education-cybernetics-ch2

Chapter 2
Dances with Valves
When biologists make
observations they are making
descriptions
Gregory Bateson
2.1 Conversation as Coordination
Everything can be subject to multiple descriptions. Conversation
is the process of coordinating the differences between descriptions.
The most fundamental aspects of scientific knowledge - whether it
is in biology, physics, chemistry or philosophy - there is always a
need to coordinate different distinctions. The process of conversation
makes possible the coherent social structures of education, which exist
despite and because of the variety of descriptions which are made it.
Conversations occur between teachers and learners, between learners
and their peers, between parents and children, between scientists,
and between colleagues in an educational institution. To understand
cybernetics is to understand the primacy of conversation above any
supposed objectivity or universality of any particular description.
A conversation is a kind of dance. The word conversation comes
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2 Dances with Valves
from the Latin, con-versare. It means, “to turn together”. Two people
can dance and intuitively understand the moves of the other. Equally,
dancing can be awkward, or occasionally break down. Conversations
are much like this. The distinctions between the things that happen
in the ‘successful’ dance and what happens in the ‘awkward’ dance
are subtle. Many signals about the wishes of each person are communicated
through movements of the legs, hands, eyes, the coordination
with the music, and the sense of physical contact. Each of these
presents sets of distinctions, and many different distinctions co-exist
at any time. If the dance partner is unable to read these, or react
appropriately, then of course things will break down. But if things
do break down, then the more expert dancer is likely to simplify the
moves, to reduce the number of distinctions they make so that their
partner might have a chance to response to something less complex.
Both the complexity of multiple distinctions in dancing, and the
shifting down from a complex dance to a simple one are aspects of
conversation. What happens in the ‘shifting down’ is a recalibration
of the moves that the dancer makes. Learning conversations are
precisely like this. Few learning conversations begin with expert coordination
between teacher and learner. There are always processes
of recalibration as the teacher recognises the need to simply their own
complexity so as to maintain an effective dialogue with the learner.
So we begin with three important concepts:
• In conversation, we exchange multiple descriptions of the world
which are often presented simultaneously
• Conversation is a process of coordination of the differences between
different descriptions
• Conversation involves a process of recalibration as one or the
other party changes the level of complexity in their moves (or
utterances).
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2.2 Technology and Conversation
2.2 Technology and Conversation
The capability of the World-Wide Web to coordinate conversations
online, bypassing the constraints of time and co-location, was one
of the most exciting transformative ideas for educational technologists.
With what seemed to be the escape from the bounds of the
classroom and timetable, new possibilities for organising education
present themselves. If conversations could be coordinated technologically,
could, for example, the institutionally-framed constraints of
curriculum similarly be transcended? Might there be new ways of
dealing with assessment and certification or the social status that is
accorded by the education system?
In the years that have passed since the advent of online forums,
Wikis, Virtual Learning Environments and social media, many of
these questions are still being asked. Yet, as many writers on educational
technology acknowledge, the transformation hasn’t been quite
what many hoped for. Diana Laurillard, for example, comments that:
“The promise of learning technologies is that they appear
to provide what the theorists are calling for. Because
they are interactive, communicative, user-controlled technologies,
they fit well with the requirement for socialconstructisit,
active learning. They have had little critique
from educational design theorists. On the other
hand, the empirical work on what is actually happening
in education now that technology is widespread has shown
that the reality falls far short of the promise.” (Laurillard
2012)
Radical champions of “Personal Learning Environments” - including
me (Johnson 2016; Wilson et al. 2009) once argued that the technology
could challenge the institutional structures of education, and
that with rising costs and student fees, environmental threats and
increasing demands for flexibility, students would exploit learning resources
freely available on the web, and desert the campus. That this
hasn’t happened raises questions about the theories about conversation,
communication and dialogue, social structures and economics.
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2 Dances with Valves
2.3 Conversation and Teach-back: Beyond
‘Message exchange’
The dance metaphor of conversation goes some way to explaining
the difficulties of online communication. A dance does not involve
‘exchange’ of messages in the way that is often conceived in the literature
about conversation in education. The classic example is Laurillard’s
conversation model (Laurillard 2001). In Laurillard’s model
of teaching and learning processes, which she adapted from a more
sophisticated (but extremely dense) cybernetic conversation theory
of Gordon Pask (Pask 1975), there is an emphasis on the exchange of
messages between the teacher to the student of what is to be taught,
or what action to take, and the messages from the student to the
teacher as to what is understood. Typically, the conversation framework
is presented as a state-transition diagram:
Teacher
Learner
Teacher’s Environment
Learner’s Environment
The model is presented as one which is driven by a comparison
between the teacher’s messages and the student’s. Understanding
is said to be achieved when the student articulates an explanation
which conforms to what they have been taught. This was an attractive
proposition to those who sought to defend text-based online
utterances as being functionally-equivalent to face-to-face discussion.
It overlooked, however, the complexities of conversation, of which
which Pask considered in more detail.
Pask introduces his original idea of teach-back in the following way:
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2.3 Conversation and Teach-back: Beyond ‘Message exchange’
Teachback goes as follows: the teacher says of the student
(or subject) that the student understands a topic
to the extent that he can teach it back to the teacher.
