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Given the assumption of this thesis that our ability to predict in the long term in
any meaningful
educational sense is hopeless, and our ability to predict in the short term is highly constricted, and
given the fact that the pace of change is already high and accelerating relentlessly, what should an
educational system do and be? Can there be any practical application of this seemingly dismal and
gloomy knowledge? To the contrary, I have never felt more positive about the future of the human
race. Over the last five years, the answer that has emerged thrives on the chaos and nonlinearity of
human diversity. The answer provides hope, not guarantees. It requires considerable change in our
educational systems, but it does not require erasing them and starting over. The answer brings
together on an hourly basis our classrooms and our government, business, religious/ethical and
home communities. The answer further raises the relevance and importance of education. The
groundwork for making the answer fully functional in our classrooms and communities is already
under rapid development across the globe.
But a linear answer to a nonlinear problem would be inappropriate. To merely read
the answer is to
miss the depth of the answer. To understand the answer requires participation. To participate, you
will need to continue this journey. Please join us in the
hypermedia realm
and follow the unfolding branches in their entirety. |
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Education
Educators take some responsibility for matching curriculum content with personal and social needs. Observation
of
the increasing rate of interaction and increasing rate of change, increasing growth in information and
increasing rate
of interaction both on a local and global scale require a testing of the educational system for its
adaptiveness to the
situation at hand. Such change will require a sensitivity to the structural implications of the previously
discussed
ideas of the organizational researchers. Further current trends also indicate a need to highlight those
aspects of
educational research that would facilitate educational self-organization. "Adaption and plasticity,
two basic features
of nonlinear dynamical systems, also rank among the most conspicuous characteristics of human societies.
It is
therefore natural to expect that dynamical models allowing for evolution and change should be the most
adequate
ones for social systems" (Nicolis, 1989, p.344). The issue of self-organization is becoming central
to even the field
of instructional design which has opened debate on the role of the learner in this process. Well-known
dynamic
models for education include the work of gestalt psychologists, Dewey, Bruner and Piaget. Some of their
ideas
relevant to unpredictability and self- organization will be given further consideration here.
How dynamic to make models for instructional design has been a central issue for at least the field
of educational
technology. The educational system`s capacity to forecast and then deliver what students need to learn
is at the
root of the debate (Streibel, 1986, 1988, 1989). Streibel's critics continue to treat the debate as
a technical one,
technical issues for which they have found and can find answers. It would be of aid to that dialog to
recall that this
issue is a long standing one with deep philosophical roots as well. It would equally be of benefit to
this offering to
recall that others without benefit of chaos theory have claimed an indeterministic and unpredictable
aspect of
education is both realistic and of value. Archambault summarizes Dewey's position on unpredictability
in the
curriculum:
The exact aims of instruction cannot be legislated, for they depend on a cluster of variables that are
unique to a
particular place and time. For a given situation, short-range aims must be relatively specific. Yet
any school
situation, since it is experimental in nature, has a quality of unpredictability. To try to specify,
in exact detail, the
precise knowledge that a student is to achieve, is to consider ends as remote, distinct, and separate
from practical
contingencies and the dynamic purposes of pupils. (1964, p.xxiii)
This thread of appreciation for the chaotic and indeterministic was also carried forward by Piaget.
Piaget's work is
in part precedent for Prigogine's work, and though the contribution is not direct, at the end of Piaget's
career he was
in communication with and empathetic with Prigogine's work in the years before such work would earn
Prigogine
the Nobel prize in chemistry.
The Developmentalists Piaget's ideas represent a continuum of ideas that stretch back into the aforementioned
developmentalist movement. The developmentalist's powerful metaphors of a growing child like the unfolding
of a
seed and the teacher as gardener communicated with the instincts of many educators. However, the plant
model
was drafted from the prior age, the agricultural age, and is more representative of linear convergent
growth
characteristic of plants. This linearity then makes a strong link to the underlying sense of convergency
discussed in
the industrial age paradigm. Yet, through the growth of the movement over the decades, the movement
away from
convergent level one beliefs and the potential for linkage with the information age paradigm can be
demonstrated.
Like the development of a seed, the process of growth and learning is an internal process played out
according to
inner values or eigenvalues, an unfolding according to the nature of the particular seed. Hall's 1883
article "The
Contents of Children's Minds" is concerned with the "...natural order of the development of
the child" (Kliebard,
1986, p.28). With a firm belief in the power to determine cause and effect, Hall proposed that the:
...curriculum riddle could be solved with ever more scientific data, not only with respect to the different
stages of
child and adolescent development, but on the nature of learning. From such knowledge, a curriculum in
harmony
with the child's real interests, needs and learning patterns could be derived. The curriculum could
then become the
means by which the natural power within the child could be unharnessed. (Kliebard, 1986, p.28).
Hall also recognized that strong forces and turbulence characterized children, noting that "the
pupil is in the age of
spontaneous variation which at no period of life is so great" (Kliebard, 1986, p.14). Later, Kilpatrick's
generalization
of Stimson's agriculture home- project method in his 1918 article The Project Method provided a primary
means for
pupils to act on their personal interests and values. This in turn developed into activity or experience
curriculum
(Kliebard, 1986). Maria Montessori also contributed significantly to the movement toward greater independence
and
individual responsibility. The great sensitivity of chaotic environments reminds one of her concern
for the teacher to
provide just a hint, touch or key and let the rest develop on its own. "The idea is to open the
door only a little, rather
than give a guided tour into the interior of all the world's knowledge" (Gebhardt-Steele, 1985,
p.7). She gave specific
attention to this sensitive period.
As a child developed, certain periods of special sensitivity appear from time to time, and disappear.
When they are
present the child shows a particular interest in certain objects and exercises, and is able most readily
to cope with
and learn the matters to which his special sensitivity applies.... (Connell, 1980, p.135)
This does not deny that there are also dissonances in her own work with such openness. There is a tension
between the open system side of human growth and development and the closed system side encouraged by
industrial age perception. Montessori remained a zealot about the use of specific curriculum materials,
their
sequence, method and manner. She adhered to the formal disciplinist tradition. She wrote "It is
exactly in the
repetition of the exercises that the education of the senses consists...[this is] true intellectual
gymnastics"
(Connell, 1980, p.135).
Whereas developmentalists sought to capture and control these sensitive periods, chaos theory implies
otherwise.
But whether they can be controlled or not, the chaotic paradigm would see that the project method is
an effective
contribution toward meeting individual need for self-organization. But more than that, the nonequilibrium
necessary
for innovation and self-organization requires an intensity and in-depth involvement, not more project
exercise.
Functionalists and Structuralists In the last decade or so of developmental literature, functionalism,
as represented
by cognitive psychologists and information processors, has come to dominate the structuralism of the
Piagetians,
yet neither is able to vanquish the other. To the contrary, as Beilin (1983) points out, both complement
each other
and, merged, should lead to better theory. "Both ...have distinct assets that complement each other
and are not
incompatible.... What is offered is the possibility of a synthesis of both orientations.... Piaget's
theory in recent
years was moving in that direction; other examples show the possibility as well" (Beilin, 1983,
p.36).
There are also connections here to chaotic dynamics. In Piaget's last work (Piaget, 1975/1985), when
explaining
dynamic processes, he cites Prigogine's 1971 work on dynamics (p.3). Prigogine would capture the Nobel
prize
later in 1977 for his work on dissipative systems, which in turn led to his previously cited work, Order
Out of Chaos
(1984). But chaos theory has ties to significant features of both of these developmental groups.
There are aspects of structuralism that chaos theory complements and there are others that it critiques.
It
complements aspects of equilibration. Intelligence forms through a balancing of assimilation against
accommodation in a series of stages of equilibrium. Disequilibrium forces accommodation which leads
to the next
equilibrium (Piaget, 1975/1985). It complements it by meeting its most serious objection. "The
principal objection to
our hypothesis has, however, been to say that we confine ourselves to description and provide no explanation"
(p.
147). Chaos theory demonstrates a functional model that can lead to both oscillations of the Piagetian
model. For
in the causal chain of chaos models, increased energy input increases the variation of system behavior
while low
energy input can continue the convergent type operations of assimilation. Yet, as self- organization
research has
shown there are powerful forms of assimilation motivated by strong energy input. In summary, the information
age
paradigm as previously delineated, meets all of Piaget's conditions for being a structural explanation.
Beilin quotes
Piaget, "(i)n short, the notion of structure is composed of three key ideas: the idea of wholeness,
the idea of
transformation and the idea of self-regulation" (1983, p. 17). These three ideas map onto the previously
articulated
principles of the information age paradigm and further argues for their completeness: holism, chaos
and self-
organization.
