Big Learning

Big Learning
How We Need To Know Things,
And Why We Need To Know How
© 2014 Malcolm Ryder / archestra research

The internet has exposed us to
a vast heterogeneous landscape of
content and ideas,
with unprecedented ad hoc ability to
navigate, inspect, select and collect
both programmatically and not.
This completely changes our general expectations
of what “knowledge” is available,
and the specific issue at hand is
how we confidently derive
consistent meaning from the
breadth of disparate information sources.

In that environment,
learning increasingly occurs
through nothing less than
the employment of
the intellectual mode used
as a basic cognitive instrument in art.
Concepts from one context
are ported to another context,
and received there as catalysts
of modelling new knowledge.

Today, knowledge transfer
increasingly means that
concepts are not just
an end point product in one subject.
They are also
a beginning of new product development
in another subject.

The “transfer of knowledge”
is really a delivery –
of concepts from intelligence.
But more specifically it is
a naturally emerging adaptive behavior,
of increasingly self-propelled learners,
in the complexity and density of
the current information landscape.

In Big Learning,
exposing patterns of intelligence
and re-purposing them
in multiple domains
becomes an
ordinary, continual, and ubiquitous
activity.

BIG LEARNING: The Notes
Mind Mapping
The History of “Knowing”
The Information Landscape
Getting Educated
Becoming “Learned”
A Learning Dynamic
Acquiring Concepts
Producing Knowledge
The Ambiguity of Content
Processing Content
Programming Learning
Intellectual “autonomy”
Designing Knowledge
Meta-Knowledge
The Diversity of Approach
Managed Learning
Proof of Learning
Intellect, ideas and knowledge are different but systemically related
The value of knowledge is in adaptation across environments
Multi-environment adaptability is the education agenda
The value of teaching is to predispose effective performance
Self-service is a naturally emerging adaptive behavior
Knowledge develops through a managed information transfer
Meaning is derived through contexts and affinities
In learning, interpretation creates meaning from intelligence
Validation of content is a prerequisite of its value as knowledge
The promoter’s purpose must be compatible with the user’s intention
Learning is a capability, and Education is developmental
Alternative paths of knowledge gain are the next normal
Self-service learning leverages frameworks and models for value
Critical thinking is the default paradigm of learning
Freedom of thought is natural and should become normal in practice
Learning requires both facilitation and authority
Learning enables on-demand production of appropriate knowledge

VALUE PROCESS INTENT SELF-SERVICE DEFINITIONS
Overview History of
Knowing
The Information
Landscape
Producing
Knowledge
Becoming
“Learned”
Mind Mapping
Requirements Getting
Educated
A Learning
Dynamic
Processing
Content
Intellectual
Autonomy
Acquiring
Concepts
Methods The Ambiguity
of Content
Programming
Learning
The Diversity of
Approach
Designing
Knowledge
Meta-
Knowledge
Performance Proof of
Learning
Managed
Learning
©2014 Malcolm Ryder / archestra
The Big Learning Notebook

Idea
Presentation
Production
Value
Package Supply
Value
Supply
Event
Proposal Wisdom Domain Expertise Advice
Example Intelligence Concept Knowledge Lesson
Sample Information Content Data Message
MAPPING THE INTELLECT
The “intellect” is a mental representation (idea) of what experience means and
of how the meaning is retained as a frame of reference for future experience.
The intellect includes a system of relationships between presentations of experience,
the importance of the presentations, and the utilization of the importance.

