Study skills

Strategies specifically for formal study

Concept maps

Broadly speaking, a concept map is a graphic display that attempts to show how concepts are connected to each other. A concept map is a diagram in which labeled nodes represent concepts, and lines connecting them show the relationships between concepts.

There is one type of concept map you’re probably all aware of — mind maps. Mind maps are a specialized form of concept map popularized very successfully by Tony Buzan.

A mind map has four essential characteristics:

  • the subject is crystallized in a central image
  • main themes radiate from it as branches
  • the branches comprise a key image or key word
  • the branches form a connected nodal structure

The essential difference between a mind map and the more general concept map is that in a mind map the main themes are connected only to this single central image — not to each other. In a concept map, there are no restrictions on the links between concepts.

Also, the connections between concepts in a concept map are labeled — they have meaning; they’re a particular kind of connection. In a mind map, connections are simply links; they could mean anything.

Mind maps are also supposed to be very pictorial. In Buzan’s own words:

“The full power of the Mind Map is realised by having a central image instead of a central word, and by using images wherever appropriate rather than words.”

Concepts in a concept map, on the other hand, can be (and usually are) entirely verbal. But the degree to which you use words or pictures is entirely up to the user.

In fact, this insistence on images is one of the things I don’t like about mind maps (I hasten to add that there are many things I do like about mind maps). While images are certainly powerful memory aids, they are not for everyone, nor for all circumstances.

Mind maps and concept maps are really aimed at different purposes, and perhaps, different personalities.

The chief usefulness of mind mapping, I believe, is when you’re still trying to come to grips with an idea. Mindmapping is good for brainstorming, for outlining a problem or topic, for helping you sort out the main ideas.

Concept maps, on the other hand, are particularly useful further down the track, when you’re ready to work out the details, to help you work out or demonstrate all the multitudinous ways in which different concepts (and a “concept” can be anything) are connected.

Concept maps are more formal than mind maps, and are better suited to situations where the concept is to be shared with others. Mind maps are considerably more personal, and are often not readily understood by others.

Both mind maps and concept maps are good at clarifying your thoughts, but because of the greater formality of the concept map — the need to be more precise in your connections — concept maps are better at showing you exactly what you don’t understand properly.

Which is why concept maps take a while to get right!

This is a very important point that I should emphasize — hardly anyone ever gets their map (mind or concept) right the first time. In fact, if you did, you probably didn’t need to construct it! It’s the redesigning that is important.

But concept maps can come in different flavors — from the more formal, to a visual display which simply use the basic idea of nodes and links. You can see a whole bunch of proper concept maps, constructed using cmap, at http://cmex.ihmc.us/cmex/table.html . And if you’re interested in becoming a cmapper yourself, check out http://cmap.ihmc.us/ .

And here’s a couple more links to help you learn more about concept maps:
http://www.fed.cuhk.edu.hk/~johnson/misconceptions/concept_map/concept_maps.html
http://cmc.ihmc.us/CMC2004Programa.html (this one has a number of conference papers available in pdf format).

I talk more about concept mapping in my podcast. Don’t forget, if you don’t want to listen to it, you can just read the transcript.

This article first appeared in the Memory Key Newsletter for October 2006

Visual language

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, first look at some examples: Horn has examples at http://www.stanford.edu/~rhorn/ and another brilliant example is available from an information designer I deeply admire - Richard Saul Wurman - at http://www.understandingusa.com/

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 (realising 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://www.macrovu.com/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://www.usask.ca/education/coursework/skaalid/theory/gestalt/similar.htmand http://www.usask.ca/education/coursework/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.

References: 

Horn, Robert E. 1998. Visual Language. Bainbridge Island, Washington: MacroVU, Inc.

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
References: 
  • 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.

Context & the conditionalization of knowledge

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:

------------------------------------------

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.

----------------------------------------------

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

References: 

Reeve, D.K. & Aggleton, J.P. 1998. On the specificity of expert knowledge about a soap opera: an everyday story of farming folk. Applied Cognitive Psychology, 12 (1), 35-42.