A conflict occurs, though, with Piaget's insistence on central tendency or equilibrium. He utilizes
convergent
assumptions about system behavior to describe the process. "There is no need to insist on functional
reasons. It is
clear that if knowledge is due to assimilation and accommodation, those activities must be equilibrated"
(Beilin,
1983, p. 148). The chaos paradigm shows that this is not necessarily so, though its axiom of self- organization
does not undo equilibration. Rather, chaotic dynamics adds a stage of disequilibrium, one at least as
important as
equilibrium. From the chaos paradigm's perspective it is more important, for chaos is the background
dynamic that
is a prerequisite for powerful nonlinear orders such as solitons to emerge. Piaget's model also provides
no
description, let alone explanation of the sudden nature of understanding, which the sensitivity of chaotic
systems
and self-organizing systems describes and explains.
Aspects of functionalism also benefit from and conflict with chaos theory. The chaotic paradigm implies
both
bottom up development and self-organization that complements functionalism.
Structures such as schemata, to organizational entities that Piaget and Barlett made popular, are conceived
of by
functionalists as dynamically organized representations. ...This conception of schemata differs considerably
from...
Piaget's theory, in which the constructs are given a much more logical and theoretical interpretation"
(Beilin, 1983,
p.23).
Chaos theory implies a unity across diverse and overlapping functions, negating Beilin's criticism of
the
functionalists that, "Information theory is a collection of models lacking a coherent theory...
Without such a
coherent theory, an endless number of models is possible for any given task" (Beilin, 1983, p.19).
This is precisely
the point made by chaotic dynamics. Information theory also lacks explanation of transformation and
self-
regulation, because the feedback loops and cybernetic principles are used within specific procedures,
not
interactively across them (Beilin, 1983), yet chaotic dynamics serves as an easy extension of this view.
On the other hand, there is conflict between chaotic dynamics and the functionalists with regards to
their "bias
towards environmental control of behavior" (Beilin, 1983, p.9). The unpredictable nature of chaotic
systems suggest
that such dominance is unlikely and unhealthy in the long term. The previous discussion of brain research
concurs
with such criticism of external control:
the contrast of this concept of organization to the cybernetical (psuedo-) concept of organization is
extreme. In no
sense are the organized states arbitrarily programmable, nor are they preformed in any palpable or coded
form. The
organized state is produced by the local interactions within the system, without there being the need
for any global
or centralized control. (Malburg, 1983, p. 240).
Chaos theory, then, appears to be able to play a role in criticizing and revitalizing aspects of the
structuralist dialog
and aspects of their work also provide positive feedback for the concepts of the chaotic paradigms.
Other aspects
of educational theory also reasonably integrate with chaotic dynamics to provide a new model for educational
inquiry, notably Nodding's work on intuition.
Intuition The chaotic paradigm also provides an interesting model for the origins of intuition. Further,
applied to
intuition, the research on chaos makes contribution to a debate on access to intuition and cognition
in education.
Nodding and Shores (1984) have made a case for promoting the development of intuition in education.
Consideration requires a discussion of what they say intuition is and what it is not. Based on this
overview,
application of the chaotic paradigm can analyzed.
Nodding and Shores (1984) identify many aspects of what intuition is. It is a form of seeing, a "capacity
that
reveals" (p.53), a feeling of direction, of something at hand. It is separate from cognition (reason)
and Will. It is multi-
functional, it: operates momentarily and intensely; turns perception inward upon the objects of conception
where it
gives objects to the analytical side and to Will for further contemplation; gives direction to experience;
contacts
objects directly in phenomena. Will controls our access to our receptivity to intuition, a receptivity
which requires
that we "put aside the urge to control and impose" (p.54) but it is not part of our unconscious
mind, that is "we
cannot catch ourselves in the act of intuiting for intuition is consciousness supreme and therefore
unanalyzable" (p.
55)
Intuition is also not accelerated unconscious analytic thinking which the authors refer to as the nonserious
view of
intuition, a view which would cause problems for a reasonable definition of inference.
To draw an inference, is precisely, to make conscious connections between prior statements (premises)
and
conclusions (inferences). The view is particularly unsatisfying when we consider the affect that accompanies
intuitive accomplishment. Why should the intuitive conclusion come to us with the surprise, clarity
and beauty of
perception if it is merely a rapid version of reasoning? Why should we so often find ourselves inarticulate
when the
intuition comes to us?" (p. 50).
We can use chaotic dynamics to theorize some answers to these questions. Gardner (1984) postulated that
our
brains housed many forms of intelligence of various degrees of development, and though his conception
emphasized more their autonomy, interaction between his divisions is not denied. In a more recent summary
of the
brain, Miskin, Mortimer and Appenzeller (1987) review a highly interactive nonlinear brain in which
all divisions are in
rapid and simultaneous contact with other divisions. From this and the previously discussed research
on brain
activity, I choose to view the brain as a vast sea of turbulent activity whatever the divisions, but
as Prigogine noted,
so sensitive that the slightest impulse is sufficient to generate order and thought. Our will is sufficient
to direct
many important aspects of thought processing, but the dynamic parallel processing brain can, like soliton
formation
in mid-ocean produce or generate perceptions of great power and duration, which Will could choose to
ignore, but
contrary to the account of Nodding and Shores could crash in unbidden even against our Will and unattached
to
chains of reason. This account provides some explanation for the surprise and inarticulateness that
can
accompany the arrival of intuitive thought. It is certainly not a mystical account, but it does not
deny mystic and
religious use of the term intuition to represent revelation and enlightenment.
Nodding and Shores then are quite right to separate truth and knowledge. Intuition can provide us with
knowledge,
and increase the liklihood of it being truth because it is not filtered by the biases of will or the
faulty logic of reason.
But it cannot guarantee truth, for the brain can only build nonlinear interactions based on the ideas,
constructs and
so forth that the brain has gathered. As they say in computer science, GIGO, or garbage in, garbage
out. But the
potential here is significant, for our chains of logic seem to be bound by strict limits to the number
of competing
concepts we can entertain in our chains at any one time. But the nonlinear interactions of that turbulent
neuronal
sea are not so constrained and can mix a far greater number of wave forms in creating intuitive solitons
and
perhaps other dissipative structures.
The chaos literature also raises the concept of chaos as health and the concept of self- organization
as a superior
mode for processing information. In this light, heavy use of rational inference and deduction driven
thought could be
seen too a degree as mentally unhealthy and limiting to the best of our abilities. This is so because
the view of
intuition is one of being open or receptive, not directive so that it is therefore encouraging nonlinear
thought by not
letting linear operations dominate.
So far, chaos theory has been integrated with educational concerns in a variety of ways: with the major
tenets
(holism and self-organization) of the alternative paradigm of the reconceptualists; with a major strand
of curriculum
theory (the developmentalists), with a recent approach to cognition (intuition). Finally, it will be
integrated with a
debate in the field that studies instructional design and the means for delivery, in some places called
educational
technology. As before, such integration considers central aspects of the area and the critique and adjustment
that
the chaos paradigm involves.
Instructional Design and Technology The field of instructional design and technology is facing a similar
paradigm
debate as the field of education at large is facing. This field also has its status quo and its reconceptualists.
In this
field also, theories of the reconceptualists would support tenets of chaotic dynamics and chaotic dynamics
would
suggest extensions to concerns addressed in that dialog.
The status quo in instructional design and technology (IDT) represents a belief system that claims that
the ends in
curriculum design can be determined, that objectives can be developed and measured, that delivery systems
for a
hierarchy of prerequisite skills are available to teach what needs to be taught in accordance with the
design and
objectives and that computer technology is the delivery system of choice both inside and outside of
schools. The
status quo is represented by the recent publications of Reigeluth (1989), Heinich (1988) and Merrill
(Shore, 1989)
and to an extent Damarin (1988).
The reconceptualist case could be represented by Apple (1987) and Streibel (1986, 1988, 1989). A central
claim is
that curriculum means and ends cannot and should not be so determined. However, the technology (especially
computer technology) is deterministically bringing about negative social consequences including the
destruction of
jobs, deskilling of labor (and teachers and learners) and computers exacerbate the lack of social opportunities
for
the poor (Apple). They also have other negative consequences for education, reducing intelligence by
restricting
student agency. That is, it is the deterministic nature of computers to bypass, narrow, impose and constrain
the
learner (Streibel).
The chaotic paradigm supports the reconceptualist position that the goal of predictability in IDT has
serious
difficulties, both for the larger question of curriculum design and the more personal question of the
learning event
and the appropriate educational individualized response. But just as chaos theory rejects the belief
in curriculum
determinism of IDT, both as a possibility and as personal and social benefit (that is even if it could
be so
determined, it would be unhealthy) so it consistently rejects technological determinism and concurs
with IDT that
computers should play a significant and active role with learning. Further, allied with the larger concerns
of the
paradigm, the chaotic paradigm would support any and all aspects of computers that increase interactions
so that
new forms of self-organization and turbulence will appear.