Content Context Value Competency
Information Concept Expertise Decision
Exercise composition presentation application selection
Interpret intelligence meaning distinction comprehension
Identify exposure recognition awareness association
Access location domain definition apprehension
Idea Presence
IdeaProcessing
The relationship of idea Creators and idea Acquirers (Learners) is one point of view in
talking about the availability of ideas. But the usability of ideas, which underlies the
importance of their availability, results from how their Providers and Users affect them.
Ideas are processed: positioned, packaged, exchanged, and implemented –
with each effort effecting different ways that usage is enabled and/or motivated.
Typically, in whatever represents the presence of the idea,
the perceived “quality” of the idea is at stake.
MANAGING IDEAS

Collection Transfer Receipt Utilization
Propose ExplainDiscover Develop Classify Adopt Provide Reproduce
DELIVERY VALUEPRODUCTION
We generally think of “learning” as knowledge acquisition. Proof of knowledge acquisition
is generally expected to rely on a demonstration. The end-to-end enablement of that
performance has the general schema shown here. In the scheme, the area that separates
pre-learned from post-learned is between propose and adopt.
MANAGING KNOWLEDGE

Beyond grand unification theories or religions,
there is always
both “more” and “different” knowledge
than we have already gained.
Hypothetically, we could stop learning
at the point where we are now.
Curiosity does not always require a response.

But we generally think of “learning”
as being
the purposeful acquisition of knowledge.
And historically, the general purposes
of acquiring additional knowledge
are what drive us on to
continue getting more than we have.

“Knowledge” is not actions;
knowledge is ideas.
But the value of knowledge is,
ultimately,
always measured
in terms of
some kind of performance.

The most important function of knowledge
was always first to support
the ability to identify and adapt to the environment.
Then, knowledge guided conscious design,
primarily to invent practical signs and tools,
which “formalized” the environment.
The next use of knowledge was usually to
change the environment.

When the environment was re-created
by design and by tools,
or by Nature,
the new circumstances called for
a return to the first knowledge function,
for re-adaptation.
So the cycle began again.

This cycle includes two fundamental
conditions of knowledge value:
awareness, and application.
That is, the basic value of knowledge
is generated from
identifying an awareness and
exercising the ability to
apply that awareness usefully.
This exercise, or “performance”,
is the attractor and goal of learning.

Today, an exponentially greater ability
to form the environment on demand
means that more builders are active,
creating more different situations.
Environmental diversity results.
Consequently, knowledge expands its
scope of purpose
to include
the ability to move across boundaries.
That involves a different requirement –
not an ability to adapt to “the” environment,
but instead to adapt across multiple environments.

In today’s information landscape,
learning means gaining the ability to
take examples from one environment and
apply them productively in other environments.
The ability to abstract and extract
productive meaning from
heterogeneous sources of intelligence
is similar to the new achievement of “Big Data”,
but instead we must see it as Big Learning…

Now, more than ever before,
thanks to information technology,
multiple environments
are virtually created from information.
Each environment is a context within
An overall information landscape.

In an “information environment”,
certain ideas are offered in a certain context.
Context predisposes how the idea is
acknowledged, understood, and appreciated.
The “meaning” of the idea emerges “in context”.
But the idea also may reappear
in a different context.
That event raises the question of whether
the meaning has thereby changed, and of whether
it is important to prefer one context over another.

The environmental diversity
of multiple contexts
requires that
future learning will intend for a
manageable and valuable adaptation
to those “information environments”
and across them.
This adaptability will be a key feature of
intellectual competency.

Adaptation to multiple environments
requires us to take on several key tasks,
both scientifically and culturally.
*prioritize what the environments
should be like,
*understand adapted behaviors,
*assess the difference between
actual behaviors and preferred behaviors,
*and then, understand how to manage
environmental diversity itself.
In effect, these adaptation tasks must become
the agenda items of education.

The value of knowledge is, ultimately,
always measured in terms of
some kind of performance.
But knowledge acquisition does not
determine when the performance is required;
instead, it predisposes an ability to perform.
Teaching and learning are “always on”,
as a hybrid (combo) provider
to satisfy the prerequisites for performance.

The teaching-learning hybrid
is what we usually call “education”.
Education always has the same goal:
to achieve intellectual competency
applicable to
“problems” that are
either chosen or unavoidable.