Novices' problems with scientific text

This is the last part in my series on understanding scientific text. In this part, as promised, I am going to talk about the difficulties novices have with scientific texts; what they or their teachers can do about it; and the problems with introductory textbooks.

The big problem for novices is of course that their lack of knowledge doesn’t allow them to make the inferences they need to repair the coherence gaps typically found in such texts. This obviously makes it difficult to construct an adequate situation model. Remember, too, that to achieve integration of two bits of information, you need to have both bits active in working memory at the same time. This, clearly, is more difficult for those for whom all the information is unfamiliar (remember what I said about long-term working memory last month).

But it’s not only a matter a matter of having knowledge of the topic itself. A good reader can compensate for their lack of relevant topic knowledge using their knowledge about the structure of the text genre. For this, the reader needs not only to have knowledge of the various kinds of expository structures, but also of the cues in the text that indicate what type of structure it is. (see my article on Reading scientific text for more on this).

One of the most effective ways of bringing different bits of information together is through the asking of appropriate questions. Searching a text in order to answer questions, for example, is an effective means of improving learning. Answering questions is also an effective means of improving comprehension monitoring (remember that one of the big problems with reading scientific texts is that students tend to be poor at judging how well they have understood what was said).

One of the reasons why children typically have pronounced deficits in their comprehension monitoring skills when dealing with expository texts, is that they have little awareness that expository texts require different explanations than narrative texts. However, these are trainable skills. One study, for example, found that children aged 10-12 could be successfully taught to use “memory questions” and “thinking questions” while studying expository texts.

Moreover, the 1994 study found that when the students were trained to ask questions intended to access prior knowledge/experience and promote connections between the lesson and that knowledge, as well as questions designed to promote connections among the ideas in the lesson, their learning and understanding was better than if they were trained only in questions aimed at promoting connections between the lesson ideas only (or if they weren’t trained in asking questions at all!). In other words, making explicit connections to existing knowledge is really important! You shouldn’t just be content to consider a topic in isolation; it needs to be fitted into your existing framework.

College students, too, demonstrate limited comprehension monitoring, with little of their self-questioning going deeply into the material. So it may be helpful to note Baker’s 7 comprehension aspects that require monitoring:

  1. Your understanding of the individual words
  2. Your understanding of the syntax of groups of words
  3. External consistency — how well the information in the text agrees with the knowledge you already have
  4. Internal consistency — how well the information in the text agrees with the other information in the text
  5. Propositional cohesiveness — making the connections between adjacent propositions
  6. Structural cohesiveness —integrating all the propositions pertaining to the main theme
  7. Information completeness — how clear and complete the information in the text is

Think of this as a checklist, for analyzing your (or your students’) understanding of the text.

But questions are not always the answer. The problem for undergraduates is that although introductory texts are presumably designed for novices, the students often have to deal not only with unfamiliar content, but also an approach that is unfamiliar. Such a situation may not be the best context for effective familiar strategies such as self-explanation.

It may be that self-explanation is best for texts that in the middle-range for the reader — neither having too little relevant knowledge, or too much.

Introductory texts also are likely to provide only partial explanations of concepts, a problem made worse by the fact that the novice student is unlikely to realize the extent of the incompleteness. Introductory texts also suffer from diffuse goals, an uneasy mix of establishing a basic grounding for more advanced study, and providing the material necessary to pass immediate exams.

A study of scientific text processing by university students in a natural situation found that the students didn’t show any deep processing, but rather two kinds of shallow processing, produced by either using their (limited knowledge of) expository structures, or by representing the information in the text more precisely.

So should beginning students be told to study texts more deeply? The researchers of this study didn’t think so. Because introductory texts suffer from these problems I’ve mentioned, in particular that of incomplete explanations, they don’t lend themselves to deep processing. The researchers suggest that what introductory texts are good for is in providing the extensive practice needed for building up knowledge of expository structures (and hopefully some necessary background knowledge of the topic! Especially technical language).

