Proceedings of the First AAAS Technology Education Research Conference
Theoretical and Empirical Issues of Technology Education Research
Robert McCormick
The Open University, UK
Introduction
It's very interesting to come all the way from the UK to a conference of this
kind, which is taking a strategic view of technology education. I don't think
such a conference has ever taken place in the UK at all, although many people
have been involved in thinking about agendas for research, the University
of Leeds, for example. Ironically this was drawn up by a group of science
educators. There is a very strong tradition of science educators doing research,
in contrast to the situation in technology, reflecting for me the difference
between technologist and scientists perfectly. Technologists are the doers,
and the scientists are the researchers, and I think we carry that through
into what we do in fields of education. Technology educators do a lot of curriculum
development, but much less research about what goes on when the developments
take place.
This paper will be in four sections. First of all, I will outline the theoretical
and empirical issues for research in technology education. Second, I will
look at some types of knowledge—procedural and conceptual knowledge,
which stem from the theoretical issues—in relation to researching technology
education. I will effectively be giving you a case study of the research that
my colleagues, in particular Patricia Murphy, and I have done at the Open
University. I want to do this because it is important to ground discussions
in actual research, as opposed to just talking about it. Third, I want to
explore a particular approach to knowledge, that is, qualitative knowledge
and qualitative reasoning. Fourth, I will outline various approaches to research
and how these might relate to classroom change. Finally, I will suggest a
brief list of questions that could contribute to an agenda for research.
The theoretical and empirical issues
The first theoretical issue concerns the definition of technological knowledge
and what the nature of that knowledge should be. The debate on this has been
going on for some time; it was a major concern at JISTEC (Jerusalem International
Science and Technology Education Conference) in 1996. The concern was to work
out a map of technological knowledge. I don't think that is going to be very
productive, as it is a very large issue. My concern here is with how
we define and think about that knowledge.
The second theoretical issue is to explore the relationship between learning
and knowledge. We often see knowledge as “out there,” as something
to talk about and map; but that stance has a particular view about learning
encapsulated within it, and a particular view about the mind. This view of
learning and mind is legitimate, but not one that I happen to share. My view
is that we need to see an inter-relationship between learning and knowledge.
The first of the empirical issues is that part of the problem about seeing
an inter-relationship of knowledge and learning has to do with the nature
of context: where we learn, the context within which we learn. Such a view
of context is extremely difficult to research. Science educators researching
misconceptions are concerned with abstract knowledge, and are trying to establish
whether the student has a grasp of these abstractions. Those interested in
knowledge in context cannot “strip the context out,” which is what
the science educator does. This stripping away of context is necessary to
the science educators because it is “unimportant.” This stance is
problematic in technology education, and hence an important empirical problem.
My second empirical issue concerns research. I think we need to be concerned
about how the research relates to change in the classroom, and how that change
takes place, rather than just with methodological issues, and I will explore
that briefly at the end.
Technological knowledge
First let me explore two basic types of technological knowledge: conceptual
and procedural.
- Procedural knowledge includes such things as: design, problem solving,
planning, systems analysis (approach), optimization, modelling, strategic
thinking (heuristics v algorithms v metacognition).
- Conceptual knowledge: systems.
The Benchmarks deal with a limited range of procedural knowledge in relation
to technology: design and problem solving. But there are many other procedural
aspects used in technology. However, we know very little about them. Not only
do we not know how technologists use them in a way that we could draw upon
them as tools in education, but also, we don't know their inter-relationships.
Nor do we know whether they are of the same kinds of thinking or level of
cognition. I'll say more about this later.
Most people would assume there must also be conceptual knowledge in technology.
But are there particular concepts related to specific aspects of technology
or more general concepts across all of technology? Systems knowledge, recognised
in Benchmarks as conceptual knowledge, is very abstract. Considering
technology as a generality (rather than a particular technology), then we
have to ask “What are the concepts that apply across all technology?”
There is a problem in coming up with a very good list.
“Systems” is a perfect example of the difficulties. A control system
is a particular view of a systems, both as a way of analysing the world (as
procedural knowledge) and as a conceptual framework. If you are dealing with
an air traffic control system, or an airport as a system, then this involves
human systems; the interaction between human, physical, and other kinds of
systems. This requires ideas of “soft systems approaches” and they
work in a completely different language from say “feedback control systems”
in electronics. How useful, then, is the generality of the concepts of systems?
Whatever the difficulties of particular concepts in technology, it is important
to recognise that they may differ from those in science. The concepts that
are important to technologists are not the concepts related to theories, such
as kinetic theory of gases, but related to laws (such as Boyle's law). Driver
et al. (1996) make an important distinction between theories and laws. The
laws are the empirical relationships: stress, strain, Young's Modulus and
so on. They are the focus of a lot of technological concepts (I will come
back to this later when I consider how the benchmarks represent technological
knowledge). Now, they happen to be particular concepts, because they relate
to particular technologies or areas of technology. In food technology, there
are a different set of concepts. Thus in defining the nature of technological
knowledge we need to clarify if we are discussing particular concepts (special
to particular technologies) or whether they are generalities. If we are interested
in technological literacy, we are not interested in people being expert in
particular sets of technologies. That is a great tension for us in relation
to technological knowledge as part of literacy for all children
and young people.
Researching Technological Knowledge
I will deal with both procedural and conceptual knowledge, starting with procedural
knowledge, using some of the work on problem solving in technology classrooms
that my colleagues and I have done at the Open University. In Britain we call
the curriculum subject “design and technology”! This is partly to
encapsulate this notion of the processes (design) being involved—it happens
to be the design process that is the dominant kind of procedural knowledge
but, as I will show, it is not quite as straightforward as that.
Why did we pick problem solving? Well, it's about the most important procedural
knowledge that occurs in technology, and indeed, in many other areas of activity.
It is very interesting that the benchmarks on design and systems mention problem
solving more than design. The notion of “clarifying the problem, thinking
of alternatives, implementing it and evaluating it” is the language of
problem solving, not of design. (Elsewhere I have discussed this distinction;
McCormick, 1999a.) Another reason it is important are the many claims that
technology education will improve or encourage children's problem solving.