This is, understanding is inferred if the student can furnish
an explanation of the previously discussed topic and
can also explain why he gave that explanation of how he
constructed it. The crucial point is that the students explanation
and the teachers explanation need not be, and
usually are not, identical. The student invents an explanation
of his own and justifies it by an explanation of how
he arrived at it (in fact an identical explanation is generally
rejected unless the student can give a reason why the
teachers explanation was particularly good).
The difference between the teacher’s utterances and the student’s
is critical in the teachback process. Pask goes on to say:
It can be argued that although retention of taught-back
items will be perfect within one session (it is), the resilience
of a memory will depend upon the number of
explanations produced in teachback; for example, that
a student impelled to give many explanations will fare
better at session 2 than a student required to give only
one. He has many ways of reconstructing a concept and
this redundancy will combat the effect of interfering and
incompatible learning experiences during the intervening
week.
In other words, there is an overlap in the things which are described
by the different explanations that a student might produce.
There is a similar overlap in the explanations which a teacher might
produce, and there is an overlap between the explanations produced
by the teacher and the explanations produced by the learner: this
overlap is what Pask refers to as redundancy: multiple descriptions
of the same thing. In more recent work on cybernetics and the production
of meaning, this overlap or redundancy has been studied and
is widely considered to be one of the central systemic features for the
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2 Dances with Valves
establishment of meaning (Leydesdorff and Ivanova 2014). Redundancy
is a way of generating shared meanings in the communications
between people, and behind this lies the essence of what is meant by
understanding.
If this is correct, then the capacity of the medium to express a
variety of different kinds of explanation for things is an important
factor. In most thinking about the role of technology in education,
the capacity of the medium is often considered to be neutral. If
the context within which the exchange between the teacher and the
learner is constrained to limited forms of articulation (for example,
and most commonly, text only in email or forums), then the scope
for the creative expression of what Pask calls the ‘redundancy’ of
explanations is equally limited. This is not to say that a variety of
description within a restricted medium isn’t possible, but that the
nature of the ‘richness’ of alternative descriptions is constrained to
what happens in successive text messages.
In face-to-face communication, or indeed, in communication over
video, there are multiple descriptions of understanding expressed simultaneously.
For example, somebody might explain their understanding
with gestures and the movement of props in front of them
(for example, in explaining Newton’s Laws of Motion). Simultaneously
to moving their arms, they will give a commentary of what they
mean, whilst also modulating the tone of their voice to convey the
important points. The redundancy in such simultaneous communication
can be seen if we were to see whether the meaning of what is
conveyed could still be conveyed if any one of these different forms
of communication (gestures, words, pitch of voice) was removed. In
most cases, it can - although the resulting communication may be less
compelling. This raises the question as to what happens in communication
between these different simultaneous forms of explanation.
In this process of conveying simultaneous forms of communication
in conversation, the redundancy between the difference messages and
different explanations suggests that there is a coordination of constraint:
both teacher and learner uncover the constraints of the other
by reading into the redundancies of description that each presents
to the other. By understanding these constraints, future utterances,
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2.3 Conversation and Teach-back: Beyond ‘Message exchange’
recalibrations and suggestions for activity are steered. A dance becomes
possible because the constraints become more explicit.
Now we can respond to the question, “How does the medium of
communication matter?”:
• Different levels of redundancy of description can be presented
in different media.
• The coordination of conversation can be effectively steered with
the expression by both teachers and learners of different kinds
of media.
Those media which convey the richest degree of redundancy of
communication present the easiest starting point for analysis. They
also, of course, present the most compelling case for the power of
technology in education. Every online course today uses videos and
animations, and many exploit visualisations, simulations, and games.
Equally, in the synchronous online conversations between individuals
using Skype or even telephone, the simultaneous description of the
voice, with its pitch, accent, tempo, combined with images over a
shared period of time presents an experience which approaches the
veracity of face-to-face discussion - with the benefit that these interactions
can be recorded and replayed. All of these forms of media
convey rich descriptions about themselves. Through the shifting
lights on the screen, sounds and opportunities for interaction,
multiple redundant descriptions are presented. By comparison, textonly
communications which Laurillard largely concerns herself offer
fewer simultaneous descriptions, which from an analytical perspective
present more complexity because the text medium is so restricted in
the number of ways in which learners and teachers can articulate
their understanding. For this reason, we begin in this chapter with
an analysis of video. In the next chapter I turn to an analysis of
text - and in particular, examine the significance of social media in
learning.
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2 Dances with Valves
2.4 Analysing Educational Video
The ability to capture and share the moving image in real-time has
transformed the way education is organised. From Khan academy to
the ‘flipping’ of classrooms, the easy means by which video can be
produced has meant that for most educational technologists, the making
of animated content in various ways has become a foundational
professional skill. With the readily-available smartphone, however,
it is not just professional educational technologists who can make
video: the smartphone has enabled learners, just as all private citizens,
to become video producers and sometimes broadcasters, able to
share experiences, or broadcast events, evidence of acts of injustice,
violence, protest, or capturing moments of life in a richly descriptive
way. Equally, of course, personal technology has enabled individuals
to broadcast video of murder, abuse, exploitation, sex and suicide.
