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This is a WWW document by Steve Draper, installed at http://www.psy.gla.ac.uk/~steve/interactivity.html.
How to refer to it.
Content and Interactivity
Stephen W. Draper
Department of Psychology
University of Glasgow
Glasgow G12 8QQ
U.K.
email: steve@psy.gla.ac.uk
This was written as a response to discussion on the
ITFORUM
email list around 29 July 1996.
(Link to list of ITFORUM participants.)
The
original paper by Rod Sims, and
discussion of it was some months earlier.
A later
message by Barney Dalgarno
triggered off a discussion that led to my comments. Another
web version of this note with better cross links is on the ITFORUM site.
Introduction
For me at least the recent exchange triggered by Barney Dalgarno on interactivity is
very stimulating, as the conflicting views show me that I believe a number of
things that seem contradictory. This long piece is my personal synthesis of
the issues (in other words, stand ready to use the delete function as writing
this was good for my learning, but reading it may not be useful to you).
The way I read it, one group (Ian Hart) see it as just another instantiation
of a contrast between medium and content, which they see as already resolved
by Clark (1983) in favour of content determining learning and medium being
irrelevant. A lone, but needed, voice said what about situation or context.
Yet Barney, like Rod Sims, obviously thinks that a classification by types of
interaction would have predictive power about learning outcomes.
Interaction types are not quite the same as the medium vs. content issue.
Learner actions are not just "medium", and learning depends not just on content
but on the actions taken in relation to the content. What we have here, I
think, is a long spectrum of hypotheses from the crudest versions of media
determinism to classifications of learner actions. The crudest renderings of
the issue are about sensory modality: is it better to receive material
through the eyeball or the eardrum; then through the "medium" e.g. books vs.
TV vs. computers; then the genre e.g. pictures vs. words. Then, where this
discussion started, through different interactive modes that the computer can
offer a learner. Behind this spectrum is another one from regarding
instructional material as something that is done to the student to regarding
the issue as about what actions the learner takes. Interactive modes are not
quite the same as "media" because they more clearly concern learner actions
which, unlike physical definitions of media, really do affect learning
outcomes. Clark argued against the relevance of media as an independent
causal factor in learning. Kozma's (1991) rebuttal of this as a general
proposition amounts to the following: what matters to learning is learner
actions, learner actions depend on particular instructional situations, and
these cannot be separated from the properties of media that define the range
of possibilities. So on Kozma's view, we can't dismiss the media factor just
because some of its forms are unproductive; and the issue of interactive
modes is at least more promising than the worst of these because it relates to
learner activity. My view, then, is that the factors that really do determine
learning outcomes are learner actions. The questions are: what kind of
learner actions are the important types i.e. the main determinants of
learning, and are computer-learner interaction modes of this type? I shall
begin by arguing that classifications by interaction type usually are
irrelevant to learning outcomes, but nevertheless this shouldn't stop us
seeking a useful classification of learner (inter)action types.
CATEGORIES OF INTERACTIVITY
OVERT BEHAVIOURAL TRANSACTIONS
I have seen quite a few categorisations of learner-computer interaction by
type, but I believe they are all of little use in education. The crudest are
overtly machine-centered, that is they categorise interaction with humans in
terms of a machine's technical characteristics. In the end, I think this
applies even to Barney's and to Rod Sim's categories in his IT forum paper.
The basic reason they are attractive both to computer scientists and to
psychologists who like to measure overt behaviour is just that: that overt
physical actions by humans or machines are unproblematic to observe and
record. The trouble is that learning does not depend upon these things: you
get learning with and without any of the interactive types mentioned.
One example that shows this concerns TV. TV is completely non-interactive
from a physical viewpoint: the viewer has no control over anything except
perhaps viewing distance and sound volume. Yet occasionally it can support an
intense learning episode. Some years ago there was a series on UK TV by Bryan
Magee on "The great philosophers". Each week there would just be two men
talking. By all the normal standards of TV production this was the least
engaging TV ever, with the pattern on the wallpaper behind them the only thing
of visual interest (actually I still remember that); but it had me on the edge
of my seat straining in concentration, as it was pitched just at the edge of
my understanding (I knew very little philosophy). This was one of the most
intensely engaging TV experiences I have had, and ranks with my best learning
experiences too, but would score zero on all these scales of interactivity and
should probably not be used in any general way to advocate two talking heads
as a general approach to good teaching and learning. It was interactive in
the way that mattered (between my prior understanding and the ideas being
discussed) but that interaction was completely invisible on physical and
behavioural criteria. Although Barney's and Rod's schemes are much more
sophisticated than the crudest in this area, I don't think they can escape
this fundamental problem: observable interaction is just not the interaction
that matters.
A second case to ponder here is how the very same material (and hence its
interaction modes) can have different effects on the same person on different
occasions. A really great novel or film, but still more a good paper or
textbook, can have a markedly different effect on you the second time you read
it than the first. This is a fairly common experience, and again shows that
observable interaction does not determine learning.