Almost any technology education curricula you look at in the world will have
problem solving as an important part. Although it is central, we know almost
nothing about it in the classroom; a difficulty that is not unique to technology
education. For example in science the most recent review of research on problem
solving was in 1988 (Garrett, 1988). There has been very little work on the
nature of science problem solving itself, rather than as a way of learning
conceptual knowledge. Mathematics has more (e.g., Hiebert et al., 1996; Schoenfeld,
1988).
Before I describe some of our approaches and findings in problem solving in
technology I want to say something about our orientations in relation to learning.
Conceptions of learning inform views of problem solving in the classroom.
Cognitive psychologists, who may take an information processing approach to
learning, view the brain as a store for concepts; these concepts are selected
to suit a situation (a problem) and applied. That is a particular view of
mind, and not a view that I share. Nevertheless their findings are important
and here I will only briefly indicate them. First, that knowledge is domain
specific (e.g., Glaser, 1984 and 1992). In the area of technology education
where we deal with many technologies (domains), that becomes an important
issue. Second, strategic thinking and the nature of such processes (procedural
thinking), can be seen in terms of heuristics, algorithms and metacognition,
which can be seen as an hierarchy. Heuristics are “rules of thumb,”
that is procedures that usually, but not always, work; they are a good rule
of thumb to use. We teach students these all the time in classrooms. We also
give them an algorithm and we say, “Do this, do this, and then this,
and this, and it will work every time.” A lot of mathematics is taught
like that. Sometimes we teach design like that; it doesn't often work, but
we pretend it does, a point I will come back to.
Metacognition is, as the name suggests, where you can monitor what you do,
to become aware of what you are doing. “Should I do this or that next?”:
This is a reflective or self-regulation activity but we very rarely find that
kind of reflective talk going on in classrooms. Take the example of a “fair
test” in science, where students are taught to do fair tests, but few
teachers ever discuss the question: “When do you use a fair test?”
So students may never know when to use such tests—the teacher always
tells them and they don't develop metacognition. In design a similar situation
exists; for example, “When do you stop generating ideas?” Is 10
enough, 15, 20 enough? Who knows? It is usually 4, perhaps because it is possible
to divide a page into 4, and put the 4 ideas there. But there is rarely a
debate about this in technology classrooms. For example, students could each
have one idea, but share the ideas and discuss why this is useful.
These insights from cognitive psychology still leave an important difficulty
for me: what constitutes the nature of the problem that they're going
to involve students in solving? Cognitive psychologists who research problem
solving use bizarre problems for subjects to solve; getting people across
a river in a boat that only carries 2 and asking how they can all get across
(the “missionaries and cannibals” problem, Reed, Ernst and Banerji,
1974).
There are many of these complicated tasks, which are very carefully controlled,
but which bear no relation to real-world problem solving outside the laboratory.
There are of course exceptions, for example, Gott, 1988. A second difficulty
arises from the question; who is it a problem for? A researcher gives a problem
to a subject, as they are called in the cognitive psychology area. The subjects
may care nothing about this problem, it is not their problem, but this researcher
has given it to them, and they'll get on with it, and they'll do it because
they are compliant people (and may even be paid to do it!). Students in classrooms
are as compliant as this most of the time. Teachers give them problems, but
they may not be problems to the students. Their problems may be something
quite different, and that is a very important issue.
So much for cognitive science, now let me turn to another theoretical approach
to learning, situated cognition. It is a complicated area and here I will
only outline its features, but it is an area which has a completely different
view of mind from that of cognitive science. A crucial idea is that cognitive
processes differ according to the domain of thinking and the specifics of
the task context. That is, the way we think depends on where we are, and the
specifics of the context and the tasks that we are doing. We don't think about
problems or situations in abstract ways; we tend to think about them in relation
to what we are doing in that situation. This leads on to an important second
point, that there is an intimate connection between knowing and doing. Now,
it isn't just that doing is an efficient way of learning (the old so-called
Chinese proverb that ends with I do and I understand).
Research shows that action affects thinking, and thinking affects action (e.g.,
Scribner, 1985). It isn't a one-way process; i.e, that a person first thinks
and then does. When a person is talking, that talking is interacting inside
the brain. The cognition is affecting thinking about the next words coming
out and the words that are coming out are affecting thinking. This is crucial
for technology education, because that is in a sense what we are trying to
get children to be able to do; to think through their doing, and for the feedback
from this doing to affect their thinking.
A third feature of situated cognition is the notion of enculturation and participation.
Learning is a process of enculturation into a domain, through participation
in shared activities. So that when we learn, we learn to become something.
One argument for learning technology is to understand the nature of technologists
and how they work; you want to participate in technological activity. It's
exactly the same for science and for any other subject. Schoenfeld (1996)
takes an approach to mathematics education where he argues that students should
be involved in real mathematical thinking and should debate mathematics with
each other. That is what real mathematicians do, but of course, you might
ask, “So, what if you don't want to be a mathematician, a scientist or
a technologist? What do you do for mathematical, scientific or technological
literacy?”
Some science educators have thought about science for “everyday life”
as opposed to science to understand what scientists do (Miller, 1996). What
about technology for everyday life? Participating, living with technology,
not participating in doing technology. This is an interesting debate
central to the issues in this conference.
The fourth feature of situated cognition is the notion of activity as authentic.
This authenticity has two sides. First, personally authentic: a student has
to be involved, and the learning has to be meaningful. If the activity is
problem solving, it should be on a problem that matters and means something
to the learner, and it also has to be culturally purposeful, the second form
of authenticity. We frequently find activity in classrooms, where students
are really involved in exciting things. It isn't important. It does not relate
to the technology world outside of school. So, of any classroom activity it
is necessary to ask the question, “Is it culturally meaningful and important?”
Finally, the definition of problem solving. In situated cognition a problem
is a “dilemma which a problem solver is emotionally engaged,” and
that makes the problem authentic (Lane, 1988). This gives a completely different
conception of problem solving than is normally offered.
Often debates about procedural knowledge like problem solving are unproductive
because we are arguing from different understandings of the nature of knowledge
and learning. Contrast the situation of a cognitive scientist giving a “problem”
to a subject and that situated cognitive view of a problem as a dilemma.