To that end, they suggest students should be advised to perform a variety of activities on the text that will help them develop their awareness of the balance between schema and textbase, with the aim of developing a large repertory of general and domain-specific schemata. Such activities / strategies include taking notes, rereading, using advance organizers, and generating study questions. This will all help with their later construction of good mental models, which are so crucial for proper understanding.

References: 
  • Baker, L. 1985. Differences in the standards used by college students to evaluate their comprehension of expository prose. Reading Research Quarterly, 20 (3), 297-313.
  • Elshout-Mohr, M. & van Daalen-Kapteijns, M. 2002. Situated regulation of scientific text processing. In Otero, J., León, J.A. & Graesser, A.C. (eds). The psychology of science text comprehension. Pp 223-252. Mahwah, NJ: LEA.
  • King, A. 1994. Guiding Knowledge Construction in the Classroom: Effects of Teaching Children How to Question and How to Explain. American Educational Research Journal, 31 (2), 338-368.

Understanding scientific text

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.

References: 

Otero, J., León, J.A. & Graesser, A.C. (eds). 2002. The psychology of science text comprehension.

Reading Scientific Text

There are many memory strategies that can be effective in improving your recall of text. However, recent research shows that it is simplistic to think that you can improve your remembering by applying any of these strategies to any text. Different strategies are effective with different types of text.

One basic classification of text structure would distinguish between narrative text and expository text. We are all familiar with narrative text (story-telling), and are skilled in using this type of structure. Perhaps for this reason, narrative text tends to be much easier for us to understand and remember. Most study texts, however, are expository texts.

Unfortunately, many students (perhaps most) tend to be blind to the more subtle distinctions between different types of expository structure, and tend to treat all expository text as a list of facts. Building an effective mental model of the text (and thus improving your understanding and recall) is easier, however, if you understand the type of structure you're dealing with, and what strategy is best suited to deal with it.

Identifying structure

Five common types of structure used in scientific texts are:

  • Generalization: the extension or clarification of main ideas through explanations or examples
  • Enumeration: listing of facts
  • Sequence: a connecting series of events or steps
  • Classification: grouping items into classes
  • Comparison / contrast: examining the relationships between two or more things

Let's look at these in a little more detail.

Generalization

In generalization, a paragraph always has a main idea. Other sentences in the paragraph either clarify the main idea by giving examples or illustrations, or extend the main idea by explaining it in more detail. Here's an example:

Enumeration

Enumeration passages may be a bulleted or numbered list, or a list of items in paragraph form, for example:

Sequence

A sequence describes a series of steps in a process. For example:

Classification

In classification, items are grouped into categories. For example:

Comparison / contrast

This type of text looks at relationships between items. In comparison, both similarities and differences are studied. In contrast, only the differences are noted. For example:

[examples taken from Cook & Mayer 1988]

A study [1] involving undergraduate students inexperienced in reading science texts (although skilled readers otherwise) found that even a small amount of training substantially improved the students' ability to classify the type of structure and use it appropriately.

Let's look briefly at the training procedures used:

Training for generalization

This involved the following steps:

  • identify the main idea
  • list and define the key words
  • restate the main idea in your own words
  • look for evidence to support the main idea
    • what kind of support is there for the main idea?
    • are there examples, illustrations?
    • do they extend or clarify the main idea?

Training for enumeration

This involved the following steps:

  • name the topic
  • identify the subtopics
  • organize and list the details within each subtopic, in your own words

Training for sequence

This involved the following steps:

  • identify the topic
  • name each step and outline the details within each
  • briefly discuss what's different from one step to another

[Only these three structures were covered in training]

Most effective text structures

Obviously, the type of structure is constrained by the material covered. We can, however, make the general statement that text that encourages the student to make connections is most helpful in terms of both understanding and memory.

