Viewing the connections of data and information

Part of the difficulty in this area is the lack of apparent agreement on the meaning of the terms data, information and knowledge. Some dictionaries say that knowledge is the same thing as information, but that data is quite different, whereas others say that data is the same thing as information, but that knowledge is quite different. That such fundamental notions can be so loosely defined is pretty amazing. Critical Thinkers, of course, can't afford to be so lax.

Even professors and other experts often use the words interchangeably, as if they're the same thing. In fact, they're quite different. Their relationship is hierarchical and the arrangement is like a pyramid:

• Data: At the bottom of the hierarchy. Data consists of facts and figures.
• Information: In the middle of the hierarchy. Information comprises data that has been organised to a greater or lesser extent.
• Knowledge: At the top of the hierarchy. Knowledge is certainly like information but rather purer, grander and certainly rather harder to find.

Another way of looking at the relationship between information and data is that the key factor is the degree to which items of data are interlinked. Take a look at Figure 8-1.

In the left hand panel, data consists of dots, showing each bit of data in splendid isolation. But in the middle panel, the data has been made ‘sense of’ and connected up. This network of connections is information. Finally, a possible view of knowledge as information further organised within collective, socially constructed linked structures.

Joining the (data) dots to create information

John Dewey, a professor of education in the United States — and something of a progressive — created his Recipe for Education, which concerns the importance of the relationship between facts (data) and how people turn them into information.

Read through and then test your skills of comprehension on the following argument from Professor Dewey on democratic education. Try to reduce it to just one line, and then compare what you extract to my note at the end of this chapter (you can read the whole article at http://www.the-philosopher.co.uk/dewey.htm ):

1. The human mind does not learn in a vacuum; the facts presented for learning, to be grasped, must have some relation to the previous experience of the individual or to his present needs; learning proceeds from the concrete to the general, not from the general to the particular.

2. Every individual is a little different from every other individual, not alone in his general capacity and character; the differences extend to rather minute abilities and characteristics, and no amount of discipline will eradicate them. The obvious conclusion of this is that uniform methods cannot possibly produce uniform results in education, that the more we wish to come to making every one alike the more varied and individualised must the methods be.

3. Individual effort is impossible without individual interest. There can be no such thing as a subject which in and by itself will furnish training for every mind. If work is not in itself interesting to the individual he cannot put his best efforts into it. However hard he may work at it, the effort does not go into the accomplishment of the work but is largely dissipated in a moral and emotional struggle to keep the attention where it is not held.

—John Dewey (in The Philosopher, 1934)

Another question to ask yourself is what would this argument mean in practice? In his book, Democracy and Education, Dewey gives an example of a man entering a shop showroom full of different chairs. He says that the man's past experiences will help him choose the chair that best suits him. And the more experience he has with various chairs, the better prepared he will be for selecting the correct one.

Everything he knows about chairs comes from the connections that he's created in his mind in the past, such as how comfortable they are to sit on, how difficult to clean, how strong and so on. These connections form the content of his knowledge about chairs. This kind of content is what enables people to make the new connections needed in new situations.

We respond to its [the new experience's] connections and not simply to the immediate occurrence. Thus our attitude to it is much freer. We may approach it, so to speak, from any one of the angles provided by its connections. We can bring into play, as we deem wise, any one of the habits appropriate to any one of the connected objects. Thus we get at a new event indirectly instead of immediately—by invention, ingenuity, resourcefulness. An ideally perfect knowledge would represent such a network of interconnections that any past experience would offer a point of advantage from which to get at the problem presented in a new experience.

—John Dewey (Democracy and Education, 1923)

So Dewey says that information, let alone items of knowledge, can't really be considered in isolation. Nonetheless, there is some sort of difference worth making. The following simple example illustrates the distinction in a nice, simple case.

Suppose that I measure rainfall in my garden for two years and write the figures into a notebook. The list of figures comprises the data. I then make a chart out of the data and send it to my local paper along with a letter explaining that my research shows that it has been a very wet summer. Both the chart and the view expressed in the letter are kinds of information — ways of processing the data.

Data just ‘is’. Data is ‘facts’ that can't be argued about, although (to extend my example) my rain gauge may be faulty. Nonetheless, the list of readings I have is my data — for better or worse.

As soon as I turn them into a chart — that is, organise the data — put it under a heading and make it ‘send’ a message, the data becomes information. (That's also what Carl Hempel means when he says that the transition from data to theory requires a bit of creative imagination — see Chapter 6.) Information is built up out of data, from facts. The facts don't have to be statistical ones like rain measurements, of course. They can come from all kinds of experience from listening to music to watching the sunset. (Qualitative data is descriptive information.)