A third case to consider is that posed by the literature on deep and shallow
learning (Marton et al.; 1984). Here the same material has different effects
on different learners because they interact with it differently, but again the
interaction is not directly observable. It also holds a disturbing lesson for
those who worry about motivation as a factor. On my reading, at least, it seems
that students who want to pass tests say they goal is to learn, and these are
the ones who do well on many tests but have short, shallow retention. Those
who end up with long retention are those who were not trying to learn, but
just to understand. In any case, the different learning outcomes depend upon
different learner cognitive actions, but not on easily observable ones, and
not on the interactive modes overtly afforded by the materials.
Before leaving this topic, it should be said that such technical analyses of
available interaction modes are not without their own interest if done
properly. Consider an ordinary textbook. These normally offer at least five
different kinds of interaction: they may be read sequentially (beginning to
end), they have a hierarchical structure (e.g. part, chapter, section), they
have an index for accessing by content, and they have page numbers which are
routinely used by instructors assigning reading. In addition learners can and
do make marks on them: highlighting, marginal notes etc. Very, very little
software offers all 5 of these modes of interaction: computers have not in the
main yet caught up with print as an interactive medium. And it is not so much
the separate properties of each of these modes that matters, but that one
product offers all these alternatives, so that one learner has multiple ways
of interacting with the same material. Again, classifications of interaction
types usually direct us away from this plurality and flexibility and in so
doing are probably directing us away from a property crucial to supporting
learning.
As a transitional example, consider someone studying a map, diagram, or graph
that is for them a rich source. Simple classifications say that such static
printed representations are non-interactive, but that is wholly at odds with
the subjective experience. Eye movements might seem to give the dedicated
observer a handle, but this is misleading: firstly because what matters is
attention, and this can move over an after-image independent of eye movements,
and secondly because what really matters is the structures implicit in the
printed representation (e.g. the relationship between two peaks on a graph,
the slope of ground implied by countour lines). The interactions that matter
are cognitive, not observable.
DEPTH OF PROCESSING
As we all know really (don't we?) when not distracted by technophoria, what
matters for learning is depth of processing (not the number of mouse clicks,
or the number of hyperlinks followed, or whether it is a simulation or a
computer tutorial). So if we don't mind abandoning checklists of technical
features and directly observable behaviour, then we could have a
classification on internal interaction: by what levels of interaction are
engaged. I think this has a real chance of predicting learning outcomes. It
would begin with Craik & Lockhart (1972) levels e.g. syntactic vs. semantic
processing, and continue with the shallow vs. deep learning distinctions
(Marton et al.; 1984).
TYPES OF PROCESSING
Actually there is an interesting alternative theory, and hence a rival
possible scheme for classifying interaction types. Perhaps what matters for
promoting learning is the number of varieties of processing a learner does,
rather than just "depth". What would be significant, then, about interaction
and learner actions in general would be their requirement to use knowledge in
a different way. In the simplest example, understanding what a teacher says
requires one kind of processing (following links from words to meanings),
re-expressing what they said requires another (creating links from meanings to
words). This theory has at least two justifications. Firstly, many models of
memory would predict that multiple different links to an item will lead to
longer retention. Secondly, most definitions of "understanding" boil down to
the idea that the more different ways you can access and use an item, the
greater (or "deeper") your understanding (or "transfer").
EXTERNAL ACTIVITIES LIKELY TO PROMOTE ...
Whether you go for depth of processing or variety of types of processing, both
the above are kinds of hidden mental interaction between a learner and
knowledge. There is reason to believe that they predict learning directly.
They are the classifications of cognitive interaction that we want, but do not
correspond to what is easily observable (the sensori-motor interactions).
However there may be an intermediate, bridging class of interaction: types of
interaction that demonstrably promote learning, at least statistically
speaking. For instance activities like reading a text, writing an essay,
doing an exercise might be instances in such a classification. Eric Smith's
and Andrew Fluck's very different lists might also both be seen as attempts at
such intermediate classifications. For me they are variations on the notion
of mathemagenic activity.
MATHEMAGENIC ACTIVITIES
"Mathemagenic activity" is a term coined by Rothkopf (1970) and means an
activity that gives birth to learning. In his words "You can lead a horse to
water but only the water that gets into his stomach is what he drinks". In
fact a teacher is in an even worse position: not only can a teacher not cause
learning directly, they cannot even perceive it happening directly unlike
horse minders who can at least see and hear whether a horse drinks and how
much. This learner autonomy and the indirectness of teacher power is of
course an aspect of constructivism. It is linked here with the idea that
learners' actions have a big effect on learning. The impulse behind
categorisations of types of CAL, types of learner-computer interaction, or
indeed types of educational intervention is that it would be a big help to
teachers if types of learner activity could be associated with types of
learning. The notion of mathemagenic activity and attempts to classify their
types is the essence of this approach to understanding teaching and learning.