For whatever purpose it is used for, video is imbued with veracity
- trusted as an accurate record of the passing of events. Video is a
medium which easily documents informal and spontaneous communication
and behaviour in ways which would otherwise require linguistic
dexterity beyond the reach of most. It has levelled the playing field
of testimony.
Whilst its lens is restricted to what can make a difference to somebody
staring at a screen, it nevertheless reveals aspects of human
behaviour and (when applied in education) something about the ancient
practices of education which become available for objective inspection.
As a way of introducing some cybernetic concepts, we start
by exploring an analysis of a simple video. In the process, we can
introduce some powerful free or Open Source tools. Just as the technology
for producing video is powerful (and much of it is free or open
source), so too is the technology by which we might analyse it. In
the following analysis I use three tools: Kinovea for video analysis,
YouTube for text transcription, and PureData for the analysis of
pitch. Other tools could, of course, be substituted for these, but the
point to make is that much powerful software is available for free.
The following analysis considers the information content of a video
from a number of different perspectives: changes to the image; changes
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2.4 Analysing Educational Video
in the pitch of the voice; the pattern of words used. By information
content I mean a way of describing the complexity of different aspects
of a video. It is one of the central propositions of cybernetics that
complexity can be counted. Whilst the information theory equations
look forbidding (at first sight), essentially all they are doing is counting.
Moreover, what is counted are differences - or perhaps more
explicitly, surprises. A surprise, after all, is a difference that makes a
difference.
2.4.1 Multiple Descriptions
Edgar Morin on the cinema
The multiple descriptions presented by the moving image has long
been a focus for the analysis of cinema. One of the most perceptive
commentators on this is Edgar Morin (who also wrote about education).
In his book "Cinema and the imaginary man", Morin details
the different descriptions that the cinema presents, making a comparison
between the descriptions presented by the crude early medium
of the animated image, and the later medium of the cinema.
With such a list of different descriptions, it is interesting to consider
to what extent Morin’s descriptions depend on each other. The most
obvious example is to point out that the distinctions between light
and shadow is complementary. Equally, the distinction between the
moving camera and the succession of shots may also suggest a codependence
between the distinctions.
⎧
Image
Affective excitation established
by the ani-
⎪⎨
mated photograph
⎪⎩
Shadow − reflection − double
W orld within arm ′ s reach
Real movement
Imaginary
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2 Dances with Valves
⎧
Affective excitation established
⎪⎨
by cinema
techniques
Camera mobility
Succession of shots
Persecution of the moving element
Acceleration
Rhythms, tempos and music
Assimilation of a milieu
Encirclements
Slow down and suppression of time
Close-up
{
lighting
lights
{
shadows
high-angle shot
⎪⎩ shooting
low-angle shot
But when might we stop? Even a static Powerpoint page with an
image on it can be subject to many different descriptions of itself.
Do these descriptions all amount to the same thing? What is it that
adding descriptions contributes to understanding? Video presents
many overlayed descriptions of a thing. There is text, moving images,
pictures, words and speech. How meaning is conveyed through the
interaction of these different elements has been the subject of study
in cinema ever since its invention.
Ezra Pound on Poetry and Chinese Ideograms
When considering the ways in which a poem communicates, Ezra
Pound points out a similar overlaying of factors in Chinese writing.
Pound argues that:
“In tables showing primitive Chinese characters in one column
and the present ‘conventionalized’ signs in another,
anyone can see how the ideogram for man or tree or sunrise
developed, or ‘was simplified from’, or was reduced to
the essentials of the first picture of man, tree or sunrise.
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2.4 Analysing Educational Video
Symbol
Meaning
= Man
= Sun
= Tree
= sun tangled in the tree’s branches, as at sunrise,
meaning now, the East”
(Pound and Dirda 2011)
With the kind of overlaying of descriptions that Pound describes
in Chinese writing, he explains how it is that words are assembled
from other words. To define ‘red’, for example, Pound asks
“How can he do it in a picture that isn’t painted in red
paint? He puts (or his ancestor put) together the abbreviated
pictures of
ROSE
IRON RUST
CHERRY
FLAMINGO
That, you see, is very much the kind of thing a biologist
does (in a very much more complicated way) when he gets
together a few hundred or thousand slides, and picks out
what is necessary for his general statement. Something
that fits the case, that applies in all of the cases.”
So what about other forms of communication? In music, for example,
‘descriptions’ are overlaid on top of one another in what is called
‘counterpoint’, of which J.S. Bach provides the supreme example:
At the very simplest level, the combination of the two lines of
music, each of which is a coherent melody in its own right, describes
a sense of harmony and dynamic drive which each melody on its own
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2 Dances with Valves
cannot convey. At the same time, each individual melody essentially
describes very similar patterns: there is a alternation between fast
and slow notes, between rising patterns and falling patterns. In each
individual melody, there is an alternation between things which are
expected and things which are surprising. The balance between what
is expected and what is surprising is mirrored in each line of melody:
each might be regarded as an alternative description of the same
thing.
The metaphor of counterpoint provides a way of examining video.