The Open University problem solving research
Let me start by outlining how we carried out the research in design and technology,
where students work on extended projects.
Our research methods consist of the following:
- intensive observation of all project sessions over 6–12 weeks
(perhaps up to hours per week),
- video recording of a small group of students,
- observation notes,
- interviews with students and teacher, and
- copies of written work and photos of outcomes.
We follow a design and make project, as it's called in Britain, over 6 to 12
weeks. We video-record usually 4 of the students, and try to video-record
everything they do. This is a complex and uncertain process in a technology
classroom. Four students may be sitting around the table, all working on something.
Suddenly one gets up and walks to another part of the room. And then another
one gets up and goes somewhere else. With one camera, selecting who to record
is difficult and results in loss of data. We eventually wired students up
with radio microphones so that at least we would hear what they were saying.
Without these, students might go off to talk to the teacher and we wouldn't
know what was discussed. Running after them, and standing behind them, listening
to what they're saying is far from satisfactory. With them wired up, we could
record four of them on separate audio tracks. However, dealing with that data
is still complex; four tracks of audio with students simultaneously talking,
but in often unconnected conversation at different times. It is essential
to have observational notes that will record what students are doing, to make
later sense of the data at the analysis stage. The video does not capture
everything, not only in terms of following what students do around the classroom,
but also because a particular shot may miss what the student is doing with
his or her hands. For example the camera may be focused on the head, and when
analysing the video you find yourself saying, “Drop the camera, drop
the camera,” to try to see what the student is doing with her hands.
Thus, trying to capture data is difficult in a technology classroom.
Another aspect of data collection is student interviews and these worked best
focused on the outcome of their project work. We also interview teachers,
usually several. We keep copies of written work, any of the outcomes students
produce, usually in the form of photographs. This all provides rich but very
messy data for analysis.
Now, let me turn to the findings of our research. One of the interesting questions
is to ask what teachers think problem solving is. I do an experiment with
teachers on in-service courses: I give out four sheets of paper about 6"
x 6" and I say write on each a phrase or a sentence that tells you something
about design, big enough so that I can stick it on the wall to be
read by the group. When they have done this I collect them all in. And then
I give them another set, and ask them to do the same for problem solving,
and then the same for planning. I put them up on the wall under headings
of design, problem solving and planning. The same statements occur under each
heading, because there is no clear distinction among them in teachers' minds.
This reflects a lack of clarity in technology education; as I said, the benchmarks
don't distinguish clearly between design and problem solving.
When we interviewed teachers, we obtained three quite clear ideas on problem
solving. One is that they believe it is a general purpose skill.
In Britain technology education involves work in food, textiles, in what we
call “resistant materials” and “electronics.” Teachers
believe that they are teaching students the skill of problem solving or design
that applies across all those areas. As I will shortly indicate, this is not
supported by research.
The second view of problem solving is not concerned about the process of problem
solving, but rather about setting students a problem, as a way of setting
the scene for their work. Such teachers like to start with a problem. In medical
education problem-based learning has become important (e.g., Alavi, 1995),
but the focus is not on teaching problem solving per se. It is
concerned to set a context for learning about medicine or learning about science,
etc.
A third approach is what we call “emergent problems,” something that
comes out of situated cognition. That is, technology teachers will often talk
about students who complain to them about how difficult technology is. When
students complain about technology learning “lots of problems” they
reply, “Yes, that's what technology is all about.” The technology
classroom is full of all these problems, that happen all the time, as students
work on designing and making projects.
Although we have found these three distinct views of what teachers think about
problem solving in technology, there isn't actually very much research on
teachers' views. So this gives a topic for research in technology education.
In the literature on problem solving there is very little evidence to support
the view that it is a general purpose skill that applies across all areas
or domains. As I noted earlier, cognitive psychology, for example, stresses
the importance of domain knowledge. When trying to solve a problem, success
depends on knowing a lot about the area within which the problem requires
solving. In some sense, this is fairly obvious; someone working in electronics
is not necessarily going to be terribly good at aircraft design or in solving
related aerodynamic problems. It is necessary to understand the domain within
which the problems are being solved. There is therefore a conflict between
what teachers (and others) believe and the findings of research.
It would be wonderful if we could teach a general problem-solving skill. In
Britain there is a notion of “key skills,” which are to be taught
as part of the whole school (also the university) curriculum, with problem
solving being a central skill. The difficulty with this view lies in transfer;
there is so little evidence for transfer. Where there is evidence, it is contested
(Greeno, 1998). There has been a debate in educational research especially
over the last 5 years on the nature of transfer and whether it is possible.
(Educational Researcher has carried this debate.) We need to
recognise this controversy and to understand the debate if we are to clarify
the nature of technological problem solving.
Now let me turn to what we found in classrooms. First, we often treat design
or problem solving as a series of steps. An “algorithm” notion of
design or problem solving characterises it as consisting of posing the problem
and thinking about the problem, clarifying it, thinking of alternate solutions,
implementing it, and then evaluating it. This can become a ritual
which is set up and lessons become structured around it. But it is a ritual
that does not affect the students' thinking. Asking students to create a predetermined
number of ideas is one of those activities that become part of a ritual. (As
I noted earlier, you have to have four ideas. We don't say why, but tell students
they have to have four.) If you look at student work, at their design portfolios,
it appears they followed such a procedure or algorithm, because it's all nicely
laid out in terms of the problem solving or design steps. If you question
them about their design ideas they say, “Oh, yeah, I created that fourth
idea after I finished,” partly because of the requirements of assessment.
They are required to show the development of ideas and gain marks for the
number of ideas, and so they do it afterwards, and that's what is called a
veneer of accomplishment (Lave et al., 1988). It looks as if they
can carry out the process, but it hasn't affected their thinking. This also
happens in science and mathematics. In mathematics when students are solving
problems, the teacher sets them a problem, tells them how to do it. But they
actually solve it a completely different way, writing it up the way the teacher
wanted them to do it, because that is what they are supposed to do (e.g.,
Lave, 1992).
In secondary schools there is some awareness of ritual in scientific investigation.
But it is now appearing in primary schools, because primary teachers may not
feel confident, and want an algorithm to teach students and they use worksheets
that follow through this algorithm. What they may not do is affect student
thinking about how to carry out an investigation (Murphy and McCormick, 1997).