Laurillard (1993 p.103) takes up this notion and proposes a model in which
there are 12 mathemagenic activities. These apply to all subject areas. The
basic approach is that no software to date covers all 12 activities, so any
complete approach will combine software (multimedia, etc.) with other
activities. This model is not without its problems: to explain how learning
occurs without overt support for and observation of some activities, I have to
hypothesise that these may be internalised. This leads to further predictions
that have not yet been properly tested e.g. that learners will report these
internalised activities when interviewed, and that study skill training would
equip learners to perform such activities internally without further support.
Nevertheless, I find this model to have real predictive power. That is,
rather than studying the effects of media or of interactive modes on learning,
I vote for studying these mathemagenic activities as primary factors
determining learning.
AN EXAMPLE: SIMULATION
As an illustration, consider whether simulations are good for learning.
Inspired by how revealing a simulation is to them, some teachers implement one
and hand it to students by itself. No learning occurs for almost all students
(the blank screen phenomenon). So then the teacher provides a worksheet, as
for many labs. Students go through the worksheet, but learn rather little,
again as in labs. However the situation is actually worse, because in labs at
least students will be learning what materials look like and how to handle
apparatus, which is one set of objectives (corresponding to activities 6-9 in
the model), whereas in the simulation they will be learning how to operate the
software not the real thing. So the third step is to go for the best (but
still not very common) practice for labs, and put on "pre-labs" that get
students to activate the theoretical concepts relevant to the simulation or
lab in advance, so as to maximise the chance of their making connections
between concepts and practice (activities 10, 11). It would not be
technically difficult to deliver worksheets and pre-labs in software as well,
but the point here is that this model suggests that learning outcomes depend
on all these aspects being delivered, whatever the delivery medium of each.
It also, unlike Clark's delivery truck metaphor, says what needs to be
delivered and allows us to analyse a situation to identify the missing
elements; and suggests that these elements are not "content" nor "media" nor
"interactive modes" but activities.
NICHE BASED SUCCESS
I am also finding that this model illuminates why some bits of courseware are
merely OK while others are big successes. For instance CAL projects that are
basically conceived of as whole courses, when tested, generally generate
results that Clark would expect: about as good as non-CAL delivery, plus
sometimes a bit of positive halo effect. In contrast the big successes seem
to be when the designer identified a defect in the previous non-CAL delivery
corresponding to one of the Laurillard activities, and then thinks of a way of
exploiting technology to fill the gap. One example (McAteer et al., 1996)
here at Glasgow concerned teaching Portuguese. Everyone nowadays thinks that
the way to learn a second language is through conversation practice, but it is
too expensive to provide each student with many hours of a Portuguese speaker.
So a piece of courseware was built to provide a conversational context (a
photo of a South American street market, then some canned utterances) and then
invite the student to utter a reply (activity 7) which is recorded, and so on.
This has produced a dramatic improvement in student learning. In a way it
seems obvious: but most courseware actually does not have a clear pedagogic
analysis of a need that it is designed to fill, and does not produce big
improvements in learning. Success in CAL seems to me to depend upon the
specific niche it addresses: clever fits score big, the rest produce the same
patchy performance that the non-technological solution did. The Laurillard
model helps to analyse what the needs are. Note too that the use of technology
here seems compatible with both Clark and Kozma's views. It doesn't do
anything that couldn't be done better by humans, but in practice in this
particular situation, the learners get many hours practice per week, instead
of just 2 in a conversation class. You can see how one set of people might
try to analyse this as due to the multimedia properties of the technology,
while another might analyse it as due to a motivational effect (after all, why
didn't these students just practice on each other or with simple audio tape
materials?), and a third as a social effect due to the successful scaffolding
by the courseware not achievable by other students (insufficient language
proficiency) or simple tape materials (not a convincing conversation). What I
like about the Laurillard model is that it lets me spot the missing or weakly
supported activities in any particular situation while remaining neutral about
whether to redress them by technology. Success then follows when a designer
sees how to use technology to cover a gap that human delivery is failing in
practice to cover. But I don't see how classifications of interaction types
have helped design successful CAL so far.
REFERENCES
Clark, R.E. (1983). "Reconsidering research on learning from media" Review
of Educational Research, vol.53 no.4 pp.445-459.
Craik, F.I.M. & Lockhart, R.S. (1972) "Levels of processing: a framework for
memory research" Journal of Verbal learning and verbal behavior vol.11
pp.671-684
Kozma, R.B. (1991). "Learning with media" Review of Educational Research,
vol.61, no.2, pp.179-211.
Laurillard, D. (1993) Rethinking university teaching: A framework for the
effective use of educational technology (Routledge: London).
McAteer,E., harland,M. & Sclater,N. (1996) "De Tudo Um Pouco Ñ a little
bit of everything" Journal of Active Learning vol.3
Marton,F., D.Hounsell & N.Entwistle (1984) (eds.) The experience of learning
(Edinburgh: Scottish academic press)
Rothkopf,E.Z. (1970) "The concept of mathemagenic activities" Review of
educ. research vol.40 pp.325-336