Watching for errors and biases

Alas, the transition from data to information brings with it the possibility of error and bias.

In the measuring rainfall scenario in the preceding section, for example, I could easily introduce distortions through the decisions I take for things such as which readings to include (and which to leave out), the scales used for the axes on my chart or maybe even my choice of measuring equipment. Perhaps, to strengthen my point, I might have been tempted to start the readings a bit later than I originally intended to avoid a dry period in the spring!

For reasons such as these, someone seeing my chart could reasonably dismiss it as only my opinion (my letter to the paper certainly is), yet not, I think, the underlying data. They constitute raw and brute facts about my garden as recorded by my gauge.

Here's a question for you to mull over: When I say ‘it's been an exceptionally wet summer’ — is that a statement of fact or merely an opinion? Flip to the end of this chapter, and compare your view with my take on this problem.

Thinking critically with Benjamin Bloom

Benjamin Bloom was one of a group of educational psychologists in the US who devised a pyramid model that they said represented different ways of learning. They made it a pyramid to show that the highest form of learning, which for them was evaluating information, was ultimately based on a much broader level of information that had just been, well, learned.

Meeting Bloom's Taxonomy

Bloom wanted to promote higher forms of Critical Thinking in education, such as the use of analysis and evaluation of material, away from teachers just drilling people into remembering facts and rote learning. His system is now half a century old — yet still looks pretty ‘progressive’ in educational terms, which tells you something about how stuck-in-the-mud schools and colleges are as regards learning.

Unlike in the ‘no frills, economy class system’ dissected in the preceding section, which places knowledge at the top, Bloom's Taxonomy — nothing to do with creepy stuffed animals, thankfully, but a system for classifying or arranging things or concepts — starts by placing knowledge at the bottom of the heap (see Figure 8-2). The pyramid then rises upwards through six levels. Here they are, with examples of each one:

• Level 1 – Knowledge: Normally people think of knowledge as something wonderful, even powerful. But Bloom defines ‘knowledge’ simply as the remembering of previously learned material. Nothing very grand about that, which is why he puts it right at the bottom of his hierarchy of learning. An example is recalling data or information, such as knowing the names of different kinds of trees.
• Level 2 – Comprehension: The next rung up, comprehension, is the ability to grasp the meaning of material. But this kind of understanding is low-level stuff too. An example is understanding texts, instructions and problems, such as being able to restate something in your own words.

• Level 3 – Application: This stage is a step up the hierarchy, because it requires the ability to apply, to use, the ‘learned material’ in new situations. An example would be the practical use of concepts or skills: someone who has studied the difference between facts and inferences is able to apply this skill to certain texts in his examination of arguments.

  But if you just rote learn how to apply something that you've been told about (as many people remember being drilled to do in classes), this is still learning at Level 1.

• Level 4 – Analysis: Only with analysis (a word which means to take things apart) does learning require an understanding of the material. You can't rote learn how to analyse things, though I suppose you can rote learn certain steps that may help you to do it. Analysing is, say, splitting up a text into its component parts to better see and understand its structure — perhaps to spot certain logical fallacies in someone's reasoning.
• Level 5 – Synthesis: Follows analysis because it refers to the ability to put information and ideas together to create something new. Creativity is involved. For example, the skills of synthesis are needed when constructing a new structure from diverse elements or reassembling parts to create a new meaning or interpretation: such as writing an original essay using multiple sources, or designing a garden choosing from a range of possible tools and approaches.
• Level 6 – Evaluation: This, the top level of the Taxonomy, is defined as the ability to assess the value (or perhaps the ‘usefulness’) of the knowledge comprehended, applied, analysed and synthesised at the earlier levels. Evaluation is really the stuff teachers wax long on (Benjamin Bloom was a professor!) about the value of ideas or materials, whether arguments work and even about the merits, skills and abilities of people. A typical example could be choosing the best book when preparing to study a new subject.

Bloom's Taxonomy was primarily created for academic education, but it's relevant to all kinds of learning.

Thinking creatively with Calvin Taylor

An American Professor of Psychology, called Calvin Taylor, is an important figure in the study of human creativity. His key idea was that many different kinds of skills and abilities exist and that people who're gifted at one thing may not be much good at many others.