Our second finding relates to emergent problems. These are the problems students
spend their time on: knowing how to cut something, how to join something,
how to construct in a particular way, etc. These are the problems they are
engaged with, not the one the teacher poses at the beginning of the project
or lesson.
Now, let me turn to the problem-solving strategies that students used. We spent
a lot of time looking at the nature of problem solving in the video recordings
we made, and could not work out what these strategies were. They certainly
do not resemble the “algorithms” of problem solving that are so
often taught. The first strategy is what we called “knowing the game.”
This is where students try to work out the rules the teacher sets in the classroom,
and play to those rules. For example, two girls producing a mobile. One wants
to do something that clinks when the wind blows, so has an idea of using metal,
in the form of disks about two inches diameter. The other one has a moon and
planets going around it, so wants some kind of glinting material. The first
student plays the rules of the game. When the teacher takes them into the
workshop and says, “Here are three boxes. One with metal, one with plastic,
and one with wood,” the girl looking for a clinking effect goes to the
metal box, picking out eighth-inch mild steel. But to cut disks two inches
in diameter requires spending hours, first trying a large foot-operated guillotine,
then tin snips gripped in a vice and finally a file. She ends up with very
red hands, and takes a long time; a very inappropriate way of doing it. But
she learned quite a lot about mild steel, as it turns out.
When presented with the choice of the three boxes of material, the other girl
looks over in the corner and sees some aluminium (not available to the class)
and asks to use this. The teacher agrees, and she can cut this easily with
tin snips. This girl took this approach many times throughout the project.
She broke the rules of the classroom, knowing what she could and couldn't
get away with. She experienced different kinds of issues and problems from
the first girl, but she was avoiding certain kinds of problems. She may have
known quite a bit about materials, to know which was the choice to take, but
she experienced different problem solving from the other girl.
The second strategy is finding a solution from someone who knows. That is the
most common strategy. Let me illustrate this with an example of a moisture
sensor, which is an ubiquitous project in the UK. (Every D & T classroom
does the moisture project.) As a design and make exercise, the main focus
is on designing a box to contain the electronics. Again we studied two girls.
Girls give interesting insights into problem solving because of the way they
work and some of the ways in which they test teachers. The teacher in this
study defines the task in terms of making a box in which to put the electronics
(the circuit, the bulb or the little speaker, switch, etc.). This has to be
appropriate to the situation of detecting moisture or lack of it. He taught
them to cut in straight lines because when he said “box,” he had
in mind a rectangular box. He taught them to cut the material (styrene) in
straight lines with a steel ruler and a knife. He also gave them a jig so
that they could put the two edges together at right angles and run the solvent
along to stick the two together. But one of the girls wanted to do the shape
shown in Figure 1. She was detecting moisture (rain) on the washing line.
She wanted her box to look like the sock hung out on the washing line.
Figure 1: box to contain electronic components in
shape of a sock
She encountered three problems. The first, cutting the shape A (Figure 1),
is quite difficult, because the teacher had only taught her to do it in straight
lines not the complex curves she wanted. She goes to the teacher and says,
“What do I do?” He says, “Well, it's easy. All you do is mark
out the shape you want, and just cut it slowly with the knife following the
line.” And adds, “You'll have to put the two bits together for the
top and bottom of the box, and then file them off so that they are identical
in shape.” So she completes this task.
Next she wants to produce the B shape (Figure 1) where the styrene must bend
round the curve shape. The styrene they were using wouldn't bend around these
corners. Again she asks the teacher, “What do I do?” His reply:
“Just use the thinner styrene over there.” Again she carried out
this instruction.
The third problem was then how to stick A and B together. For the other two
problems the teacher had an instant response, an instant solution. This time
the teacher has to think about it, so comes over to the student's workbench,
picks up the two sides of A, and looks, puzzles a bit, looks around the bench,
and tells the student to hold one side. And so she holds one, and he puts
the other on top of it, picks up bits and looks at it, starts to think about
it, and says, “Right, cut 16 of these (small struts) and when you've
done that, come back and I'll tell you what to do next.” What the teacher
had done was to think about rigidity to hold the structure stable, and to
create some surface area to put the solvent along. None of that was shared
with the student at all. All the student received was the solution without
being involved in the problem solving. This continually being “given
solutions” becomes a culture of the classroom at the expense of a “problem-solving”
culture.
We find that teachers in elementary school, who work with younger children
(10- and 11-year-olds), are much better. When a student comes up with a problem,
the teacher asks questions about their problem, or poses alternative solutions
(because sometimes students can't cope with the questions). They give them
more than one solution, because what they are trying to do is engage students
in the problem and the problem solving. Such a teacher has to set up a completely
different culture in the classroom. It takes longer, and it is harder to do,
but it is crucial to foster problem solving.
The final strategy is the student collaboration model, and that happens in
a variety of ways. One way is through co-operation. In D & T in the UK
students are usually set individual projects, so they may be working alongside
each other on a table or a bench or whatever, and they can co-operate because
they're doing similar things; they are not identical, but similar enough to
help each other. They will share ideas occasionally, but more often than not,
they will simply share tasks; if one is good at soldering, that one will do
the soldering; if one is good at cutting, that one will do the cutting. It's
that kind of co-operation.
The second form of collaboration involves then dividing the task up: “You
do this bit, I'll do that bit. You're good at that and I'm good at this.”
Some of the learning is lost in this approach. But at least it is a way of
collaborating, because they have to put the two bits together at some stage,
and that is good collaborative problem solving.
The final form of collaboration occurs when they have a shared task, and they
can talk about it. This requires the design of the task to require
the students to collaborate. It has to be designed into it, so that there
is no way the students can avoid it. These group tasks are difficult in Britain,
because so much activity is driven by individual assessment; students have
to have their own product and their own design. (This focus on individual
assessment occurs because teachers have to report on each student's performance
at ages 7, 11 and 14.) This prevents collaboration that would allow a joint
problem, and hence some of the joint problem solving and sharing. Designed
correctly the tasks should require any solution to a problem to be brought
to the other students. In the real world of industry, individuals may work
on their own with that work contributing to the overall task. They share the
products of their work, not necessarily the process.