In video, the counterpoint is between the images which are presented
to the viewer, the words that are spoken, the tone of the voice that
is speaking, the speech rhythm, the pace of different shots, the movement
of the camera, and so on. There is, of course, often additionally
music. In analysing the video’s counterpoint like this, we can examine
each of these elements - the text, the pitch, the image. . The
experience of watching a video, just like the experience of listening
music, is one of being emotionally engaged as the different elements
of surprise overlap one another.
We begin by analysing a very short video which introduces the
concept of ‘educational cybernetics’. The video uses animations as
pictures are drawn on the screen whilst somebody talks. We are going
to explore the changes to the image, the text spoken and the pitch
of the voice. From a practical perspective, with this kind of analysis,
each of these different elements require different tools to capture
and analyse them. One of the wonders of the modern technological
environment is that most of the highly sophisticated tools required
for performing sophisticated analyses are available for free or as open
source.
To use open source tools, the analysis of the moving image requires
a tool to label the different moments in the video and export the data.
The open-source tool Kinovea (http://www.kinovea.org) enables this
and exports a simple spreadsheet containing a time code (seconds
into the video) with the description of what is happening at that
time.
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2.5 Encoding surprise in the moving image
2.5 Encoding surprise in the moving image
Kinovea provides a simple way in which transformations in the moving
image can be encoded. It is a manual process (although it is not
impossible for automatic coding of changes to be achieved), and since
this is the case, there is an element of subjectivity in terms of what
is considered to be significant and what is not. However, this subjectivity
should be seen from the perspective from which we opened this
chapter: every identification of a feature, in whatever way, is one of
many possible descriptions. The point of the following analysis is to
see how multiple subjective descriptions relate to one another.
From a practical perspective, a code can be devised to identify
significant events in the video. In this example, the following code is
used:
Code
A
F
S
X
P
T
Meaning
Change of camera angle
Draw letter
Draw Spiral
Zoom in
Zoom out
New shot
By going through each second of the video, codes can be assigned
to the events at different times. Kinovea will export this code as a
spreadsheet of time against code which looks a bit like this:
Time Code
0.3 A
1.0 F
1.3 F
1.5 F
2.2 F
3.1 ...
This basic format of encoding media into a time-code plus an event
code is the basic template into which other descriptions of the video
will be produced.
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2 Dances with Valves
Encoding surprise in the Spoken text
Another dimension of the video are the words that are spoken as
the image changes. Modern technologies for the automatic transcription
of the spoken work work by analysing waveforms and rapidly
matching them to a textual equivalent. The technology is embedded
in many online services, including YouTube. Any video uploaded
to YouTube will quickly acquire an automatically-generated caption
file. This is often surprisingly accurate, and any mistakes can be
corrected. The file can be downloaded in a variety of text formats
which can be further manipulated to produce a time code with the
text spoken at that time.
The analysis of the text is less problematic than the analysis of the
video since it doesn’t need coding. What is produced is a time code
and a passage of text, which looks a bit like this:
seconds spoken text
1.0 Education cybernetics
2.0 concerns itself with
3.0 the organisation
4.0 of education
In examining this table and comparing it to the table of video
codes, we can see that there is at least coherence in the time codes,
but that the data, the text, and the codes of what happens in the
image, are of a different type. If we want to make a meaningful
comparison between these two types of data, we have to convert them
somehow into the same type of data. Before going on to do this, we
will consider the final element in this analysis.
Encoding surprise in the Pitch of Voice
Modern music processing tools are as powerful as tools for converting
spoken words into text. As with the analysis of video, many of these
tools are Open Source. The two leading tools are Supercollider and
PureData. For the purpose of analysing pitch, PureData provides a
component called ‘Fiddle ’ which we will use to ‘listen’ to the audio
behind the video and estimate the shifts in the pitch of the voice over
time.
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2.6 Putting things together: Analysing Surprise with a ‘complexity machine’
Any sound - particularly that of speech - is a highly complex waveform.
Whilst humans can work out the pitch of a sound by listening
to it, for a computer it is more difficult. The Fiddle~ component
in PureData listens to a waveform and breaks it down into its
constituent components using a technique called ‘Fourier Analysis’.
From the components of the sound, Fiddle~ can calculate the pitch
of the sound.
Having done this, it can produce another table of time against
pitch. It’s another type of data, so now we have to address the
problem of how to align all these different types of data into a single
type so that they can be compared.
2.6 Putting things together: Analysing
Surprise with a ‘complexity machine’
For anyone who has followed the preceding details of how to gather
data about the words used, the pitch of the voice or the coding of
the moving image, there now appears an obvious question: How can
a meaningful coordination be made between these wildly different
factors? Or to put it more simply, how could they be plotted on the
same axis so that they can be compared?
To solve this problem, we need to invent a special machine. I’m
calling the machine a ‘complexity machine’ because its only purpose
is to embody a particular amount of complexity. This can be done because
some things are less complex than other things. A light switch
is less complex than a steam engine, for example. What makes it less
complex is the fact that the light switch can only occupy two possible
states: on or off. The steam engine, however, because it comprises
many components, most of which are more complex than the light
switch, can occupy many states. Is there a way of characterising the
complexity of the steam engine?