Providing education presumes an ability to
execute and coordinate responses to
two different but complementary challenges.
The biggest challenge in teaching is
to convey, as information,
the relationship of conditions to events.
But the biggest challenge in learning is
to recognize the meaning
of received information.
Meanwhile, no amount of teaching
is ultimately valuable
without effective learning.

In our information-saturated times,
the teaching-learning relationship
is still indispensable to
developing large targeted communities of
effectively educated individuals.
However, learning is becoming
increasingly enabled by
self-service technology
that both produces and explores
knowledge in the information landscape
for the separate individual.

Technology also increases
the deliverability
of a wider range of knowledge forms,
both to any person and to more persons,
by any individual teacher.
That same increase changes
the individual’s ability to teach,
Thus it also means that
the technology creates
a newer and increased capability
for self-teaching as well.

A technology-enabled combination of
self-teaching and self-learning
is now an alternative default option
in driving intellectual development.
But regardless of any deliberate intent,
the self-service option is essentially a
naturally emerging adaptive behavior
responding to the complexity and density of
the current information landscape.
In that behavior, the bias (demand) is towards learning,
and the key question is,
what is being learned, and how?

We normally think of a “learning event”
as being
an acquisition of knowledge
achieved by
a transfer of
knowledge from an information source –
or that results from
a development of
knowledge from an experience.

Meanwhile, any life experience itself
can be the information source.
Said differently,
there is a way to understand
the development of knowledge
as a “transfer” of
information that was originally
embedded in experience.

The “mobility” of the information
means that it can be extracted from its origin
and re-situated elsewhere, for example
as part of a different experience.
Learning is both
a practice and a result of how
that transfer takes place.

Usually, the responsibility for
orchestrating and managing
that transfer and the affects of it
is given to teachers.
But today, an increasing reliance on
self-service learning opportunities
means that the learners must take up
more of the responsibility themselves –
for balancing the
production and benefits of
the affects of the transfer.

This illustrated overview identifies the
numerous factors that typically affect
how education emerges as “learned
knowledge” for an individual.
The relationships of these factors form
dynamics in an overall environment of
information mobility, including the web.
An example of a dynamic is the “path”
highlighted by the red and green lines,
which is one possible set of affinities.
The picture also represents relationships
(options) such as the following: a method
can contain multiple styles; a discipline
can contain multiple methods; a domain
can include multiple concepts; etc.
The key relationship between teaching
approaches and learning styles is also in
the scheme as a variable ability. And the
new information landscape created by
the internet presents a completely
different balance of powers between
how people learn and who is teaching.
In the current web-based landscape, concepts and disciplines increasingly move freely and
unpredictably across domains, establishing new connections. Some of the movement is
intentional and some is not; most of it is highly exposed to any self-directing observer.
OVERVIEW: Dynamics of Information Mobility in Knowledge Development
©2013,2014MalcolmRyder/archestra

Prior experience is often documented as
intelligence about a subject.
Collections of preserved information
present intelligence.
A concept is
a given meaning associated with
a given set of intelligence.
In that way,
intelligence supports and indicates concepts.
But the same intelligence can support and indicate
different concepts simultaneously.

Conventionally, designated domains
host and cultivate certain concepts
within the boundary of a subject.
Disciplines act within a domain,
to add, move, change and delete
concepts in the domain.
Some disciplines may be inherent to a domain
while others are not and may be
used on the domain.

Within the subject,
disciplines and domains together
organize concepts as
verified, maintained “material” (content)
that we may call knowledge.
In effect, they “administer” knowledge.
In that way,
the disciplines and domains manage
the meanings that are intended to be
available to any attending person.

Subject areas and domains
provide shared expectations that help to
guide the intentions of
independent learners
approaching information
from whatever their circumstances.
But increasingly,
the information environment
that is available on demand to an individual
allows content – and therefore concepts –
to be freely transported and included
across their different circumstances,
not just across different people
in the same circumstance.