However, it is important to remember that often people in industry are expert
in the area in which they are working and students are not expert, they are
learning, and they do not have the skills for all the contributing aspects.
But how the problem solving is organized depends on your agenda. If your agenda
is to make students expert in a part of technological activity, and make them
learn to work with other people, then this “industrial approach”
is right. If you want them to be involved in the thinking processes of problem
solving, you need to do organize collaboration as well. It's not exclusive.
Underlying this desire for individual work is also the students' desire to
have a finished product that can be taken home. This leads to what a colleague
and I called “the tyranny of product outcomes” (McCormick and Davidson,
1996).
These four strategies of problem solving in the technology classroom differ
from the way problem solving is depicted in the benchmarks, and the way we
normally think about it. How common are these kinds of strategies? We need
to know a lot more about whether these are consistent strategies across classrooms;
another item on the agenda for research.
Researching conceptual knowledge
As I indicated earlier, technology educators trying to learn something from
science education in relation to conceptual knowledge is problematic. In science
context is ignored, because abstractions are the important focus; science
educators tend to ignore practical knowledge and play it down. It isn't just
a matter of status of knowledge as in the philosophical debate that says practical
knowledge is not valued because we value the power of abstractions (see Lewis,
1993). If a person can use an abstraction in a variety of situations, it is
a very powerful technique. In fact few of us can do this, instead we work
within our own domains, using science or mathematical abstractions within
a limited range of situations. Practical knowledge is, however, an important
element of technology education.
Let me make a diversion into snooker (pool or billiards), a subject I have
studied in more depth elsewhere (McCormick 1999b). Snooker with balls rolling
around a table is a wonderful arena for scientists and mathematicians to work
in. It is almost frictionless, with almost perfect impacts, and it is two-dimensional,
and hence easy to analyse. For example, Figure 2 shows a classic interaction
of a two-dimensional collision. If ball A collides with ball B, the theory
is that B goes off in the line that joins the two centres (of A and B). Well,
if you play snooker, you'll know it doesn't happen in this way. What actually
happens is that, at the point of impact A is squeezed in the table surface
(the nap) and ball B is thrown off the line that the simple geometry predicts.
If you ever watch these games, you'll see the ball very seldom goes in square
to a pocket. But mathematicians will deal with these collisions in this general
abstract way ignoring the details of the context (the nap). Most mathematicians
will also assume there is no friction between the ball surfaces when they
collide. A second example is shown in Figure 3. If you hit the white ball
to collide with the red ball, such that it goes along the line across the
snooker table, then in theory, it should come straight back because there
is no friction between the balls. In fact, what happens is it goes off at
an angle, as shown in Figure 3, because there is a spin imparted to the red
ball.
Figure 2: a two-dimensional collision
Figure 3: a collison showing the effect of transmitted
spin on a snooker table
Now, I use those two simple examples because there is quite a lot of complicated
mathematics (including statistics) and science that tries to explain the behaviour
of balls all over the table. Snooker players can not only hit balls accurately,
which is one skill, but they can predict about 3, 4 or 5 moves ahead predicting
where the ball will go after each successive collision. Most physicists will
give up calculating after about two moves, because it's so complicated that
they cannot do it or the problem is not worth the increased complexity. So,
there comes a point where practical knowledge is absolutely invaluable and
is sometimes as good as, or better than, the theoretical knowledge. That is
an important point. In work on artificial intelligence, for example in chemical
plant control, practical knowledge is important, because it is impossible
to program the science equations for the operation of the plant. It's just
too complicated. Dillon (1994) has given an overview of this approach to knowledge
and shows how it is qualitative in nature, a point I will take up later.
An illustration of this from snooker was given by a statistician who decided
that he would produce a statistical model that could predict whether a particular
shot was worth attempting (i.e., the potential score of a ball going into
a pocket depending on what the conditions were). He built a model, which snooker
players could use to predict accurately what shots they should try when faced
with choices. They could use a calculator to determine the probability of
success for given balls. The first assumption he made was that he did not
consider what happened to the white ball after it hit the red ball. Not even
a novice player would do this! That is a perfect example of ignoring the user
of the mathematics.
In using such practical situations the scientist and the mathematician are
actually more concerned with the science or mathematics than with billiards,
snooker or pool. They want to just use the situation to illustrate their mathematics,
not to deal with the real context and solve a practical problem. (In fact
there was a very good 19th century mathematician who did analyse
billiards and tested a lot of his theories on the table; Hemming, 1899.)
Research into the effect of context
There is evidence from the UK Assessment of Performance Unit (APU) obtained
when children were questioned about parallel lines. Given the lines in Figure
4, students were asked the question “Which of these lines is not parallel?”
When 11-year-olds were questioned, 56 percent of them got it right, and 82
percent of 15 year olds were correct (APU undated). This is a question where
the context of parallel lines is varied a small amount: some of the lines
are longer than others, some of them are at one angle, and some another, some
of them are offset. Often students are presented with parallel lines in mathematics
as being horizontal, of the same length and even with arrows on them to tell
them they are parallel.
Figure 4: a question for students to choose which
lines are not parallel
In this question there is only a slight change in context, and yet students
are unable to move into the context. The technological context is more complex,
in for example, orthographic projection (Figure 5), where there are many parallel
lines, perpendicular lines, lines of reflection. Imagine, then, students who
walk out of geometry class, where they can barely understand a slight shift
in context of the way parallel lines are presented, moving into a technology
class where no one tells them there might be parallel lines there, indeed
doesn't even talk about them. (Our research has shown how tools, such as T-squares
that automatically produce parallel lines, hide the geometry; Evens and McCormick,
1997.) Another set of questions tried by APU (1984a) in science considered
reflection. Students are presented with various reflections and the context
varied (Figure 6): “If these are bouncing balls, what do you expect to
happen and which of these is right?” If the context is changed to “light,”
rather than balls or if they are presented as abstract lines, then students
are much better in answering the question in the context of bouncing balls
than they are with abstractions, with the light context being in between.
So with the same question, their performance drops across different contexts.