In light of this, compare/contrast would seem to be the most helpful type of text. Another text structure that is clearly of a similar type has also been found to be particularly effective: refutational text. In a refutational text, a common misconception is directly addressed (and refuted). Obviously, this is only effective when there is a common misconception that stands in the way of the reader's understanding -- but it's surprising how often this is the case! Incompatible knowledge is at least as bad as a lack of knowledge in hindering the learning of new information, and it really does need to be directly addressed.

Refutational text is however, not usually enough on its own. While helpful, it is more effective if combined with other, supportive, strategies. One such strategy is elaborative interrogation, which involves (basically) the student asking herself why such a fact is true.

Unfortunately, however, text structures that encourage connection building are not the most common type of structure in scientific texts. Indeed, it has been argued that "the presentation of information in science textbooks is more likely to resemble that of a series of facts [and thus] presents an additional challenge that may thwart readers' efforts to organize text ideas relative to each other".

Most effective strategies

The fundamental rule (that memory and understanding are facilitated by any making of connections) also points to the strategies that are most effective.

As a general rule, strategies that involve elaborating the connections between concepts in a text are the most effective, but it is also true that the specifics of such strategies vary according to the text structure (and other variables, such as the level of difficulty).

Let's look at how such a linking strategy might be expressed in the context of our five structures.

Generalization

Restatement in your own words -- paraphrasing -- is a useful strategy not simply because it requires you to actively engage with the material, but also because it encourages you to connect the information to be learned with the information you already have in your head. We can, however, take this further in the last stage, when we look for the evidence supporting the main idea, if we don't simply restrict ourselves to the material before us, but actively search our minds for our own supporting evidence.

Enumeration

This text structure is probably the hardest to engage with. You may be able to find a connective thread running through the listed items, or be able to group the listed items in some manner, but this structure is the one most likely to require mnemonic assistance (see verbal mnemonics and list-learning mnemonics).

Sequence

With this text structure, items are listed, but there is a connecting thread — a very powerful one. Causal connections are ones we are particularly disposed to pay attention to and remember; they are the backbone of narrative text. So, sequence has a strong factor going for it.

Illustrations particularly lend themselves to this type of structure, and research has shown that memory and comprehension is greatly helped when pictures portraying a series of steps, in a cause-and-effect chain, are closely integrated with explanatory text. The closeness is vital — a study that used computerized instruction found dramatic improvement in memory when the narration was synchronous with the animation, for example, but there was no improvement when the narration was presented either before or after the text. If you are presented with an illustration that is provided with companion text, but is not closely integrated with it, you will probably find it helpful to integrate it with the text yourself.

Classification

Classification is frequently as simple as grouping items. However, while this is in itself a useful strategy that helps memory, it will be more effective if the connections between and within groups are strong and clear. Connections within groups generally emphasize similarities, while connections between groups emphasize both similarities (between closely connected groups) and differences. Ordering groups in a hierarchical system is probably the type of arrangement most familiar to students, but don't restrict yourself to it. Remember, the important thing is that the arrangement has meaning for you, and that the connections emphasize the similarities and differences.

Compare / contrast

This type of structure lends itself, of course, to making connections. Your main strategy is probably therefore to simply organize the material in such a way as to make those connections clear and explicit.

References: 
  1. Cook, L.K. & Mayer, R.E. 1988. Teaching readers about the structure of scientific text. Journal of Educational Psychology, 80, 448-54.
  2. Castaneda, S., Lopez, M. & Romero, M. 1987. The role of five induced learning strategies in scientific text comprehension. The Journal of Experimental Education, 55(3), 125–131.
  3. Diakidoy, I.N., Kendeou, P. & Ioannides, C. 2002. Reading about energy: The effects of text structure in science learning and conceptual change. http://www.edmeasurement.net/aera/papers/KENDEOU.PDF

Metacognitive questioning and the use of worked examples

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).