In this vein, chaos theory would integrate the conceptual work of artists using computers. Their experience
makes
an important contribution to this debate and argues strongly for the further integration of the inquiry
of both art and
science. Pope (1988) not only argues for the expressive potential of computers but notes that the computer
is
revitalizing persistent ideas in the history of art that have lacked an appropriate medium until now:
kineticism,
participation and cybernetics. But the questions raised here go deeper than the potential for new art
forms. They
touch on the capacity of humans to reject that which is not beneficial and the capacity to reinvent
if given the
opportunity. Part of the problem is that the reconceptualists taken by technological determinism underestimate
human capacity. The problem is not with the technology, it is with the inadequacies of the paradigm
employed. A
deeper look at artistic perception better reveals the location of the problem.
The criticism that technology, especially computers, deterministically forces design and use and therefore
is
suspect would have a different reception among artists who consider the computer their expressive medium
and
perhaps among artists in general. For to make such a criticism is to locate your level of development
in the stages
of apprentice, journeyman and master. From the art perspective it is understood that all mediums have
limitations
and restrictions whether clay, paint, ballet or the trumpet. There are skills to be learned, concepts
to be explored
and mastered, limitations to be faced and perhaps overcome. But the status of art and of the artist
comes not from
pieces dictated by the medium, but by artists of sufficient ability to produce work that rises above
and yet through
the medium, artists that make innovative and unique contributions linked intimately to the needs of
the spirit and
time in which they are made and yet, make visible enduring aspects of what it means to be human.
This is of course not so harsh a criticism for a field to say of itself that it is not yet capable,
that it is not beyond the
apprentice level. But for a field to be constrained from creative acts by its technology and to so indicate
to others
that such is the case is to announce that the field has reached the end of its time and its issues should
be passed
out to other fields for further pursuit. Chaos theory allows a different emphasis, one that would revitalize
computer
utilization. It allows for the computer as an instrument capable of increasing interaction in numerous
ways, ways
that extend human agency and expressiveness. It refutes equally the curriculum determinism of the status
quo and
the technological or computer determinism of the reconceptualists.
The Taxonomy Revisited At the end of chapter three, the taxonomy contained a simple symmetrical arrangement
with three categories of each branch of system behavior, convergent and divergent. The former derived
from linear
mathematics and the latter from nonlinear mathematics. Further, disorder grew from left to right, from
convergency
to divergency. Order was simply less disorder. The picture is now not so simple in chapter four.
Disorderly nonlinear chaotic dynamics, in the more complicated view in chapter four, can now also produce
patterns of convergency, e.g., the periodicity and torus forms of a soliton and eventually dissipate
in the manner of
fixed point systems. All the prior convergent categories are not replaced however, as they arise from
a different
source of explanation. Even more curious, the branches of the taxonomy interact on the nonlinear level,
with the
increasing complexity of life building on layers which transpose from convergent to divergent categories
and back
again, layers which thrive in feedback to each other. We have come a long way from Krathwohl's models
of cause
and effect. But we have borne out Krathwohl's hunch that attention to cause and effect is important,
for pursuit of
their ramifications suggests a new paradigm for educational inquiry.
Inquiry Three concerns conclude this chapter and this thesis. They pertain to the label for this paradigm,
a
resolution to the two cultures debate and prescriptions for educational reform.
The question of an overarching label for this paradigm was raised previously. Is "chaotic"
the best description for
this paradigm? It was previously argued that chaotic phenomena were the dominant event in nature and
that the
potential in the turbulence and interaction accounted for the initiation of the structures that exist,
and thereby being
more fundamental, deserved to be the labelling flag. But if the criteria was the development of identifying
terminology that represented values for education suggested by the chaotic paradigm, then the goal of
chaos, is
inappropriate and one-sided. Education would find no value in chaos for chaos's sake. Education requires
both
chaos and self-organization to explore the parameters of human potential. But aspects of both what chaotic
dynamics manipulates and how organization "becomes" are significant to educational values.
A selection from these values seems a better source for the term to communicate the meaning of this
paradigm for
education. Chaotic dynamics passes information from one variable to another and in doing so changes
the variables
and thereby creates more information. Information manipulation is the grist for the educational mill.
On the other
hand, the concept of becoming is about process, but in chaotic dynamics a process of building that comes
not
from advance planning, but from the contingencies of the present, a process that I would call emergence.
To call
the paradigm the information paradigm would highlight concern for communication and the manipulation
and
creation of information. Yet the phrase does not convey the dynamicism of the paradigm. The better fit
is the
phrase the emergent paradigm, (see figure 4-7. the emergent paradigm). Yet, chaotic dynamics emerges
as a
phrase that resonates with the social role that educators must fill, generators of a dynamic that allows
individuals to
develop to their fullest in an increasingly turbulent and divergent world.
A second issue arises in this summation of theory and research surrounding this now titled emergent
paradigm. In
the process of applying the radical model of chaotic dynamics to a reasonable integration with educational
theories,
the reasons for a major division in human inquiry and curriculum organization lose their relevance.
The chaotic
paradigm implies that as science's ability to falsify has decreased another giant step, science should
give greater
emphasis to its creative side, the stage, that as Krathwohl said, is prerequisite to any effort to confirm
or falsify.
This suggests the vanishing of the chasm between science and art. The physicists Bohm and Peat have
proposed
(1987) that science move closer to art from the perception that "scientific truth, like artistic
truth, is a matter of
endless nuance... [and that] theory is an abstraction of the whole and therefore is in a sense, an illusion"
(Briggs &
Peat, 1989, p.200). This illusion is an important inoculation of irony for both scientific and artistic
inquiry. In the
chaotic paradigm, then, art and science lose one of their common distinctions. But there is a second
aspect to
this. The falsification of the rationale for specialization and reduction requires that any area of
inquiry cannot ignore
any other. As a consequence, essential to formation of the larger holistic view, art and science must
integrate their
programs. This development does not foreclose experimental work, destroy statistics or end academia
as we know
it, but rather will put them to different ends, predominantly creative ends.
As interdisciplinary study cuts against the grain of the general organization of curriculum and instruction
in
American education, reform along interdisciplinary lines poses special challenges for educational reform.
The study
of chaotic dynamics serves as case in point. The problems and needs of interdisciplinary study of nonlinear
and
chaotic phenomena have been reported in conjunction with the National Academy of Sciences and other
national
policy groups (Feigenbaum et al., 1987). Among their several recommendations, there should be greater
international contact with stronger nonlinear programs of study abroad, especially with the French and
the Soviets.
Typical United States universities are often rigidly defined along traditional disciplines and lack
the proper flexibility
to handle such study. Also, institutions should create more focused centers for such study. "In
any effort to guide
this research, however, it is imperative that nonlinear science be recognized for what it is: an inherently
interdisciplinary effort not suited to confinement within any single conventional discipline or department"
(Feigenbaum, 1987, p. 54). Hence administrative arrangements will need more attention. The reward for
participating faculty is a "remarkable breadth of application and the potential to influence both
our basic
understanding of the world and our daily life" (p. 54). The chaotic paradigm of this thesis provides
an opportunity for
educators to offer leadership for the study of phenomena of great interest to the field of education
and to all
disciplines from the field of education which by its very nature has many interdisciplinary needs and
interests.
Finally, certain prescriptions for research and practice appear to follow from the emergent paradigm
theory,
prescriptions that suggest further areas for research and test. From the concept of the butterfly effect,
it follows that
highly sensitive human environments would benefit from explicit instruction in ethics, supporting Lincoln
and Guba's
contention "that it is possible to shape affairs in a desired direction, albeit with a good deal
of uncertainty" (1985,
p.151). From the fractal structure of self-organizing systems (Barnsley, 1986), it follows that educational
assessment that seeks to map the edge of an individual's knowledge should seek a fractal edge with its
expected
discontinuities. But perhaps the principle conclusion derived from chaos theory and the larger emergent
paradigm
for education is simple and basic: interact. To interact is a fundamental directive for education as
a system and the
individual as a learner. But to simply interact is insufficient in itself. There are certain qualitative
levels that must be
reached. Taking to heart the assumption of chaotic dynamics as health in a system, a healthy educational
program
stimulates interaction into the chaotic regime. A principle contribution of the industrial paradigm
for professional
practice has been the development of hierarchy, which is a principle tool for conservation. Hierarchy,
the
organizational pyramid, is inimical to meeting the needs of a rapidly changing world. It is a fundamental
roadblock
to reaching the chaotic regime in education and learning and must be destroyed. Further, in those systems
that
have emerged from the chaotic regime, those with the longest survival rates are those that have retained
an intimate
link with the level of chaotic dynamics beneath them. Educators must find and use the many tools and
networks
now available that will enable them to stay in touch with and contribute to chaos and self- organization.