The diversity of circumstances adds
uncertainty, variety, and/or novelty
to how concepts will be recognized
by an individual receiving information.
Differences in recognition
cause differences in meaning.
An individual receiver’s circumstance
is dominated by two aspects:
context, and affinity.

Discover,
Organize,
and Express
Intelligence
Discover,
Organize,
and Express
Meaning
CONTEXT
Teach Learn
AFFINITY
preference
standard
map
model
explore
train
engage
Educational experience prominently
features contexts and affinities.
Technology has hugely amplified the
range and variability of differentiated
contexts and affinities. Along with
that, it has also increased the ease
and power of associating the two.
The association of context and affinity
can be ad hoc, habitual, or formulaic;
and the influences on it can be
internal, external, or a blend of both.
Both teaching and learning bring
organization to the association of
context and affinity.
Meanwhile, teaching and learning
aim to combine in a structured
engagement; but it is not necessarily
programmatic and scheduled; it can
instead be improvised and continual. ©2014 Malcolm Ryder / archestra

By definition, intelligence is
information about something.
Due to some “pre=processing”,
information enters the learning process
as intelligence.
The “pre=processing”
composes the intelligence to be
appropriate for
its original intended circumstance.

To “acquire” knowledge
from that intelligence,
the learning process features
a capability
for interpreting the composition.
In that process, interpretation reveals
how the included information
is being used as offered.

“Knowledge” occurs when
the capability for interpreting composition
makes it clear why, not just how,
the included information can be meaningful.
This is similar to the ability to
understand language and to speak,
based on the grammar and syntax.
In other words,
knowledge is fundamentally
a practical capacity,
not a collection of intelligence.

As an effort to “produce” knowledge,
education concerns itself with
how people learn and how they practice thinking,
not just how they acquire facts and make decisions.
To do that, education focuses on developing
the capability for interpretation
into the capacity of knowledge.
In education’s development effort,
teaching focuses on
what the presenter knows and why;
learning addresses the question,
“how do we know what the presenter knows?”

The development effort called education
is itself an experience.
The value of the education experience
increases along with
the complexity that comes from
today’s super-abundance
of information and intelligence.

Abundant availability of intelligence
does not necessarily cause
commensurate usability,
and it may even complicate it or reduce it.
The challenge created by this abundance
is due to both
packaging and delivery.

The process of learning that leads to
the occurrence of knowledge
presumes that
information has a usability
based on its
credibility, persistence and authority –
in other words, reliability.
But the portability of information
can readily create
ambiguity about how or why
information is reliable.

The massive volumes of information
in the current landscape
increasingly make
“cross domain” and “cross discipline”
exposure of information
normal, not exceptional.
That exposure of information gives it
more chances to be valuable
but not necessarily
more likelihood of being valuable.
The mass exposure means that
confusion or devaluation
are also easily possible effects.

In the face of
potential information overload,
information processing
is a prerequisite of
deriving value from information.
That is,
something must be done to
make the information into
a “product”
that offers
explicitly intended value.

Ordinarily, the “productized” information is
information packaged as “content”.
Content is usually
intended to be provided
in association with specified uses,
which offers a sense of reliability or certainty.
But what now is becoming
abundantly “ordinary” as content
is the mash-up, the multi-media,
the virtualization, the sampling,
the personalization, and more...
This challenges any presumption that information is
“original” and therefore “authentic”…

Additionally, because of
networked communications and digitization,
the cycle time from
source material collection,
to re-composition,
to re-presentation –
is down from weeks to minutes;
and the outputs of the first cycle
very quickly become
the inputs of another cycle.

As a defacto practice,
information processing and content production
both now feature
a vast and sometimes volatile
open sourcing of
excerpts, proxies, relatives, and derivatives
of requested information,
built into daily presentations
along with original material.
Individuals
have unprecedented power to do this
in their own role
as technology-enabled providers of material.