Boys are much better than girls with the balls context, and the answer to
this difference is found in the nature of pastimes of boys (Figure 7); they
play snooker and billiards a lot more, so they know quite a bit about balls
bouncing off cushions and so on.
Figure 5: an orthographic projection
Figure 6: a question on reflection
Figure 7: student pastimes
Interestingly, students do better in answering questions about spinning balls
bouncing, which is a really complicated situation of the kind that happens
typically in snooker, or when you play with a ball against the wall. There
is no difference in the performance on such questions between boys and girls
because they both bounce balls out of the classroom. They are better at performing
in that spinning situation about reflection than they are in a simple one.
This shows they are using the every-day knowledge of what happens, and we
often don't capitalize on their understanding of situations. Rather we emphasise
that science is counter-intuitive, and we dispense then with the real world
that students inhabit.
It is very important that we realise that children's knowledge is learned in
context, and that they find it very difficult to move from one context to
another. That is my explanation for the film of the graduates from MIT who
cannot connect a battery and bulb with wires [shown at the conference]. It
is not an issue of understanding circuits per se, it is that they understand
circuits in a particular context. After all, they can do some really complicated
things back in the laboratories of MIT, but a context change can fool them.
We don't think much about the issue of moving from one context to another
and how we teach students to do this.
Another simple illustration of a change in context is shown in Figure 8, where
the top representation of a circuit would be typical of that shown in a science
laboratory. In a technology laboratory the bottom representation may be used.
This lower technology circuit comes from AC representation where the bottom
line should be zero voltage and the top one will be plus or minus. But this
notion of “rails” requires an understanding of potential difference
and of voltage. Many teachers use a “current” model and create complex
explanations of current moving down the circuit, rather than the potential
difference across the circuit. Children are supposed to transfer this notion
of circuits from one area (science) where the conceptions and the way it's
represented are quite different from that in the technology area. The effect
of shifting context seems to be a fundamental area for research.
Figure 8: two representations of a circuit found
in schools
Representing technology knowledge
The Benchmarks for Science Literacy represent concepts of science
that are important in technology, but not always in the way that is appropriate.
For example in the area of “properties and materials”:
The choice of materials for a job depends on their properties and
on how they interact with other materials. Similarly, the usefulness of some
manufactured parts of an object depends on how well they fit together with
the other parts. 8B(6-8)#1.
When a new material is made by combining two or more materials, it
has properties that are different from the original materials. For that reason,
a lot of different materials can be made from a small number of basic kinds
of materials. 4D(3-5)#4.
These are actually about chemistry, and about the theories of elements and
molecules, etc. For most technologists this level is not important, except
at the high end of technology, where the molecular structure might be important.
What most technologists care about is the properties of the materials rather
than molecular structure. Their principle concern is with the law that comes
out of how the material behaves, stiffness, strength, these kinds of things.
When a technologist is concerned about design constraints, the laws and the
concern about behaviour of materials is central. But over on the science side
the concern is with theories. How are these co-ordinated in students' minds?
What is the interrelationship for them?
Another example is found in the area of “heat”:
Heat energy in a material consists of the disordered motions of its
atoms or molecules. In any interactions of atoms and molecules, the statistical
odds are that they will end up with less order than they began—that is,
with the heat energy spread out more evenly. With huge numbers of atoms and
molecules, the greater disorder is almost certain. 4E(9-12)#2.
Linn and Muilenburg (1996) have written very interestingly about the use of
the kinetic theory model. It is a theory about the interaction at the molecular
level. Most students, when they are dealing with heat in school, need a heat
flow model. They need to be concerned about relationships of temperature change
across conductors or insulators, etc. This requires an empirical relationship.
But in science the focus is on a theoretical view of heat, the model of heat
flow. Linn and Muilenburg argue that it is not useful for everyday life. The
heat flow model is what most adolescents require: many engineers also use
that model, because it is often the most appropriate model. This gives us
a problem of modelling an environment through a theory that does not necessarily
relate to how we want to use the model in a technological sense.
The benchmarks deal with systems in the following way:
Almost all control systems have inputs, outputs, and feedback. The
essence of control is comparing information about what is happening to what
people want to happen and then making appropriate adjustments. This procedure
requires sensing information, processing it, and making changes. In almost
all modern machines, microprocessors serve as centers of performance control.
3B(6-8)#3.
As noted earlier, systems require abstract representations and these abstractions
of systems apply across all sorts of areas and practical contexts. It is an
extraordinarily difficult idea, because of one simple thing: how you characterize
the system depends on the purpose you're doing it for.
Consider the example of a flush cistern in a toilet; the ballcock that regulates
the level of the tank. If a variety of technologists are asked to draw a systems
diagram of a cistern, they will produce different diagrams. It is actually
very hard to draw such a systems diagram. But it is very easy to explain how
a cistern works, and it is obvious to students. If you try to use a systems
representation, it sometimes becomes more complicated than the practical system.
Systems approaches aim to reduce complexity, not increase it!
An example of a central heating system is shown in Figure 9. This is a system
that is referred to in Benchmarks, that children in grades 6-9
typically should know. This system is extremely complex with primary and secondary
inputs. Simple input, process, output systems are already complex enough at
theses grade levels. For example, 13 year olds working on input (make-or-break
contact), the process (transistor as switch) and the output (e.g., a buzzer).
When such pupils are asked “What's the input?” they say, “The
battery,” which is a secondary input. What may appear a simple system
is a level of abstraction that students may not understand; another area for
research, to explore across what range of situations it can be used, to see
how understanding of complexity might vary according to age. Everyone in technology
education thinks it is important to teach students about systems, that it
is a central concept, yet we understand little about how students come to
understand it.
Figure 9: a systems diagram of a central heating
system
Qualitative knowledge
Let me look at an alternative approach, which has to do with qualitative knowledge,
which I intimated earlier was important in technology. I want to start in
the classroom with a project where students are making a little woodpecker
moneybox (Figure 10).
Figure 10: a woodpecker moneybox mechanism (front
view)
Figure 11: a woodpecker moneybox mechanism (rear
view)
Money is put in at the top (right), the coin drops down and hits a horizontal
“lever” (Figure 11) and drops into the box. This lever then rocks
and it makes the bird peck on the other side (Figure 10); a simple mechanism.