Emergent education and the adoption of the chaotic or emergent paradigm await the march of time, but
their
metaphors in particular provide motivation and hope for their elaboration. To believe in emergent education
and the
infra-structure of chaos theory is to believe in the butterfly effect metaphor, that a single individual's
simplest
gesture has the potential to cause significant change even beyond the horizon. No perturbation is too
small to be of
influence (Ekeland, 1988). To accept self-organization metaphors like the tidal bore or the giant red
spot of Jupiter,
is to believe that the collective force of individuals acting in light of their own eigen-value, or
personal values can be
enduring and powerful. The former contrasts with the vanity of individual effort in the face of the
overwhelming
averaging effect of central tendency. The latter connotes that significant collective effort does not
require central
authority. Both raise the value and status of teachers and learning.
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1. Introduction and Background
The rapid evolution of Information and Communication Technologies (ICT) and the emergence of the Information
Society create numerous new opportunities for the improvement of the quality of education. Especially
the Internet
and the WWW facilitate mouse-click access to enormous information repositories, and learning material
and
services, providing an unprecedented potential for education and training. On the other hand, it can
be argued that
education has not yet realised the full potential of the Information Society: “there is a shortage of
solid evidence to
back up the belief that telematic learning systems provide real advantages” (European Commission, 2000).
Besides
the apparent benefits for delivering education to distance learners independently of time and location,
several
studies question whether there is a “significant difference” with respect to learning effectiveness
when ICT is
employed in education (Russell, 1999; Gilbert & Han 1999).
Personalised learning (PL) is widely considered as one promising direction towards the full exploitation
of the
potential of the Information Society in education (Cronbach & Snow, 1997; Corno & Snow, 1986;
Sararin, 1998). PL
advocates that it is the learning context that should be adapted to the individual learner, as opposed
to “traditional”
learning settings, where it is the learner’s responsibility to adapt to the learning context in order
to maximise the
“learning outcome”. Along the same lines, personalised (adaptive and intelligent) learning environments
have
attracted considerable attention, since they constitute the “enabling technology” which can realise
the PL concept
(Park, 1996). Despite the intensive R&D efforts in this field, however, PL technology – intelligent
tutoring systems
(Shute & Psotka, 1996), adaptive educational hypermedia (Brusilovsky et al, 1998), intelligent pedagogical
agents
(Johnson et al, 2000)– is still far from being mainstream (Brusilovsky et al, 2000).
One of the most important reasons for this fact, and the “under-exploitation” of ICT (and especially
the Internet) in
education, in general, is that the information available in the Internet is generally “unstructured”.
There are several
on-going international efforts for the development of standards for the description of the (educational)
information
available in the Internet (towards the “semantic web”): one can easily describe, for example, the structure
and
contents of learning resources through educational meta-data, content packaging information, etc.
On the other hand, it is still not feasible to describe the conditions that determine which part(s)
of the educational
material are appropriate for different learner characteristics. As a result, and also due to the limitations
of currently
available searching technologies, educational applications retrieve the same material for all learners
(or, for the
“average learner”), without taking into account the diverse needs of each individual learner (e.g. previous
knowledge,
background, skills, preferences). Therefore, the material available at the Internet cannot be fully
exploited, and thus
the Internet is under-exploited from an educational point of view (i.e. Internet content is not “educationally
useful”),
both for “formal” curriculum-based, as well as in self-directed, “informal” educational settings, vocational
training,
and other learning settings.
This paper addresses the problem of automatically integrating adaptive content into different courses
and curricula,
thus exploiting the potential of the Internet in education. In the context of this paper, “adaptive
content” refers to
learning material that can be adapted to the requirements of different learners. This is to differentiate
from the way
that the term “adaptive technologies” is used in the USA, where it usually connotes those technologies
that are
used to assist people with handicaps to use computers, and include screen enlargers, pointing devices
and other
items.
The paper focuses on the work of the KOD “Knowledge on Demand” European project (see acknowledgements
section) in this context. The KOD project is working on developing methodologies, techniques and tools
for
developing adaptive content in an interoperable and interchangeable format, so that is can be easily
transferred
across different Internet-based personalised educational applications and services. The aim is that
(potentially any)
content available in the Internet can be directly, effectively and smoothly integrated into different
educational
settings.
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NECSI Activities & Events
PLANNING DOCUMENTS FOR A NATIONAL INITIATIVE ON COMPLEX SYSTEMS IN K- 16 EDUCATION
TWO ROLES FOR COMPLEX SYSTEMS IN EDUCATION:
MAINSTREAM CONTENT AND MEANS FOR UNDERSTANDING THE EDUCATION SYSTEM ITSELF[1]
Report on Project #REC-9980241
Jim Kaput, Yaneer Bar-Yam, Michael Jacobson, Eric Jakobsson, Jay Lemke, Uri Wilensky, and Collaborators
Context for the Preliminary Inquiry
The educational systems of advanced societies are highly complex, consisting of many components that
interact
at multiple layers of organization and at different time-scales. The multiplicity of these components
and of their loci
of control, the diverse nature of the stakeholders--who range from students and their parents to their
elected
representatives in cities and towns, states and the Federal government--the richness of interactions
among these
groups, are all essential components of the system’s functioning and must be part of any attempts to
support,
reform or improve it. In their complexity, education systems are similar to other social organizations,
and in fact,
share aspects of interaction among components with most physical systems of global importance.
The integration of ideas and methods from many disciplines into the study of complex physical and social
systems
is indeed generating excitement among scientists, policy- makers, and segments of the general public.
Concepts
and methods enabled by rapid advances of information technologies are enabling us to understand aspects
of the
real world where events and actions have multiple causes and consequences, and where order and structure
co-
exist at many different scales of time, space and organization. Within this complexity framework, critical
behaviors
that were systematically ignored by classical science can now be included as essential elements that
account for
many observed aspects of our world–for example, global phenomena that require multiple physical, biological,
social, and mathematical perspectives.
Education researchers and policy makers have only recently begun to explore how researchers from other
fields
approach the study of complex systems and how to manage complexity for productive purposes. The number
of
systemic reform efforts funded by NSF, coupled with other experimental large-scale reform projects funded
by the
Department of Education, suggest that the time is ripe to consider how to explore the complex nature
of the
education system as an object of study, since data to move such studies from theoretical discussions
to grounded
frameworks of analysis that could be used in a predictive manner is now becoming available.
These arguments led a diverse group of scientists (physicists, chemists, biologists, psychologists,
sociologists,
mathematicians, computer scientists) and educational researchers to propose to NSF a series of meetings
to
define a common ground that could be used to generate researchable ideas for integrating the field of
education
research with advances in the study of complex systems in other disciplines. The group first met in
June, 1999 and
then in May, 2000, to begin to examine the implications of these deep changes for education – in content,
teaching, learning and cognition, and for understanding from a fresh perspective that most complex of
systems, the
educational system itself. This document summarizes aspects of the discussions, and its appendices
examine
these ideas in more depth, with the fundamental realization that we are at the beginning of an inquiry,
where
questions far outnumber answers, and where exploration of the nature of the questions is itself an important
topic of
inquiry.
The preliminary analysis proposed to NSF identified the two roles for complex systems concepts as focus
of
deliberation. One is complex system concepts as part of content for mainstream education and the other
is
complex system methods and perspectives as means for the analysis of education itself.
The groups addressed two broad types of questions surrounding the first focus, complex concepts as
part of the
content of education: (1-a) What kind of complex-systems concepts are important, for whom and for what
purposes? (1-b) What is already known about the teaching and learning of such content? Questions regarding
the
second role (2), the education system as an object of study, centered on how to characterize the multilevel
education system as a complex system, and what questions could be posed to probe its workings. The
Appendices to this report present a summary of the deliberations of the three groups formed during the
meetings.
To simplify this document the ideas around pedagogy discussed in group 1-b are integrated with the discussion
on
content 1-a.
We offer the following executive summary with full understanding that the hard work lies ahead, and
requires the
collaboration of many other contributors with wider expertise. This report does little more than set
the stage for why
such work should be undertaken, and ends with some suggestions on how this work might commence.
Why Complex Systems?
The motivation for this inquiry arises from the insights obtained from the applications of the new ideas
of complex
systems that are now appearing in mathematical, physical, biological, and social sciences. Indeed these
applications are being integrated into the working conceptual framework of many professions (e.g., engineering,
medicine, finance and management) as well as sciences. For example, interdependence and co-evolution
in
ecosystems, with emergent patterns formed by self-organization, are now seen as equally important as
competitive
selection in understanding biological evolution. Similarly, interdependence and self-organization in
social systems
now informs corporate managers' thinking about their employees as well as the relationships among corporations,
who then seek synergistic alliances as well as a simple competitive edge. The success of genome mapping
has
now led to both the need and opportunity to develop an understanding of functional genomics, in which
groups of
genes—rather than isolated genes--are seen as functional units, such as metabolic or signaling pathways.
