problem, planning, and the kind of
diagnostic thinking involved in debugging
• abilities of formal reasoning and
representation eg thinking of all
possible combinations, and constructing
mathematical models
• cognitive styles eg precision, and
reflectivity over impulsivity
• enthusiasms and tolerances eg
persistence, and enthusiasm for
meaningful academic engagement.
The logic based programming language
LOGO has been widely used in schools and
widely evaluated. Results seem equivocal.
Simon (1987) surveys a number of
evaluations to conclude that Papert’s
hopes that using LOGO would lead to the
emergence of general problem solving
skills were ‘pipe dreams’ and ‘technoromanticism’.
Underwood and
Underwood’s survey of evaluation results
is much more positive (1990). Liao and
Bright (1991, quoted in Kirkwood, 1998)
conducted a meta-analysis of sixty-five
classroom based studies into the
relationship between computer
programming and general cognitive skills,
using quantitative comparisons between
treatment and control groups. Their main
conclusion is that programming can
provide a mildly effective approach to
developing students’ cognitive skills in a
classroom setting.
Erik De Corte (1990) demonstrated that
using LOGO alone does not produce
transfer but that LOGO can be a useful
resource in teaching approaches that lead
to transfer. The same point is made by
Clements and Gullo (1984). Hughes (1990)
sums up a balanced survey of the evidence
with the following conclusions:
Exposure to LOGO by itself does not
usually lead to cognitive gains; that such
gains are more likely to be found with
structured teaching; and that the Logo
environment promotes social interaction
amongst peers. (1990. p133)
3.3.2 VISUALISATIONS AND SIMULATIONS
Some kinds of powerful thinking work
through a series of ‘leap-frogging’
manoeuvres. Scientific thinking, for
example, relies on turning processes into
nouns so that they can be objectified and
thought about more easily (Halliday and
Martin, 1993). Representations of every
kind allow us to objectify our thoughts so
that we can reflect upon them. Writing,
graphs, tables and specialist notations
such as mathematics are already cognitive
tools allowing thought to ‘leap-frog’ to a
higher level of understanding. Computers
can take this further through allowing the
direct manipulation of representations.
Paul Cobb argues that providing computer
tools to help students manipulate complex
data-sets enables them to understand
statistical arguments and therefore equips
them to be able to participate in many
public debates (2002). Sharon Ainsworth
(1997) similarly argues for the value of
multiple representations for supporting the
development of understanding. Jonassen
makes the same kind of case for the use
of ‘visualisation tools’ that allow learners
to visualise scientific ideas (Jonassen,
2000). Many simulations of systems play
a similar role. They allow users to
manipulate dynamic representations of
real-world systems.
The literature about using the computer as a tool sometimes blurs the distinction |
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between using external cognitive tools, eg computers, and developing internal cognitive tools, eg thinking skills. These are not the same things. I use a calculator
when I change currency, perhaps it is a ‘cognitive tool’, but that does not mean that I learn how to do long division – I can do long division only because a teacher taught me a pencil and paper technique that I still remember and use. When asked to do long division in the absence of paper and pencil I find myself visualising the
numbers and imagining the procedure. I do not find it useful to visualise a calculator and imagine pressing the
buttons. If I were a hospital manager funding the development of a computer system to diagnose patients I would want
the best system possible, a system that did not need doctors. If, on the other hand, I wanted to help teach medical students the complex skills of diagnosis, involving
reasoning, evaluation, information
processing and so on, I would want a very
different system. I would want the careful
‘scaffolding’ of skills with the rich
presentation of case studies and lots of
interactive feedback. Using a computer as
a ‘mindtool’ to solve a problem and
learning how to solve it for oneself are
therefore different things.
Jonassen claims that the best way to learn
about an area is to build a computer
system to model the area. From my
experience of building computer systems I
suspect that in reality far more time would
be spent learning how to get the computer
tools you were using to do exactly what you
wanted than in learning about the area.
This is not a very efficient approach to
teaching and learning. The evidence
suggests that learning skills that can
transfer requires someone, usually a
teacher, to plan activities and experiences
that help to make it happen. There is no
good evidence that thinking skills will
simply ‘rub off’ as a ‘cognitive residue’
(Salomon 1991 quoted in Jonassen, 2000),
from using new technology.
The point about ‘mindtools’, as Jonassen
calls them, is that they do not work on
their own. To learn from such tools
students need to be primed as to what to
look for, they need opportunities to
articulate what they find and they need
feedback on their discoveries. One way
of achieving this is to use mindtools as a
resource for small group collaborative
learning within a teaching and learning
framework (Laurillard, 1993 p137: Wegerif, 2002).
3.3.3 CONCEPT MAPPING
Concept maps or ‘semantic networks’ are
spatial representations of the concepts and
their interrelationships that are intended to
represent the knowledge structures that
humans store in their minds (Jonassen,
2000). While concept maps do not require
computers, computer-based conceptmapping
software, such as ‘SemNet’,
‘Learning Tool’, ‘Inspiration’, ‘Mind
Mapper’, and many others, enable the
production of concept maps. Great claims
are made for the use of concept-mapping
as a tool to support critical thinking and
reflection on the organisation of knowledge
in a subject area while also learning about
the area (Buzan and Buzan, 2000:
Jonassen, 2000).
The purpose of semantic networks is to
represent the structure of knowledge that
someone has constructed. So, creating
semantic networks requires learners to
analyze the structural relationships among
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the point about
‘mindtools’, as
Jonassen calls
them, is that
they do not work
on their own. To
learn from such
tools students
need to be
primed as to
what to look for,
they need
opportunities to
articulate what
they find and
they need
feedback on their
discoveries |