Consider the science of this mechanism and the fact that 11-year-olds are
doing this project. When a coin drops through the coin slot and hits the lever
the latter then goes out of balance (Figure 12). The size of the coin, and
the distance it travels, will determine the momentum that it acquires. The
rate of change of momentum when it hits the lever exerts the force, and the
greater the distance and coin size will create a greater force. The “lever”
(or beam) is counter-weighted on the left end because, unlike most of the
beams in science, this beam has mass. There is also an offset pivot, and the
length of that beam (on the right hand side) will determine the amount it
swings when hit by the coin (it gives a mechanical advantage to the force
of the coin). The pendulum is part of the lever system which will make that
system swing, exerting a torque on the pivot. The bird (on the other side
of the board; see Figure 10), the pivot, and the beam are all fixed together.
It is also possible to consider that the bird itself will have a moment of
inertia as it rocks back and forward, depending on its shape.
Figure 12: a diagram of the woodpecker mechanism
Would it be possible to write the equations that would describe how the system
works? If it was possible, it would be extremely complex, and hence unlikely
to be successful and certainly not worth the effort. To create a
system like that requires a piece of design thinking. The science will tell
you about changing the size of the coin, about making the beam longer, etc.
It probably will tell you to ignore the pendulum size, because it would not
be possible to obtain the frequency resembling that of the pecking of a woodpecker.
If engineers were trying to get this to work they would not use quantitative
science but what might appear to be trial and error. In fact they would use
qualitative thinking. They would know that if they increased the heights of
the coin drop or made the beam slightly longer, and so on, they could eventually
obtain sufficient movement in the bird for all coins. It is my view that we
should start to teach this kind of thinking in technology education. It is
not the use of quantitative ideas of science, but their use in qualitative
terms. Although there may be successive iterations, it is necessary to have
some concepts about what is to be changed. Otherwise, it does become trial
and error, and it would be a random event if a successful mechanism is produced.
A classroom example of qualitative thinking
Now, what I want to show you are some excerpts of classrooms where the students
are working on this woodpecker mechanism and where I think qualitative knowledge
is being used. There are two aspects of this qualitative knowledge. One is
that it is qualitative, it isn't quantitative; thus I talked earlier of making
“the beam slightly longer.” The other is that it involves
device knowledge (Gott, 1988). We often talk about machines and the like not
in the abstract, but in the particulars of the machine or device, and it is
very evident in the classroom incident I will show, where the teachers and
the students are using these two features of qualitative knowledge.
In the first of the classroom extracts I will deal with, the students are just
starting the project, and about to try to model the mechanism in cardboard.
This modelling will allow them to get some ideas of dimensions and how the
mechanism will work. The teacher has given them a model of previous students
who used it for a rocking boat. Their mechanism will, however, have a completely
different feel about it, so they have to redesign it to work in their context.
The teacher is at the front of the class illustrating some of the ideas of
the mechanism.
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| Figure 13: Teacher illustrating rocking
mechanism with his hand. |
| Click on the picture to view the video (requires
the latest version of RealPlayer). |
| |
During this classroom incident, first we see the teacher using his hand to
illustrate the rocking movement of the mechanism and woodpecker (Figure 13).
Second he uses device-related language and “non-science” language:
T: Transmit movement [from lever to bird] to the front .
T: Lock pivot to lever to make sure it runs . [with the lever]
There is probably no other way the ideas involved could be explained for students
to understand motion and how the mechanism operates. The phrase “to make
sure it runs . [with a lever]” is device knowledge expressed in terms
of the elements of the mechanism.
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| Figure 14: Students working on the project
after their teacher's instructions. |
| Click on the picture to view the video (requires the latest
version of RealPlayer). |
|
About 20 minutes after the above incident, the students are back at their desk,
and they start to fiddle with their cardboard model, trying to work out what
will happen, and also talk about the model they have been given.
An extract from what they say illustrates the students' attempts at qualitative
reasoning:
P: It'll be in that position first of all, then it's going to go knock,
knock.
[As the bird is rocked to peck the tree.]
P: It depends how much money they put in, because if it's a 50p, its
going to do “dong” like that, so it's going to go really far.
[As the lever is pushed down as far as it will go.]
P: Then if it's a 5p . it will still move only a bit. [Again referring
to the lever.]
In saying “it will go knock, knock,” (and moving the bird at the
same time) she is thinking about the device. In referring to the effect of
the coins, she says the big coin (50 pence) is going to go “dong,”
so that's the device thinking. The reference to going “to go really far”
shows a qualitative idea, contrasting with the small coin (5 pence like a
dime), then it will only move “a little bit.” So they are starting
to reason about how the device works.
Two important points emerge for me in looking at such incidents. First we should
be developing students' thinking about machines and other technological devices,
and second, such thinking is often hidden, because teachers, in the business
of classrooms often cannot monitor that kind of interaction. But it is important
that they talk about it. I think, therefore, that qualitative knowledge is
a really productive area of research work in technology education. In carrying
out research into both conceptual and procedural knowledge, there are, as
I have illustrated, many complexities with which we have to deal.
Research and curriculum change
We often think about research methods in relation to methodological approaches,
qualitative, quantitative, and critical as discussed by Householder and Newberry
at this conference.
However, we don't think enough about how it will affect what teachers do in
classrooms, and how we understand those classrooms. In the end we want to
help teachers, and then want them to be able to help the children they work
with. Jim Rutherford's depiction of that process was that we should be very
clear about the learning outcomes that we want to achieve and then we should
set up the classroom so we achieve them and go in and research and show that
it is effective. Now, that is one form of necessary research, but it is only
one form.
In the UK we adopted a similar approach to Rutherford's model. The Assessment
of Performance Unit Design and Technology Project (APU, 1991) specified tasks
covering design processes that children had to do to test the achievement
of students.
They collected some classroom and context data, but the focus was on student
outcomes. To take this approach it is necessary to pre-specify what the knowledge
and processes are in technology. If, however, the research and argument I
have put forward in this paper are valid, this pre-specification is problematic.
For example, they did not test the conceptual knowledge used in any design
and make task.