Some
medical researchers are beginning to refocus on both the interdependent biological systems and the psychosocial
context of an individual as an integrated system, an area of high importance to educators. The coupling
of
meteorological, oceanic and human actions in the understanding of such phenomena as El Niño weather
and global
climatic change is playing an increasing role not only in meteorology, but also in government policy
and in financial
decision-making. The widespread interest in complex systems strategies has been intensified by the manifest
complexity of the global economy and society, and has been accelerated by the growth of the Internet/WWW
and
the diversifying and decentralized opportunities for communication and collaboration in the day-to-day
lives of
citizens and organizations of every scale.
The conceptual basis of complex systems ideas reflects a change in perspective about our world that
is important
for students to develop, as it corresponds to the scientific environment that will exist when they graduate.
This
perspective emphasizes both the limits of predictability as well as the possibility of understanding
indirect
consequences of actions taken, both positive and negative, through modeling the interdependence of our
world.
While complex system-related concepts are embedded in school science, they are not identified or exploited
for
their unifying capacity across disciplines. Concepts such as multi-scale hierarchical organization,
interdependence, emergent patterning, agent-based modeling, dynamical attractors, deterministic chaos,
information flows and constraints, system-environment interaction, developmental trajectories, fitness
landscapes,
and self-organization are becoming key tools for qualitative reasoning about real complex systems as
well as
quantitative modeling and simulation in the contexts of synthetic systems.
Except for the education of a few highly trained specialists in a few scientific areas, little of the
conceptual power
embodied in the rapidly developing tools and perspectives of complex, dynamical systems or informatics
has
reached the educational experience of our citizenry at any level. Moreover, to the extent that these
tools and
perspectives remain largely absent from educational decision-making and ongoing efforts directed towards
education improvement and leadership development– in content, teaching, learning, curriculum structure,
assessment, teacher education and credentialling, management, financing, and the many other factors
involved in
educational improvement, systemic and otherwise– their absence from mainstream education, and the negative
impact of this absence, are destined to continue.
The authors share a perspective that this state of affairs will need to change as the impact of changes
in science
become integrated into the fabric of social life and are moved to ask what changes might be needed and
what new
understandings might help us now to know how to achieve them. To this end, we addressed issues associated
with complex systems as educational content, and those related to complex systems as an analytic tool
set for
the study of the education system:
1. (a) What goals in content relating to complex systems might be appropriate for mainstream K-16
education,
both in schools and outside schools? and (b) what do we already know and what do we need to know about
teaching and learning of this kind of content, including uses of new technologies?
2. How can the perspectives, analyses and tools of complexity itself inform the process of educational
change and
improvement– what are good questions to ask to begin the analysis?
The study of complex systems involves experimental, computational, and theoretical approaches for observation,
analysis, and modeling (including dynamical simulation). The interplay among model predictions, analysis
and
observation is crucial for gaining useful insights into the systems studied, and for gauging the usefulness
of follow-
up activities. And in the service of understanding the education system, relatively recent‘systemic
education
experiments’ and the data they are accumulating provide, for the first time, the possibility of using
observational
data to test preliminary predictive models of the education system as a complex system.
Three working groups were formed to produce three starting-point documents intended to lay out shared
understandings and beliefs and to outline the larger questions that need further investigation. These
three working
papers accompany this Executive Summary as Appendices. A bibliographic resource was assembled that
documents prior and ongoing work in the field of complex systems in education and is included as a fourth
Appendix. All materials are available at http://www.necsi.org/events/cxedk16/cxedk16.html
The remainder of this Executive Summary recaps the working papers and closes by suggesting next steps,
including calls for the formulation of a series of research initiatives to begin to answer the questions
elaborated in
the papers, and the possibility of follow-up national conferences.
Key Questions Regarding Complex Systems Content in the Mainstream Education of America’s
Citizens: What
Content for Whom? and What Do We Know About Teaching and Learning That Content?
Across many domains, concepts derived from a complex systems perspective provide organization to the
otherwise
bewildering properties of diverse complex systems, help create a common framework and language, and
help frame
the use of dynamic and often highly nonlinear simulations to test predictions and hypotheses. Indeed,
the
application of these concepts in conjunction with the application of computational models and visually
compelling
data-driven simulations yields unprecedented means for understanding complex phenomena and relationships,
revealing new, sometimes counter-intuitive patterns and contingencies. Such understandings lead to asking
new
and essential questions and viewing situations from new perspectives. This common framework and language
of
complex systems also appears to be critical for the ability of individuals and groups to use knowledge
and
techniques across very different contexts.
Complementing the power of concepts associated with complex analysis and dynamical modeling (such as
feedback and multiple causality), is the power of informatics in understanding complex systems. These
techniques, that include pattern matching, parsing and tree construction, for example, reveal relationships
among
elements of complex systems in ways complementary to dynamical understanding. These techniques are a
direct
result of the very recent advances in the integration of computing and science. Their potential role
in education
needs to be studied so that students will be ready for the world in which they will live, and not for
the world in which
we, the Report’s authors and readers, studied.
Knowledge of complex systems rests on three foundations: Experiment and observation in the real world;
use of
computation for modeling and for information search and analysis; and underlying theory about the time-dependent
properties of nonlinear systems. It is important for students to understand the concepts of feedback
in general and
adaptive feedback in particular, and their consequences for the dynamic evolution of systems. The fact
that real-
world systems have multiple causes and effects and are open to exchange of information, matter, and
energy with
the rest of the world has consequences that students of all appropriate ages would greatly benefit from
understanding. We wish students to learn how all of these aspects of systems emerge from the properties
of the
system components and their interconnections, and what aspects of particular systems govern what aspects
of the
behavior of those systems. It would also be very useful to learn how to distinguish between general
properties of
complex systems and those features that are specific to the system under consideration.
Several different types of computational modeling styles and techniques should be introduced in appropriate
context—calculus-based differential equations, difference models, random-walk or stochastic models,
and active-
agent models and cellular automata, etc.; also randomness, deterministic vs. non-deterministic chaos,
thresholds,
sensitivity to initial conditions, periodicity, different kinds of orbits, attractors and repellors,
and, most importantly,
how to interpret the many kinds of phase-space representations used to describe complex dynamical systems.
Students, at an appropriate age, and through the use of observations and appropriate technology can
come to
understand information flows and constraints, multi-scale hierarchical organization, system- environment
interaction, developmental trajectories, selectional ratchets, fitness landscapes, and other conceptual
tools of
complex systems. There is also a need for exposing students to the power of informatics through appropriately
constructed computer exercises in pattern matching and data analysis. The WWW can thus be conceptualized
and utilized as a complex system of information transfer, of personal and institutional interaction,
and a medium in
which modeling is done.
As a consequence of working with these concepts, and through serious, sustained experiences in modeling,
argument and justification beginning at an early age, students can learn that effects do not usually
have single
causes, and that causes may be either direct (primary) or indirect (secondary). They can develop experientially
based intuitions for the power and, most important, the limitations of systems modeling, simulations
and
information analysis as applied to understanding real-world complex systems. They can likewise develop
a feeling
for the complementary use of informatics to cope with those aspects of very large-scale systems that
are beyond
the scope of detailed dynamics modeling. These basic intuitions are as important for making personal
decisions in
life and civic decisions in a democratic society as they are for technical work in science, engineering
and the
professions.
Recent research in student learning has shown that many central concepts of science in general, and
of complex
systems and nonlinear dynamics in particular, are challenging for most students and are often counterintuitive
or
conflict with commonly held beliefs. For example, most people expect a linear relationship between the
size of an
action and its corresponding effect, which conflicts with the sensitivity to initial conditions that
is commonly found
outside the classroom. Similarly, people tend to favor explanations that assume the existence of central
control
(e.g., the flight of flocks of geese) and deterministic single causality, even in cases where such a
central control is
impossible (e.g., traffic jams). People do not understand the mechanisms for feedback, and thus exhibit
deep-
seated resistance towards ideas describing various phenomena in terms of self-organization, stochastic,
and
decentralized processes. Similarly, delayed feedback and differing time-scales often require intuitively
unexpected
timing of actions, e.g., actions to slow an economy or growing system long before the expected need
for slowing–
or vice versa.
More significant for the future, however, is the fact that almost all the prior research on student
learning involved
studying students being educated in traditional ways towards the scientific perspectives that are changing
with the
advent of technology. A major initiative is needed to determine how younger students can learn these
ideas over an
extended period beginning at an early age with appropriate experiences and instruction. It will also
be important to
determine what are appropriate learning experiences and instruction activities and to define curricular
options, both
within and across subject matter domains. Of particular interest is determining how to integrate traditionally
important topics as well as new topics in ways that are broadly implementable. In doing that, we seek
to use
complex systems ideas and methods to unify and render more coherent students’ educational experiences
in the
same ways that these ideas and methods are helping unify and organize knowledge across traditional boundaries.