There are two possible approaches: we could examine all the different
components of the steam engine and calculate its theoretical
number of states; alternatively, we could examine the behaviour of
the whole thing (just as we might examine the behaviour of the light
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2 Dances with Valves
switch). Something with a lot of complexity has surprising behaviour
(light switches, by contrast, are not very surprising!). But the fact
that we might examine the behaviour of the light switch, and examine
the behaviour of the steam engine in order to compare their
complexity presents an intriguing possibility: could the complexity of
the steam engine be expressed in terms of the complexity of multiple
light switches?
...
Having gathered each of these three descriptions (and of course
there are many other descriptions which we might make of video),
we can begin to examine them individually and then together. Each
description is time-based: text, pitch and video events occur over
time. As time passes, some things stand out and draw our attention.
They do this because they appear as ‘surprising’ - jumping out of the
context of what’s gone before: it is something different.
For early communication engineers, this problem of how to count
information was a practical issue. Communications systems carry
voices and data as electric impulses over many thousands of miles.
Over that space, the quality of the signal degrades through interference
and resistance to the point where it can become unintelligible.
If the message communicated is very simple, then perhaps this isn’t
a problem: multiple repetitions of a simple message might be able
to withstand a significant loss of quality and still be interpreted correctly.
But if the message is complex, involving many different kinds
of signal (as with speech, for example), then the challenge is to be
able to predict the quality of medium (or its bandwidth) over which
the message is transmitted. To do this, we have to calculate the relationship
between the bandwidth and the complexity of the message
to be communicated.
The solution to this engineering challenge was created by Claude
Shannon (Shannon and Weaver 1949), and has since become known
as "Information theory". Information theory measures the complexity
of a message by calculating the probability of each event and
producing an index of the ‘average surprisingness’ of a sequence of
messages expressed in terms of the number of ‘bits’ or on-off switches
which would be required to transmit a message of this complexity.
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2.6 Putting things together: Analysing Surprise with a ‘complexity machine’
Shannon derived his equations for information theory from similar
equations devised by Ludwig Boltzmann which are used in physics to
calculate the dissipation of heat in what statistical thermodynamics.
Boltzmann called his idea for the dissipation of heat entropy, and
Shannon borrowed this same term to describe his measure of surprise
in messages - partly at the suggestion of John Von Neumann, who
reportedly said “why don’t you call it entropy? That way, you can
impress people in arguments, but nobody will understand what you
are talking about”.
The key issue in transmission and reception of a message was that
the complexity of a machine (i.e. the number of on-off switches it
had) to generate a particular message had to equal the complexity
of a machine which could successfully receive it. This complexity
of the machine could equally be called the variety of the machine
- the number of possible states which the machine could exist in.
Simultaneously with Shannon, Ross Ashby was exploring the idea
of variety in abstract communicating machines where he stated that
any complex machine could only be controlled by a machine of equal
of greater complexity.
At the same time, if the complexity of the message was damaged
through transmission, then this would impair the ability to successfully
receive the message. Therefore there is a trade-off between the
complexity of the sender and receiver, and the bandwidth of communication.
How, then is this complexity measured? Consider a very simple
message:
A A A A A A A A
For each symbol in a message, we can calculate its surprisingness by
multiplying its probability by the log of its probability. The total
surprisingness of the message is the sum of this calculation for all the
different symbols which appear. With 8 A’s, the number of symbols
is 1, and the probability of A appearing is 1. The log of 1 is 0,
therefore, the surprisingness of the message is 1 × 0 = 0. But what
about this:
A A B A A A A B
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2 Dances with Valves
Here, there are two symbols, so we calculate the probability of
each symbol and multiply it by the log of the probability of each
symbol. So the probability of A is 6 8
and the probability of B is
2
8 = 1 4
. Information theory calculates logs to base 2. This is because
in digital signals, something can be either on or off. Shannon’s central
problem which he sought to address was how many on/off switches
would be required to transmit a particular message with a particular
degree of surprisingness. There had to be enough switches to generate
the variety of different symbols that were required to be sent.
6
So we can use log 2 to calculate log 2 8 = −0.415 and log 2 1 4 = −2.
Now we multiply these log values with the probability of those values
to give 6 8 ×−0.415 = −0.311 and 1 4
×−2 = 0.5. Adding them together
gives −0.811. So the addition of an extra symbol produces quite a
jump in entropy. Note, if there was only one B in a sea of A’s,
1
then the average surprisingness would be higher: log 2 8
= −3 and
7
log 2 8
= −0.192, and multiplying these by the probabilities gives:
1
8 × −3 = −0.375 and 7 8
× −0.192 = −0.168 giving a total of 0.543.
Finally, what if we have more symbols and more randomness?
A F X P T T U W
Here the probabilities are:
Symbol (i) Prob.(p i ) log 2 p i p i × log 2 p i
1
A
8
−3 -0.375
1
F
8
−3 -0.375
1
X
8
−3 -0.375
1
P
8
−3 -0.375
1
T
4
−2 -2
1
U
8
−3 -0.375
1
W
8
−3 -0.375
TOTAL -1.75
Now we can turn to the actual text in the video. It reads:
Education cybernetics is the study of the organisation of
education. Technologies help us to organise ourselves in
different ways.