More reading

Here are some papers by the creators of IMPROVE on their studies into the benefits of metacognitive instruction (in PDF format):

http://www.dm.unipi.it/~didattica/CERME3/proceedings/Groups/TG8/TG8_Kramarski_cerme3.pdf

http://www.hbcse.tifr.res.in/episteme/allabs/zemira_abs.pdf (no longer available)

http://www.icme-organisers.dk/tsg18/S32MevarechKramarski.pdf (no longer available)

References: 
  • Mevarech, Z.R. & Kramarski, B. 2003. The effects of metacognitive training versus worked-out examples on students' mathematical reasoning. British Journal of Educational Psychology, 73, 449-471.

Successful Transfer

Transfer refers to the extent to which learning is applied to new contexts.

Transfer is facilitated by:

  • understanding
  • instruction in the abstract principles involved
  • demonstration of contrasting cases
  • explicit instruction of transfer implications
  • sufficient time

Learning for transfer requires more time and effort in the short term, but saves time in the long term.

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!

References: 
  • National Research Council, 1999. How People Learn: Brain, Mind, Experience, and School. Washington, D.C.: National Academy Press. http://www.nap.edu

1. Scholckow & Judd, described in Judd, C.H. 1908. The relation of special training to general intelligence. Educational Review, 36, 28-42.

Regulating your study time and effort

Knowing how well or how poorly you know something is critical to effectively allocating your study time and effort.

The more difficult the material being learned, the worse we tend to be at estimating how well we know it.

Various learning strategies improve our awareness of how well we know something.

Learner attributes are also important, particularly our attitudes to learning and beliefs about our abilities.

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].

References: 
  • Peverly, S.T., Brobst, K.E., Grahan, M. & Shaw, R. 2003. College adults are not good at self-regulation: A study on the relationship of self-regulation, note taking, and test taking. Journal of Educational Psychology, 95 (2), 335-346.
  • Thiede, K.W., Anderson, M.C.M. & Therriault, D. 2003. Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95(1), 66-73.
  1. Dunlosky, J. & Nelson, T.O. 1992. Importance of the kind of cue for judgments of learning (JOL) and the delayed-JOL effect. Memory & Cognition, 20, 374-380.
  2. Mazzoni, G. & Nelson, T.O. 1993. Metacognitive monitoring after different kinds of monitoring. Journal of Experimental Psychology: Learning, Memory and Cognition, 21, 1263-1274.
  3. King, J.F., Zechmeister, E.B. & Shaughnessy, J.J. 1980. Judgments of knowing: The influence of retrieval practice. American Journal of Psychology, 93, 329-343.
    Lovelace, E.A. 1984. Metamemory: Monitoring future recall ability during study. Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 756-766.
    Shaughnessy, J.J. & Zechmeister, E.B. 1992. Memory monitoring accuracy as influenced by the distribution of retrieval practice. Bulletin of the Psychonomic Society, 30, 125-128.
    Ghatala, E.S., Levin, J.R., Foorman, B.R. & Pressley, M. 1989. Improving children's regulation of their reading PREP time. Contemporary Educational Psychology, 14, 49-66.
    Pressley, M., Snyder, B.L., Levin, J.R., Murray, H.G. & Ghatala, E.S. 1987. Perceived readiness for examination performance (PREP) produced by initial reading of text and text containing adjunct questions. Reading Research Quarterly, 22, 219-236.
  4. Thiede, K.W. & Anderson, M.C.M. 2003. Summarizing can improve metacomprehension accuracy. Contemporary Educational Psychology, 28,
  5. Thiede, K.W., Anderson, M.C.M. & Therriault, D. 2003. Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95(1), 66-73.
  6. Bjorklund, D.F. & Schneider, W. 1996. The interaction of knowledge, aptitude, and strategies in children's memory performance. In H. Reese (ed.), Advances in child development (vol. 26, pp. 59-89). New York: Academic press.
    Ceci, S.J. 1996. On intelligence: A bioecological treatise on intellectual development. Cambridge, MA: Harvard University press.
  7. Dweck, C. 1999. Self-theories: Their role in motivation, personality, and development. Philadelphia: Psychology Press.
Syndicate content