Taylor claimed that typical intelligence tests measure only a small fraction of talents that have been identified: 10 per cent at most. So he proposed that multiple talents should be evaluated in the classroom instead. He came up with nine ‘talent areas’ that he said were often sidelined by the emphasis on traditional measures of talent and ability: productive thinking, planning, communicating, forecasting, decision-making, implementing, human relations and discerning opportunities.

Great news! Many people rated as low performers by the traditional measures rise to at least ‘average’ level in one or other of the new talent areas. Taylor claimed that one third of students would probably be highly gifted in at least one of the new talent areas. This new rating would thus increase their motivation, and also allow efforts to be directed more constructively towards ‘what people are good at’ instead of uselessly at what people aren't good at.

Feeling your way to academic success!

So, plenty of research suggests that motivation is crucial to success, but this sort of evidence points at a problem.

Pause a moment to put your Critical Thinking hat on and jot down some possible weaknesses in the argument that boosting motivation is the key to success. (You can read what I come up with at the end of this chapter.)

Nonetheless, solid research shows that motivation is a key indicator not only of school or academic success but also for achievement in all areas of life. Hardly a surprise, of course. But motivation isn't something that gets taught at school or college — though a bit is trialled as in-service training in the workplace.

Instead most educational activity is firmly focused on ‘delivering the curriculum’ and pedagogy (teaching methods). This approach is despite the fact that much research shows that psychological factors (such as motivation) often matter far more than so-called cognitive factors (such as verbal skills or even general intelligence) in terms of eventual results. Feelings matter! Feelings, say, about a student's own worth, towards the school or college, or beliefs about job prospects.

Perusing the paradoxical nature of praise

In the 1990s, as part of what was later called the self-esteem movement, some teachers attempted to instill more positive beliefs in their flocks by concentrating on making them ‘feel good about themselves’ and their abilities — and their prospects of success. Unfortunately, the strategy intended to improve students thinking rested on flawed thinking. The self-esteem movement assumed that assuring students that they were exceptional people, clever and talented and so on, would not only make people feel positive about themselves, but also increase their motivation to work and do well too. In fact, it had the opposite effect.

Praising students for their ability tends to produce a defensive response — afterwards students want to ‘rest on their laurels’, so to speak. The study found that students who were praised like this were more likely afterwards to try to avoid starting hard problems — because such problems seemed to carry a risk of failure (a risk of losing their ‘laurels’!).

Praising abilities makes recipients think that they have a fixed level of intelligence, instead of them seeing learning as something they can nourish, and developing and increasing their intelligence.

Developing the necessary mindset

The reality is that in all areas of life, learning and achievement are about the willingness to explore new areas, and to work persistently, steadily and conscientiously. The paradox of praising someone's intelligence or skill levels is that it can discourage that person from progressing. (Which is not to say that plenty of other ways exist to discourage people too — such as calling them stupid and useless.)

The mindset needed to achieve and be successful has two essential ingredients:

• A willingness to take on challenging tasks that provide opportunities to learn new things, instead of settling for easier tasks in your comfort zone.
• The psychological skills of persistence and self-control, instead of being the person who delivers a half-done project because the deadline clashed with a favourite TV show or last week's hot weather and fresh air seemed too enticing. Check out the nearby sidebar ‘Would you pass the Marshmallow Test?’ for research in this area.

Dewey's recipe for education

My quick note would be: knowledge is all about connections. The rest of the material is interesting, but this is the key idea.

‘It's been an exceptionally wet summer’

I have the rainfall measurements, but the statement is still definitely my opinion. Consider the background context of the claim: with which other towns or areas do I compare my figures and over what time periods? Plenty of value judgements are lurking behind even this simple claim.

Research on the problems of demotivation

I can see at least three things that should ring alarm bells for a Critical Reader with this kind of research:

• The claim that low motivation explains students dropping out from school. The significant finding to support this view would be low motivation among dropouts and high motivation among those who see their courses through. However, perhaps in their zeal to see low motivation everywhere, the researchers also identified evidence of low morale among those who stay in school, which undermines the idea that motivation is the key factor in whether a student stays on or drops out.
• A ‘cause and effect’ law applies to this kind of claimed relationship. Students who struggle academically may become demoralised and drop-out, instead of vice versa.
• The evidence offered is too vague to be persuasive. The researchers offer no names or dates for the survey, which is essentially a bit of hearsay, or these days, ‘something read on the Internet’. Such research wouldn't stand up in a court of law — it doesn't really stand up in an essay!