A second view is the Research and Development (R & D) approach. Ann Brown
as a learning theorist takes this approach (Brown, 1992). She has a clear
idea of the learning model (e.g., a community of learners) and how it should
be implemented in the classroom. Such an approach requires changing the classroom
to fit the theory, and then investigating in the classroom to see how the
theory works.
A third approach is through naturalistic studies of the classroom. The classroom
is investigated to see what is happening, to understand the situations that
exist there at the moment. For this approach the issues are “what informs
the investigation” (i.e., what directs the data collected), and “what
is done next after the research is carried out.” This latter issue is
one for all approaches. The R & D approach has a problem with disseminating
the practice when the theorists are not available and the teachers don't understand
the theories: how do we transfer the teaching and learning ideas to other
classrooms? We know that this transformation is difficult to “engineer.”
Thus we need to be clear about how we move from the R & D to the dissemination
around the education system. It is also necessary to be clear about the conditions
under which it is successful, another layer of complexity. What works in one
classroom may not work in others.
The problem with naturalistic studies equally occurs after the research; having
reached the kinds of conclusions I discussed earlier, “What are you going
to do next?” This requires intervention in the classroom through a development
with teachers and working with them. That is the stage my colleagues and I
have reached on the Open University. We want to go into classrooms and intervene
to try out some strategies working with teachers to explore effective approaches.
But I think it is premature to go in and intervene if you don't know much
about what is currently happening in classrooms. This should make us wary
of taking prescriptions: “This is how you should change the classrooms.”
Whatever approach we take to research in technology education, it is important
to have a view of how classrooms will change as a result of it. Models of
curriculum change do not favour the simple dissemination of findings through
articles and the like. (See McCormick, 1992, for a parallel argument in the
field of information technology.)
A Research Agenda
Let me end with a list of topics and issues for research in technology education
that arise from my paper:
- clarify views of procedural knowledge, such as problem solving and
design;
- investigate how students use procedural knowledge and both scientific
and technological conceptual knowledge in technology classrooms;
- investigate the extent to which these procedures and concepts are different
in different areas of technology (i.e., what is the role of context?);
- investigate the kinds of uses of qualitative knowledge that occur in the
classroom and how it can be encouraged and developed by teachers and students;
and
- keep a clear focus on what happens in classrooms and move from “what
is” to “what ought to be” the situation.
References
Alavi, C. (Ed.) (1995). Problem-based Learning in a Health Sciences Curriculum.
London, Routledge.
American Association for the Advancement of Science (1993). Benchmarks
for Science Literacy. New York: Oxford.
Assessment of Performance Unit [APU] (undated). A Review of monitoring
in Mathematics 1978-82 Part 1 London, APU.
Assessment of Performance Unit [APU] (1991). The Assessment of Performance
in Design and Technology. London, School Examinations and Assessment
Council.
Assessment of Performance Unit [APU] (1984a). Science in Schools Age
13: Report Number 1. London, Department of Education and Science, Department
of Education for Northern Ireland and Welsh Office.
Assessment of Performance Unit [APU] (1984b). Science in Schools Age
13: Report Number 2. London, Department of Education and Science, Department
of Education for Northern Ireland and Welsh Office.
Dillon, C. (1994). “Qualitative reasoning about physical systems - an
overview,” Studies in Science Education, 23,
pp. 39-57.
Driver, R., Leach, J., Millar, R. and Scott, P. (1996). Young People's
Images of Science. Buckingham, Open University Press.
Evens, H. and McCormick, R. (1997). Mathematics by Design: An Investigation
into Key Stage 3. Report to the Design Council. Milton Keynes, School
of Education, Open University.
Garrett, R.M. (1988). “Problem-solving in science education.” Studies
in Science Education, 13, pp. 70-95.
Gott, S. H. (1988). “Apprenticeship instruction for real-world tasks:
the coordination of procedures, mental models and strategies.” In E.
Z. Rothkopf (ed.) Review of Research in Education 15 1988-89
(pp. 97-169). Washington DC, American Educational Research Association.
Greeno, J. G. (1998). “On claims that answer the wrong question.”
Educational Researcher, 26 (1), pp. 5-17.
Hemming, G. W. (1899). Billiards Mathematically Treated. London,
MacMillan and Co. Ltd.
Lave, J. (1988). Cognition in practice: mind, mathematics and culture
in everyday life. Cambridge, Cambridge University Press.
Lave, J., Smith, S. and Butler, M. (1988). “Problem solving as an everyday
practice.” In J. Lave, J.E. Green, A. Schoenfeld, S. Smith and M. Butler
(eds.) Learning Mathematical Problem Solving (pp. 1-21) Palo
Alto, CA, Institute for Research on Learning.
Lewis, T. (1993). “Valid knowledge and the problem of the practical arts
curricula.” Curriculum Inquiry, 23 (2), pp.
175-202.
Linn, M. C. and Muilenburg, L. (1996). “Creating lifelong science learners:
what models form a firm foundation?” Educational Researcher,
25 (5), pp. 18-24.
McCormick, R. (1992). “Curriculum development and new information technology.”
Journal of Information Technology for Teacher Education, 1
(1), pp. 24-41.
McCormick, R. (1999a). “Capability lost and found?” Journal
of Design and Technology Education, 4 (1), pp. 5-14.
McCormick, R. (1999b). “Practical knowledge: a view from the snooker table.”
In McCormick and Paechter (pp. 112-135).
McCormick, R. and Davidson, M. (1996). “Problem solving and the tyranny
of product outcomes.” Journal of Design and Technology Education,
1 (3), pp. 230-241.
McCormick, R. and Paechter, C. (Eds.) (1999). Learning and Knowledge.
London, Paul Chapman.
Millar, R. (1996) “In pursuit of authenticity.” Studies in
Science Education, 27, pp. 149-165.
Reed, S. K., Ernst, G. W. and Banerji, R. (1974) “The role of analogy
in transfer between similar problem states,” Cognitive Psychology,
6, pp. 436-450.
Schoenfeld, A. H. (1985) Mathematical Problem Solving. Orlando,
Academic Press.
Schoenfeld, A. (1996) “In fostering communities of inquiry, must it matter
that the teacher knows the answer?” For the Learning of Mathematics,
14 (1), pp. 44-55.