It should be noted that many of the ideas we seek to integrate into formal education are at the core
of activities,
such as computer games, for which young students have developed informal and intuitive understandings.
Emerging theories of learning and thinking, themselves informed by complex systems analysis of individual
and
group interactions, will play an important role in designing and researching new means by which new
complex
systems content may be learned. Such theories integrate the roles of context, experience, and active
engagement
of learners, as individuals and as members of collaborating groups. In addition, new computational
environments,
especially computer-based models and simulations explicitly related to (or data-linked to) physical
systems –
particularly physical systems that are systematically manipulable and experienced by students – provide
opportunity to build qualitative and quantitative understanding of key ideas. New multi-person“participatory”
simulation environments enable students to act out roles within a simulation and thus to make experientially
grounded connections between the individual micro-level and the population macro- level of phenomena.
Such
modeling and simulation environments also enable instructional designers to render important concepts
and their
relationshipsexplicit, to control novelty and levels of complication, to vary context systematically
while holding
underlying structure invariant, and to overcome limitations of scale in space and time, particularly
to enable
observation and control of self-organizing and long-term emergent phenomena that require large numbers
of cycles
to become manifest.
All the above research and development agendas must be pursued systemically within a broad educational
context,
including how teachers, both pre- and in-service, learn science, how new materials and curricula can
be developed
and implemented in pedagogically significant ways, and how assessments can be produced that document
learning in compelling ways consistent with the larger political expectations regarding accountability
and standards.
How Can Complex Systems Help Us Understand the Systems of Education and Their Change?
While the U.S. educational system can be narrowly defined as the system of public and private schools
and
colleges that offer students formal education from pre-kindergarten to college graduation and beyond,
ultimately the
system must be defined by its dynamics of interaction. Which institutions and social practices, which
sources and
users of information and material and human resources are tightly enough coupled and interdependent
in their
behavior that they must be included within the system? Likewise, what are the ranges of time scales
characteristic
of the critical processes that enable the system to maintain itself? What are its significant levels
of organization,
not simply or primarily in terms of lines of authority (control hierarchies) or in terms of political
geography, but in
terms of characteristic structures and characteristic emergent processes and patterns at each level?
What kinds of
material resource and information flows connect adjacent and non-adjacent levels? How is information
transformed,
filtered, re-organized, and added to from level to level? How is information-overload avoided by emergent
systems for
pattern-recognition that extract from large data-flows only what matters for the dynamics of the next
higher level?
Formal organizational hierarchies offer one starting point for identifying levels within the core educational
system:
What would a dynamical analysis propose in terms of different time scales, and what would the units
of analysis
be? How do brief actions by teachers and students add up to coherent activities over periods of days
and months?
How do curriculum change processes that occur over periods of years exchange information with classroom
activities that occur over periods of minutes? How do learning events in a laboratory or at a computer
workstation
and in classrooms and hallways add up to a coherent longer-term process of development of facility with
a
particular concept?
Whatever level of organization or subsystem is the focus of our concerns at a particular point, we can
always ask a
series of key questions motivated by the highly general perspectives of complex systems theory. What
next higher
level of organization determines constraints on the dynamics at the focal level? How do all subsystems
subject to
those constraints interact to constitute the dynamics of the higher level? What degrees of freedom remain
at the
focal level after the constraints are allowed for? What units of analysis at the next level below interact
to constitute
units (or processes or patterns) at the focal level? What characteristics of those lower level units
(attractors)
determine the range of dynamical possibilities at the focal level. How do new attractors emerge over
the history of
the system's development and its evolution? What manifolds describe the conditions on the range of values
of all
other parameters that must be met to achieve some value of the parameter of interest?
At a given level of organization, how are the different units and processes coupled with one another?
What kinds of
information do they exchange? What are the significant branchings, closed loops, and connectivity
decompositions? What is ‘system’ and what is 'environment'? And how do system and environment form
a
supersystem from the viewpoint of some still larger-scale unit or process?
How is the educational system as a whole driven by external events and pressures such as advances in
scientific
understanding (e.g., changes in content or in understanding of human learning), the increasing complexity
of
problems addressed by communities and societies, changing technologies, and public demands for reform?
How is
educational change enabled by bringing new kinds of people into contact with one another? How would
educational
processes be affected by creating new feedback loops, such as research data, which systematically describes
outcomes, back to teachers, students, and parents? How might new educational institutions (e.g., charter
schools,
online systems) create niches for themselves in the educational ecology, and how do they effect changes
in the
formal system? Or how do new spontaneous networks, such as online communication groups of teachers within
a
school or across the country, affect the rate of educational change?
We need to learn how to model and analyze issues like these using the concepts and techniques of other
fields
that are exploiting complex systems thinking. Such fields range from ecosystem theory and developmental
biology
to parallel distributed computation and informatics and infodynamics, among others. Today, how well
could we
design a simulation program to enable us to create alternative systems and study their evolution over
time, their
needs and problems, their probable outcomes? And how would we as a society evaluate various designs
proposed
by others? Does data exist that could be used to test such a model of existing systems? Is such data
possible?
What is not yet known that would be needed to make such a project credible?
The above perspectives offer insights into opportunities, critical points for systemic change, and identify
strategies
that are unlikely to yield change. For example, it is a common phenomenon in complex systems that system
behavior is limited when some elements are decoupled from others and interactions that might otherwise
be
expected to occur are blocked or diluted. We have rigid segregation between grades, and usually between
subject
domains, and, most importantly, between teachers and researchers, between schools and their communities.
These are examples in the present educational system, and each offers an opportunity to unleash educational
improvements by providing new channels for introducing change agents. How can new technological connectivity
change these conditions, and what might prevent it from having an impact?
Complex Systems Education and Education Research: Possible Next Steps
Many sources of expertise need to be engaged in building new complex systems content for education and
for
studying its learnability and implementability. Scientists from many different areas need to collaborate
with
educators, education researchers and materials developers (including technology developers) to help
define new
science education goals, appropriate curriculum, technology and pedagogical options for formal and informal
education. Setting the stage for developing research and action agendas on a national scale, including
national
dialogues presumably supported by appropriate conference gatherings, is a next major step.
Similarly, many sources of expertise need to be engaged in building models and frameworks for a complex
systems analysis of our education system, involving both creators of the knowledge and potential users
of the
knowledge gained:
* Scholars in the fields of testing, assessment, and evaluation. * Historians of education;
ecologists and
ecosystem theorists.
* Researchers who have studied examples of successful and unsuccessful educational
innovations and
schemes for curriculum change.
* Educators and managers with experience of current corporate, organization, and
workplace models of
employee education and human resource development.
* Researchers who work within the context and agendas of professional organizations
in the field of education,
such as teacher organizations, national associations, etc.
* Researchers who work with and are advocates for marginalized and disenfranchised
groups, including the
homeless, the physically challenged, communities with high unemployment, communities with low school
completion rates, etc.
* Researchers who work with private foundations that study and promote educational
change; economists,
political scientists, and sociologists with interests in the role of education in society; science and
education
journalists.
* Researchers in comparative education and those who have studied educational
change in other parts of the
world; researchers who study learning in non-school or informal educational institutions.
* Researchers who specialize in the study of the impact of technology on social
institutions.
* Leaders in higher education who have worked on educational reform and curriculum
innovation in colleges and
universities.
* And others.
Communities of this size and scope do not typically arise by themselves, or, if so, may take too long
to do so. An
important question arises then regarding how to begin. Some major changes in education have had high-profile
beginnings at elite higher education institutions in response to dissatisfaction with the status quo
(e.g., case study
approaches at the Harvard Business School). Others were the result of leadership emanating from a professional
community as occurred near the turn of the 20th century with the origins of criteria for medical school
education.
Others more recently have been the gradual result of extended investments by NSF in standards-based
curriculum
projects in K-12, where the standards were broadly advocated by educators prior to the investment and
which were
informed by research over the prior two or more decades, much of which was funded by Federal agencies
including
the NSF. Some ongoing changes, such as those involving distance learning and collaborative work, are
the result
of new technology affordances. Some widespread changes, such as the strong move to accountability and
assessment, have political origins.
Regarding the development of new content materials and new curricula, we see catalytic promise in the
development of highly interactive, compelling instructional modules with wide curricular breadth and
the potential of
entry at multiple levels of sophistication. Such might be built around a major recent scientific achievement
and/or
issue, such as the development of the early HIV/AIDS treatment cocktail, or perhaps dynamic models of
climate
change. We invite the reader to envision such possibilities. We further suggest that a fruitful next
step may be to
bring together small focused groups to help with the envisioning and design of such exemplars, their
fit with current
education content standards, and the R&D processes needed to study their learnability and implementability.