Using each word as a symbol, we can draw up the following table:
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2.6 Putting things together: Analysing Surprise with a ‘complexity machine’
Symbol (i) Prob.(p i ) log 2 p i p i × log 2 p i
1
Education
8
−3 -0.375
1
Cybernetics
8
−3 -0.375
1
is
8
−3 -0.375
1
the
8
−3 -0.375
1
study
4
−2 -2
1
of
8
−3 -0.375
1
organisation
8
−3 -0.375
1
Technologies
8
−3 -0.375
1
help
8
−3 -0.375
1
us
8
−3 -0.375
1
to
8
−3 -0.375
1
ourselves
8
−3 -0.375
1
in
8
−3 -0.375
1
different
8
−3 -0.375
1
ways
8
−3 -0.375
TOTAL -1.75
So we can get a value for the entropy of this particular passage of
text.
What then about the corresponding entropies in the pitch of the
voice? Using a simple program written with the PureData (pd) tool,
it is possible to retrieve a set of values for the different pitches used
as these words are spoken. What does this tell us? Some people talk
in a very monotonous voice. If speech like this was analysed, then
there wouldn’t be very much entropy. Alternatively, if my voice had
been highly animated, there would be a greater variety of pitch, and
therefore a greater value for entropy.
The name for the study of the music of spoken language is prosody.
In research into prosody, various forms of notation have been developed
to illustrate the way we speak. One way is to notate the pitches
of words with the height of letters. For example, you might imagine
the text ‘Education cybernetics” spoken in a ‘singing’ line like this:
19

2 Dances with Valves
Analysing the pitches of the text, we can get another set of numbers
Pitch Frequency (i) Prob.(p i ) log 2 p i p i × log 2 p i
1
A
8
−3 -0.375
1
F
8
−3 -0.375
1
X
8
−3 -0.375
1
P
8
−3 -0.375
1
T
4
−2 -2
1
U
8
−3 -0.375
1
W
8
−3 -0.375
TOTAL -1.75
For this passage of text, we can also calculate an overall value for
the surprisingness: in this case, -3.246.
What about for the video events which accompany the spoken text?
Here too, there is a series of surprising events, although the video
in this case might not show too much in terms of surprising things
happening.
2.6.1 Multiple Descriptions over time
As time passes in a video, there are clearly varieties in the nature
of surprise. Without going into the detail of the second-by-second
dynamics of this video, I want to jump ahead to a key moment in
the video: the moment when the key message is asserted. Imagine
at this moment, the key message is “Education is about Organisation”.
Imagine that at this moment, the words “Education is about
Organisation” are spoken strongly in a forceful way - the pitches of
the words are static, and accompanying them are the words in the
video which appear one by one with a regular rhythm. What occurs
here?
The principle observation is that each description displays a similar
measure of surprisingness. Whatever difference might have existed
between the different descriptions at other points in the video disappear,
at a climactic moment when the creator of the video wants to
drive the point home.
20

2.7 Objectivity and Analysis
In other words, a graph of the different entropy values might be constructed
which enable us to monitor the relationship between different
values.
2.7 Objectivity and Analysis
In essence, what is produced through this process is a description of
the behaviour of a ‘machine’ which produces a set of differences over
time, where each difference is reduced to a particular letter or code.
There is a certain degree of arbitrariness in this coding. It might be
argued, for example, that these particular differences are not the only
possible ones. For a different observer, there might be a different set
of observations (and different codes) which might be produced. This
is obviously true. Indeed, there is no reason why the same exercise
might be carried out by any number of observers, producing different
kinds of machine which generate different codes. Does this matter?
To address this question, we have to return to some of the issues
raised in Chapter 1. Any distinction is framed by a context which
is under-determined. Every distinction is framed by other distinctions,
including those distinctions which are out-of-scope if we are to
simply focus on the events in the video images alone. Whilst some
research methodologies in the social sciences require that researchers
‘bracket-out’ contextual factors, there is an implicit assumption behind
such backeting-out that each element does not exist in a relation
to others. Cybernetic analysis, on the other hand, embraces the idea
of relation and rejects the possibility that any single distinction can
ever be objectively determined. By contrast, every distinction, and
every alternative description, indicates - but does not determine - the
constraints within which it is produced. Descriptions layered upon
21

2 Dances with Valves
descriptions reveal constraints at multiple levels: constraints of bias
in individual perception, constraints of social norms in language, educational
practice, media practice, and so on.
Cybernetic analysis aims to articulate the relations of constraints,
and to some extent these can be apprehended by overlaying multiple
descriptions of the same thing. However, whilst an analytical
indication of constraint can be useful, it itself is constrained by the
range of observations made, the bias of the analyst, the mathematical
tools and techniques deployed, and the implicit assumptions about
the world (including the assumptions about cybernetics!) which underpin
those tools. There is no objectivity. So what’s the point?
The point is simple. Cybernetics is concerned with steering. Imagine
that you are driving over unknown territory attempting to navigate
to a destination whose location you are only vaguely informed
about. You begin with a basic hypothesis about how to get there,
and the challenges that lie ahead. The environment presents constraints
in the form of an uneven road surface, or maybe the occasion
cliff-edge. You will be careful: which means that each step of the
way you will be learning about your environment, and the kinds of
constraints that you have to be aware of. You will be creating a working
hypothesis of how this environment is, and every now and then
something will happen which surprises you, causing you to change
your hypothesis.
