. However, when the analogy was pointed out to them, 90% of them were able to apply the principle successfully.

Much of the information “learned” in school is inert. A compelling demonstration of this comes from studies conducted by Perfetto, Bransford and Franks (1983), in which college students were given a number of “insight” problems, such as:

Some students were given clues to help them solve these problems:

These clues were given before the students were shown the problems. Some of the students given clues were also explicitly advised that the clues would help them solve the problems. They performed very well. Other students however, were not prompted to use the clues they had been given, and they performed as poorly as those students who weren’t given clues.

The poor performance of those students who were given clues but not prompted to use them surprised the authors of the study, because the clues were so obviously relevant to the problems, but it provides a compelling demonstration of inert knowledge.

The ability of students to apply relevant knowledge in new contexts tends to be grossly over-estimated by instructors. Most assume that it will happen “naturally”, but what this research tells us is that the conditionalization of knowledge is something that happens quite a long way down the track, and if students are to be able to use the information they have learned, they need help in understanding where, when and how to use new knowledge.

Differences between experts and novices:

  • experts have more categories
  • experts have richer categories
  • experts’ categories are based on deeper principles
  • novices’ categories emphasize surface similarities3



  • Anderson, R.C. 1984. Role of reader's schema in comprehension, learning and memory. In R. Anderson, J. Osborn, & R. Tierney (eds), Learning to read in American schools: Basal readers and content texts. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Bransford, J.D., Brown, A.L. & Cocking, R.R. (eds.) 1999. How people learn: Brain, Mind, Experience, and School. Washington, DC: National Academy Press.
  • Bransford, J.D., Stein, B.S., Shelton, T.S., & Owings, R.A. 1981. Cognition and adaptation: The importance of learning to learn. In J. Harvey (ed.), Cognition, social behavior and the environment. Hillsdale, NJ: Erlbaum.
  • Bransford, J.D., Stein, B.S., Vye, N.J., Franks, J.J., Auble, P.M., Mezynski, K.J. & Perfetto, G.A. 1982. Differences in approaches to learning: an overview. Journal of Experimental Psychology: General, 111, 390-398.
  • Gick, M.L. & Holyoake, K.J. 1980. Analogical problem solving. Cognitive Psychology, 12, 306-355.
  • Perfetto, G.A., Bransford, J.D. & Franks, J.J. 1983. Constraints on access in a problem solving context. Memory & Cognition, 11, 24-31.

1. Chi, MTH, Feltovich, PJ, & Glaser, R. 1981. Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.

Larkin, JH, 1981. Enriching formal knowledge: A model for learning to solve problems in physics. In JR Anderson (ed), Cognitive skills and their acquisition. Hillsdale, NJ: Erlbaum.

1983. The role of problem representation in physics. In D. Gentner & A.L. Stevens (eds), Mental models. Hillsdale, NJ: Erlbaum.

2. Chi et al 1981

3. Taken from The Memory Key.

tags study: 


Subscribe to comprehension