Regarding the study of education as a complex system, we see a need for a long term and substantive
research
initiative involving multiple and competing perspectives and methods derived from the study of complex
systems in
other domains but informed by members of the communities listed above. We sketch here, as an illustrative
and
tentative proposal, an example of a kind of analysis, with a focus on multiscale analytic methods, but
with no
assumptions that the perspective offered and the first-level intuitive analysis is an accurate, complete,
or even
appropriate, account. Considerable study will be needed to satisfy criteria of accuracy, completeness,
data
availability, and adequacy, required for a study of the sort tentatively proposed.
As previously noted, complex systems have structure and dynamics at many scales. The purpose of multiscale
analysis is to describe the behavior at different scales and the ways they relate to each other, and
the
environmental forces that affect the system. A multiscale analysis approach also integrates and sheds
light on
many other complex systems concepts, including interdependence, emergence, complexity, evolution, adaptation,
indirect effects, spatio-temporal patterns, networks, and environment-system interactions.
An intuitive multiscale approach would identify the relationships between forces and behaviors of the
system at
different scales, and their relationship to functional requirements that the system is expected to perform,
and
opportunities for change. Such an analysis would provide the basis for consideration of a set of problems
of the
education system and various efforts that have been or are being considered for implementation at the
local, state
and national level. Such an analysis should also provide key insights into systemic problems and the
opportunities
for educational reforms that can address them. The authors of this report hope that the call for proposals
in
Quadrant 4 of NSF’s ROLE will provide short-term incentives for researchers to approach in similar manner
the hard
questions needed to jump-start a productive research community.
Recommendations for Continuing Planning Meetings, Research Initiatives and Progress-
Reporting Meetings
We recommend for the next five or more years a continuing series of meetings, supported and organized
by various
agencies and organizations at the Federal level, including the National Academies, the AAAS, the NIH,
with the
NSF playing a coordinating and facilitating role through the formation of a broadly participatory and
high profile Task
Force on Complex Systems in Education. The Task Force would include a small coordinating group to
operationalize its activity. With the support of the Task Force, the meetings would address the two
broad
challenges outlined in this report: Complex systems as new content/organization for mainstream education
of our
citizenry, and complex systems as means for understanding and continuing to improve our education system.
Beginning as soon as practical, focused meetings of deliberately mixed communities of experts of the
sort listed
above should lead to specific research initiatives sponsored by various agencies and funded by mixes
of public,
private and foundation resources. Other larger and higher profile meetings, involving educational leaders
and
leaders of national science and professional organizations that generate and exploit complex systems
knowledge,
should be used periodically to assess and to publicize progress on these twin challenges. Versions
of these latter
meetings might be appended to or integrated with the regular annual meetings of the participating organizations.
[1] The authors gratefully acknowledge Dr. Nora Sabelli’s insight, intellectual support, and editorial
assistance
throughout this project.
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Excerpted from: F. Hesselbein et al. (eds.) 1997. The Organization of the Future.
The Drucker
Foundation.
"Creating Sustainable Learning Communities for the Twenty-First Century"
Stephanie Pace Marshall
Stephanie Pace Marshall is the founding executive director of the Illinois Mathematics
and Science
Academy in Aurora, Illinois. Prior to this, she served as the superintendent of schools in Batavia,
Illinois, and as a member of the graduate faculty at Loyola University. In 1991, she was elected to
head the Association for Supervision and Curriculum Development (ASCD), an international
educational leadership association committed to the success of all learners. In April 1992, she
became its president. In February 1984, and again in 1990, she was selected by Executive
Educator magazine and the National School Board Association as one of North America’s one
hundred top school executives.
Building on discoveries in fields as diverse as quantum physics, chaos mathematics,
evolutionary
biology, neuroscience, cognitive science, and systems theory, revolutionary insights about the
universe, the natural world, and human learning have all converged into a new understanding of how
human systems continue to grow, evolve, and learn (or change). Although these disciplines seem
either remote from or irrelevant to designing and leading twenty-first-century organizations, the new
learning they provide allows us to reconceptualize the language and professional discourse of
organizational learning and leadership and to discard the cause-and-effect mental models that have
grounded them in the past. Behavioral algorithms do not govern the dynamics of organic living
systems.
To prevail in the face of the disease and disorganization of the twentieth-century
workplace requires
new organizational forms, new visions of leadership, and new metaphors for organizational growth
and change that are grounded in the hopeful and enduring characteristics of the natural world, the
human spirit, and the brain itself. Although this chapter focuses on the principles and conditions
governing the creation of robust, resilient, and dynamically sustainable learning communities within
the structures currently called schools and classrooms, they apply to any learning enterprise.
Every organization must become a generative learning and teaching community if it desires to
enhance the fullness and diversity of human capacities.
All around us we see evidence that we are in the midst of a cultural transformation,
fueled by the
recognition that the competition, independence, and isolationism of the past cannot elevate the
capacities of the human spirit that will energize and guide us in the next phase of our development
as we create new ways of being together in the world. Human interdependence, not independence,
will be the foundation for a new global civilization, one that will require new mental models and
structures for learning.
The Crisis in Learning
For well over a decade we have been barraged with reports and rhetoric about the crisis
in public
education. It is my belief, however, that the espoused crisis in public education is predominantly a
crisis about learning and that it is fundamentally grounded in the dynamic integration of two new
domains of inquiry:
1. The paradigm shift from a machine-based "clockwork" conception of
the universe to a complex
adaptive system perspective
2. The paradigm shift from understanding the brain as a computer to be programmed
and learning
as a linear process of information accumulation to understanding the brain as a dynamic, self-
organizing neural network and learning as a natural, active, and messy process of pattern
formulation and constructed meaning
Inherent in the old mental models are three mechanistic metaphors that have historically
contextualized our view of schooling and learning: universe as clock, brain as computer, and
learning as tabula rasa (blank slate). The insights of complex adaptive system theory and learning
theory have fundamentally altered these metaphors and have radically reframed the discourse on
learning and schooling; in place of machine-based metaphors are fluid, organic, and biological
metaphors that place current schooling structures in dynamic opposition to our new knowledge.
The Creation of Clockwork - Organizations and Schools
The way that scientists view the dynamics, patterns, and relationships of the universe
and natural
world has profound implications for the way we construct our world. As a consequence, we shape,
organize, and direct our institutions according to the science of our times. For three centuries, the
dominant scientific worldview was the image of a static, repetitive, predictable, linear, and
clockwork universe. This Newtonian worldview seemed to create an obsession with linear thinking
and encouraged the escalation of an almost exclusively rational trajectory that has controlled and
defined almost every dimension of our cultural arid organizational life, including our schools.
As leaders, we focused on predictive cause-and-effect models of human learning; we
became
preoccupied with things and efficiently managed our organizations and our schools by reducing
them to discrete, observable, and measurable parts. Deriving our insight from Newtonian science,
we behaved as if we actually believed that by understanding the parts we would discern the
behavior of the whole, and that analysis would inevitably lead to synthesis. We may have thought
that the structure of our current educational system was derived from the principles of Frederick
Winslow Taylor and Adam Smith and the needs of the nineteenth- century industrial revolution;
however, it is even more fundamentally rooted in seventeenth- century science and false
conceptions of how the brain works and learning occurs.
In alignment with the clockwork, mechanistic metaphor of schooling, we embraced an
erroneous
and dysfunctional paradigm of learning based on the following assumptions:
* Education is passive and incremental, not dynamic and developmental.
* Learning is acquired information, not constructed meaning.
* Intelligence is a fixed capacity and is not learnable.
* Potential and capability are finite and are not capable of being enhanced
* Learning is defined by the calendar and the amount of time one stays on task
and not by
demonstrations and what DavidPerkins calls ‘performances of understanding."
* Content coverage and reproduction are more important than genuine understanding.
* Rote memory is "better" than spatial memory.
* Prior knowledge is unimportant to future understanding.
* Content segmentation is more highly valued than concept integration.
* Reliable evaluation can only be objective and external, not qualitative and
self-adjusting.
* Competition is a far more powerful motivator than cooperation.
By design, we constructed and operated our Newtonian schools as we understood our
world, and
this produced iatrogenic and learning-disabled institutions that have suppressed reflective thought,
creativity, and the innate and inexhaustible human capacity for lifelong growth. The unexamined
application of Newtonian laws to complex adaptive social systems diminished our capacity for
continuous growth and change because it diminished our capacity to "grow" the individual and
collective intelligence, energy, spirit, and hope of the whole system.