In this process of steering, constraints are identified negatively, as
the difference between what is expected and what is perceived. By
reflecting on the things that are perceived, an increasingly rich picture
of constraints emerges, which occasionally will change the hypotheses
about what to expect.
2.8 Video as Conversation
If video is a powerful medium, it is because it has a capacity to steer
understanding at a distance. The analysis conducted here has focused
on the fact that educational video shares an important feature
of a face-to-face encounter: it presents simultaneous multiple descriptions,
just as we do in face-to-face conversation. Having said that,
22

2.9 Conversing and Control: The origins of Cybernetics
the educational video does not appear to be conversational: it is,
after all, one-way communication.
The same argument can be made of many media of communication
- particularly books. Is there a conversation which occurs between a
learner and a long-dead author? The learner is faced with a body of
work, commentaries about that work, biographies and so on. In other
words, there are many descriptions. The process of uncovering the
meaning of those many descriptions is the process that the learner
has to engage in through reading, studying and talking to others.
Long dead authors teach not in direct conversation with a learner,
but through the presentation of multiple descriptions of their ideas
which seep into a culture, and which demand of the learner that new
conversations with others similarly engaged with author’s work are a
necessary component in being able to piece together its meaning. In
the process, the culture is renewed.
Just as a learner will pore over pages in a text, reading and rereading,
so with an educational video, learners will replay key moments
to see new things that they might have missed first time.
Equally importantly, through social media, they will share the video
resources they discover: the video object, by virtue of the complexity
of the multiple descriptions it contains (just like the book) becomes
a focus for conversation among those who share a fascination for it.
It helps to establish conversations within which the relationship between
the parties is controled by each party such that the ’dance’
doesn’t break down.
2.9 Conversing and Control: The origins of
Cybernetics
Until this point we have avoided talking about control, ignoring the
fact that cybernetics was originally defined as ‘the art and science of
control in man and machine’. But we have spoken of conversation
being a dance, and that the dance can either work, or it can break
down - sometimes necessitating the teacher to recalibrate their approach.
This issue of dancing and conversation is precisely the same
23

2 Dances with Valves
as the issue of control. However, unfortunately the word ‘control’ has
some unpleasant associations with coercion, a loss of free will, authoritarianism,
and so on. Yet all of those cases are characterised by
a lack of control. So we require a more precise definition of control.
Stafford Beer explains the cybernetic sense of control like this:
Control is an attribute of a system. This word is not used
in the way in which either an office manager or a gambler
might use it; it is used as a name for connectiveness. (Beer
1965)
Control simply refers to the manifest connection between dancers
whose moves complement each other, and who participate in a whole
system which exhibits coherence in its behaviour. Control is just
as evident in two people having a conversation in which they are
both deeply committed and involved. It is also evident in a game of
football between two teams, or between two people playing a game
of chess.
Cybernetics was originally developed in the 1940s as an approach
to studying the dynamics of mechanical control systems. For Norbert
Wiener, who founded the discipline, the mechanical control problem
was one of being able to shoot incoming missiles. The mechanical
problem involved feedback: the incoming missile’s position and trajectory
would change, and as it did, so the calibration of the countermeasures
had to be adjusted. The incoming missile and the antimissile
missile engaged in a dance.
The dancing between two people could be abstracted to the ‘dancing’
between two complex systems. Ross Ashby was a pioneer of
early cybernetics, who in 1948 built a machine which ‘danced’ with
itself. He called it the ‘homeostat’. From observing the behaviour of
the homeostat, Ashby devised a ‘law’ of control which has dominated
the discipline of cybernetics ever since. What became know as the
‘Law of Requisite Variety’ states that any complex system can only
be controlled by another system of equal of greater complexity.
24

2.10 Ashby’s Dancing Machine
2.10 Ashby’s Dancing Machine
Ashby’s Homeostat was a machine that danced with itself. This behaviour
led the early cyberneticians to think about the similarities
between the behaviour of machines like this and the behaviour of human
beings. Were the elements of Ashby’s machine communicating?
Were they ‘coordinating their understanding’ with one another? Was
the machine thinking?
The machine comprised four units which were connected to each
other in such a way that the output from one fed into the input of
another. The four dials on Ashby’s Homeostat each articulated a particular
description - or a guess - as to what the settled value would
be between them. The mechanism that connected them ensured that
this complexity between the dials led to a gradual process of accommodation
of the dynamics of each of the other dials. The more dials
that were thrown in to the mix, the richer the number of descriptions,
and the richer the challenge of finding an accommodation. Yet
any single dial could have articulated the answer. Whilst every extra
‘description’ might be seen to be ‘redundant’, its effect is to increase
the complexity of the overall dance in the machine.
Whatever answer might be given to these specific questions, the
mechanical relations between interacting units with feedback was the
spur for extensive scientific investigation - which later fed into different
disciplines of psychology, philosophy, biology, ecology, sociology,
anthropology and computer science.
The starting point for this was the ‘dancing machine’ of Ashby’s
25

2 Dances with Valves
‘’. Ashby’s homeostat was the machine which presented Ashby with
his insight into the self-organising mechanisms of the brain.
Waltslawick explains the dynamics of the homeostat (Watzlawick
1968):
This device consists of four identical self-regulating subsystems
that are fully interconnected so that a disturbance
caused in any one of them affets, and is in turn
reacted to, by the others. This means that no wsubsystem
can attain its oiwn equilibrium in isolation from the
others, and that Ashby has been able to prove a number
of most remarkable “behavioural” characteristics of
this machine. Although the circuitry of the homeostat
is very simple when compared with the human brain or
even with other manmade devices, it is capable of 390,625
combinations of parameter values, or, to make the same
statement in more anthropomorphic terms, it has that
number of possible adaptive attitudes to any changes in
its internal or external medium. The homeostat achieves
its stability by going through a random search of its combinations,
continuing until the appropriate internal configuration
is reached. This is identical with the trial-anderror
behaviour of many organisms under stress. In the
case of the homeostat the time required for this search
may range from seconds to hours.
The emphasis placed on repetition and redundancy in the previous
section raises a question about what happens when we are informed
about something. What happens when a communication takes place
- maybe with some redundancy - and we have understood the communication?
In Ashby’s Homeostat, the state of ‘being informed’ was the state
where a stable setting was acquired between the different components
of the machine. In the process of reaching this state, each device sent
signals to each other device, and each other device reacted to those
signals and sent signals to other devices. Most importantly, each
26

2.11 Summary: From Video to Ashby’s Law of Requisite Variety
device could potentially exist in the same number of states as each
other device: the Homeostat obeyed Ashby’s Law.
Another way of expressing Ashby’s Law is to consider two communicating
devices: one sends a message and the other receives and
interprets the message.
This setup only works if the sender and the receiver have the same
number of options to either send a symbol, or interpret that symbol.
If the sender has more options to send than the receiver has to
interpret, then the communication cannot work. When information
has been ‘transferred’ then the receiver is able to predict the message
that the sender is sending (in the case of the Homeostat, they have
settled on the same value).
Communication can be improved if there are particular ‘patterns’
which can be identified by the receiver in the messages being sent.
These patterns are formed by increasing the amount of redundancy
in the message. A simple pattern is to repeat messages. A more
complex patten is to send the same message in a different way. The
pattern becomes the essential feature of the communication.
2.11 Summary: From Video to Ashby’s Law of
Requisite Variety
There are many introductions to cybernetics whichbegin with Ashby’s
Law of Requisite Variety and gradually work their way up to the
27

2 Dances with Valves
complexities of biology or ecology. The implication of this is that
somehow through a incremental process, a simple idea can grow into
the richness of nature through a series of logical connections. But
Ashby’s Law, and indeed the whole of cybernetics, is not intended
as universal foundational knowledge from which all life can be explained.
Instead, it is a way of describing the manifest complexities
of life with the intention of constructing coherent plans for negotiating
them. But the process of describing complexity and negotiating
it is circular. Indeed, the drive for cybernetic inquiry is not the explanatory
success of any particular cybernetic description - it is the
gap between what happens that can be explained and what happens
that can’t be explained. Ashby held to a scientific practice which is
radically different from the practice handed down from the enlightenment.
The cybernetician, he says,
"Observes what might have happened, but did not"
Educational theory makes many assumptions about what might
happen in learning and with using technology. It rarely considers the
ways in which it might be wrong. If the theory doesn’t work out in
practice, then an excuse is generated: insufficient resource/training
or under-developed technology. This is partly because many interventions
in education are not made to explore the power of a theory,
but instead use a theory as a theoretical justification for plans which
may seem like a good idea, but are not necessarily theoretically coherent.
If things don’t work, however, even if it is because of a plan
being over-ambitious, there is some gap in the model of the world
which led to an intervention being attempted and the nature of the
constraints which prevented that plan being executed. Ashby’s scientific
cybernetic challenge is to identify those gaps more clearly so
that they can be better negotiated in future.
The other side of this is trying to explain things that do work for
which there is not a lot of good explanation. Online educational video
provides a good example. Cybernetics can be useful in describing this
not because its descriptions or its theories are intrinsically better than
anything else, but because the cybernetic approach is both generative
of many alternative descriptions and generative of ways of bringing
28

2.11 Summary: From Video to Ashby’s Law of Requisite Variety
those descriptions together.
The point about multiple descriptions leads straight to the central
issue of conversation. Conversation, control, and coordination
are the same thing. Despite a somewhat uncritical championing of
‘conversation’ in educational technology theory, some of the manifest
effects of technology in universities has been to kill the spaces for
conversation. Educational video sits in an uncomfortable theoretical
position where, on the one hand it can be viewed as one-way and not
conversational, whilst on the other, the richness of the experience of
good video suggests many similarities to conversational engagement
even when the engagement appears to be one-way.
The critical feature of conversation is multiple description. All
great objects for discussion in history have embodied many different
distinctions and produced many different descriptions. The negotiation
of different descriptions is a conversational process, but the
production of artifacts (videos) which themselves embrace multiple
descriptions provides a critical coordination which steers subsequent
conversations. In this process, Pask’s observation, supported by the
aesthetic observations of Pound or Morin, that what matters is the
overlap between redundant descriptions is the most important idea
underpins the nature of human communication itself.
29

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