We had designed a linear system based upon predictive models of change and a belief
that
learning was incremental, when in fact human systems, like most of nature, are not predictable;
change is nonlinear and learning is dynamic and patterned. Human beings do not follow the logic of
cause and effect. We crave connectedness and meaning, we seek lasting and deep relationships,
we grow by sharing and not by keeping secrets, and we need to trust and be trusted in order to feel
safe enough to dare. If we want to create learning communities that continuously renew and
reintegrate themselves toward higher levels of complexity, we must ground our organizational
transformation and our leadership in the science of our times and we must create conditions for the
purposeful and soulful engagement of people in their work.
A New Learning Covenant
The turn of the twentieth century brought the linear and mechanistic worldview to
an end and
heralded the conception of an ecological universe—a holistic, dynamic, and inextricably connected
system in which everything seems to affect everything else. Furthermore, the last two decades
have produced revolutionary new insights about how human beings learn and how we can best
create environments that accelerate our natural learning processes. These new insights come at a
time when we are beginning to experience some of the transformational power of information and
communication technologies.
Because knowledge is doubling at a remarkable rate, the structures (schools) and strategies
historically used to deliver that knowledge in prescriptive and linear ways are now being challenged
by knowledge-based institutions that recognize the need for continuous workplace learning.
According to Stan Davis and Jim Botkin in their fascinating book The Monster Under the Bed,
"Lifelong learning is the norm that is augmenting and in some cases displacing school- age
education (p. 16). Consequently, "the schoolhouse of the future may be neither school nor house"
(p. 23). We must prepare our children for the learning workplace they will encounter. The foundation
for growth and sustainability in this new learning environment is the continuous generation and
exchange of knowledge, a process made possible by our inherent desire and capacity for new
learning.
The old "educational efficiency" contract for the nineteenth-century school
prescribed a "one size
fits all" delivery system that accepted erroneous proxies like class time and course credits as
indicators of genuine understanding. As a result, we created brain- and learning- antagonistic
environments that actually inhibited integrative learning, distorted the learner’s identity and his
or
her competence as a learner, and discouraged inventiveness, inquiry, and complex cognition. The
new personalized learning covenant for the twenty-first century must continuously build individual
capacity by stimulating natural learning, and it must be established and built upon a foundation of
connection, coherence, mutually created meaning and purpose, dynamic relationships, and the
evolutionary nature of the human experience itself.
What all this means is that we must transform the mechanistic paradigm of schooling
into an
integrated, holistic, and systemic vision of a sustainable learning community. How might we do
this?
A Pattern Language
In a remarkable book titled The Timeless Way of Building, architect Christopher Alexander
describes the essence of creating alive and dynamic space. According to Alexander, the structures
we build (buildings or organizations) are created through a "system of patterns which function
as a
language" (p. 178). He says, "All acts of building are governed by a pattern language . .
. and the
patterns in the world are there entirely because they are created by the language that people use"
(p. 193). As we explore the principles and conditions necessary to create sustainable learning
communities, we must recognize that our new understandings about the universe, the natural
world, and learning are enabling us to create a new pattern language for conceiving and describing
learning environments.
We have moved from a linear language to a living language, from machine-based metaphors
to
ecology-based metaphors, and from rigid structures to mutable environments. Because, as
Alexander asserts, "patterns are not things, but are complex and potent fields" (p. 223),
we must
acknowledge that the natural world itself is trying to draw us closer to a new way of seeing and
being in the world, and this has profound implications for the creation of learning communities.
When we acknowledge that the brain is a complex, self-adjusting, living system and not a
computer, when we understand that learning is a goal-directed and internally mediated process of
constructing meaning and not an information accumulation process, and when we recognize that
human systems are dynamic
and organic and not linear and predictive, we are compelled to use this new understanding
to
create new language, new patterns, and new environments that support and celebrate the nature of
learning itself.
The reason our society must create a new language for learning communities that transcends
school and classroom walls is that the dominance, attraction, and power of the current machine-
based language of schooling is not capable of generating the organic patterns of the global learning
community we now require. The very nature of the language, the potency of its field, and the
meaning it constructs preempt its capacity to generate living patterns; only a living language can
create living patterns and only living patterns can create living environments. We have excelled in
the language of schooling. We must now become fluent in the language of learning and life.
If our language is prescriptive, our schools cannot be generative. If our language
is static, our
schools cannot be dynamic. If our language is linear and algorithmic, our schools cannot be playful
and creative. If our language is controlled, our schools cannot be mutable. A school cannot come
alive and cannot become a sustainable learning community without a living language that creates
living patterns of interaction and relationships; the language of nature and the new learning
technologies provide such a lexicon. The creation of ecological learning communities is, therefore,
inextricably connected to the language of learning itself.
We need to create learning and teaching communities that enable learners to direct
their own
learning toward greater rigor, coherence, and complexity; to increase their intellectual, social, and
emotional engagement with others; and to foster collaborative and dynamic approaches to learning
that enable them to develop thoughtful and integrative ways of knowing. We must create a learning
culture that provides a forum for risk, novelty, experimentation, and challenge and that redirects and
personalizes learning. We must create learning communities for learners of all ages that can give
power, time, and voice to their inquiry and their creativity.
Such a community is governed by the principles of learning, not schooling, and is:
* Personalized, flexible, and coherent (learning is connected to real-life issues)
* Internally and externally networked and not bounded by physical, geographic,
or temporal
space
* Invitational, with students engaged in meaningful research and serious inquiry
* Accountable to the learner to provide adaptive instructional environments
* Rich in information and learning experiences for all learners
* Open to emergent and generative knowledge
* Self-organized around core principles, beliefs, and a shared and mutually created
purpose
* Intergenerational in the configuration of learning experience
* Flexible, diverse, and innovative
* Interconnected and collaborative, fostering interorganizational linkages
* Engaged in authentic dialogue with members of the internal and external community
* Focused on inquiry, complex cognition, problem finding, and problem resolution
* Committed to increasing what David Perkins, in Outsmarting IQ, calls the "learnable
intelligences" of every individual
* Comfortable with ambiguity and paradox
* Playful
* Trusting
* Responsible
* Lovable
If we are truly going to create learning communities for the twenty-first century,
we must view our
schools as dynamic, adaptive, self-organizing systems, not only capable but inherently designed to
renew themselves and to grow and change.
Leaders’ New Work
Although my focus has been on the creation of sustainable learning communities to
replace the
linear and depersonalized delivery system of schools, the sustainability of all human systems lies
in their evolution to learning and teaching communities. It is for this reason that the current context
of organizations must be reframed so that self-organizing complex adaptive systems can emerge.
Leaders, therefore, will need to understand their systems’ natural desire for self- organization.
What creates self-organization in living systems? In Leadership arid the New Science,
Margaret
Wheatley has made it clear that self-organization in natural systems will emerge from the webbed
and dynamic interconnectedness of three domains: identity, information, and relationships. If we
want to build the resiliency and adaptive capacity of everyone in the organization; if we want the
organization to increase its collective intelligence, potential for relatedness, and shared sense of
meaning; and if we want to ensure long-term sustainability, leaders must be engaged in new
work—they must create the conditions whereby identity, information, and relationships are
dynamically connected around the system’s larger purpose. What are the conditions that leaders
must create?
Identity
Identity is the principle that is most fundamental to all self-organizing systems.
It encompasses the
organization’s meaning, purpose, and intentionality and provides the coherence around which
system stability emerges. Identity facilitates order and transformation even in turbulent
environments because it provides a constant frame of reference for organizational integrity and
renewal. Organizations and people have the capacity for self reference when the organization’s
identity, purpose, and meaning are clear and when leaders create the following conditions:
* They bring the system together to think about itself and to make decisions for
itself as a
system.
* They involve the expertise and experience of everyone in the system in creating
the
organization’s fundamental beliefs, values, and shared purpose (mission) and encourage people to
organize around them.
* They clearly and continuously identify the patterns in the organization, what
the organization is
trying to accomplish, and how each individual is connected to its future.
* They promote an organizational consciousness and a sense of belongingness to
a larger
purpose.
* They make decisions at the local level based upon a strong sense of organizational
self
(identity).
* They promote individual and organizational freedom and efficacy.
Information
Information is both the medium of exchange for generative organizational learning
and its source of
power. Within self-organizing systems, information is not a thing; it is the dynamic center of
organizational life that allows continual growth and defines what is essential for sustainability.
Without the constant flow of informational energy to both excite and serve the system, the system
will become closed and isolated. Leaders can create the following conditions to ensure its
robustness:
* They create open and multiple pathways for communication.
* They infuse the organization with abundant information by explicitly bringing
the environment’s
voice into the system.
* They move information everywhere in the system.
* They continuously generate and share new knowledge.
* They promote honest dialogue, feedback, and interaction.
* They keep rules simple for detecting, processing, and integrating information.
* They seek out information that is complex, ambiguous, and paradoxical and encourage
people
to publicly discuss and use it.
* They encourage frequent and rapid | |
1. Thoughts