In a parallel practice,
individuals have unprecedented power
to search for, and become receivers of,
content and its information.
But they are now more often left to their own devices
to determine their level of confidence in
whether the information in the presentations
Is appropriate to their purpose.
Given the far greater numbers
of independent recipients,
such idiosyncrasy brings
far greater variety in
how content is
interpreted for use.

In short, when information is encountered,
its utility and meaning can be
completely recast in short order,
versus the intended utility and meaning
with which it was originally provided.
Meanwhile, the biggest challenge in learning is
to recognize the meaning
of received information.

Because of that,
certain practices of learning
must now become
ordinary and defacto
“best practices” as well.
The key practice emphasizes
the active decision-making that
validates available information
as being
credible and appropriate for
purposeful adoption.

In both context and content,
a presentation of information
uses and provides the information
in a given form.
Learning requires
interpreting that presentation
to derive meaning through its form.

The new “ordinary” process of learning
must build explicit awareness
of why
that form is provided for presentation,
and of how
that form is valid for a recipient’s need.

The new ordinary process of learning
must also train an explicit applicability
of that awareness
through techniques that
extract and determine
the relevance
of the information.
The development of awareness and applicability
will be through techniques of
interpretation and recognition.

In short, the learning process requires
accessed content and its composition
to be
“exposed”,
through critical thinking,
either before the learning event
or during the learning event.
Intention Recognition Validation
Expectation Interpretation Relevance
Learning by Processing the Composition of Content
Applicability
Awareness

The exposure of composition
help to “unpack” the information in content
and discover the characteristics of the information
that were formed by
the presentation of the content.
That discovery guides the
subsequent selection and acceptance of
a relevant arrangement of the information
as further-usable ideas.

The main challenges to that exposure
now come from
the ease of hit-&-run content consumption…
Today’s super-high availability of content
and speed of access to it
encourage an impatience in usage (applicability)
that results in
taking content at apparent “face value”.
The superficial encounter with information
appears to suffice in the moment, but
content acquisition winds up
substituting for learning.

A second challenge to the
learning-oriented exposure
is that
original creators of content
make a significant initial effort to
package information in a way that
is already “expressive”
without investigation or interpretation.
By discouraging inspection (awareness),
the packaging can actually create
a difference between
what the content creator intended to deliver,
and what can be expected to arrive through
the “filter” of the content receiver’s context.

Superficiality (face value) and misalignment
are drawbacks of passive exposure that indicate
two important preconditions for
learning from the content.
First, to align applicability,
a content promoter
may need to give equal or greater emphasis
to the content purpose over the content ingredients.
Second, to align awareness,
a content provider
may need to be a responsible arbiter of
producer intentions and requestor expectations.

The result of composition
is the relationships of
the elements and components
that build up
concepts conveyed in content.
This means that
concepts can be seen as a result of
selections, arrangements, and emphases
of the elements and components.

New technologies increasingly automate
techniques that perform those constructions.
The automation in turn makes the techniques
far more widely usable by
independently working individuals.
Automation can affect
the elements and components
at almost any level of their exposure.

The priority in learning is
to avoid having automation
reinforce inattention and instead
to have it reinforce appropriate applicability.
This reflects the goal of producing
knowledge that has value.

As one example of
production automation,
we now have Big Data –
the computerized analysis of relationships
within high volumes of
heterogeneous data.
Data is commonly seen as
one level of element that is
processed as a component
of knowledge.

We imagine
Big Data having “constructive” impact
on information and knowledge value
in this conventionally arranged way:
Data processing creates information,
and information processing creates knowledge,
and knowledge processing creates wisdom,
so Big Data will start pushing
value-supporting effects “upstream”.
But that convention is quite vague about
what “processing” occurs.

Big Data actually operates on
unprecedented volumes of information
and offers intelligence as its product.
Much of that product is then
packaged by other means
as “concepts”
which in turn are deliverable as
knowledge and expertise.
In effect, the packager largely decides
what the intelligence will appear to “mean”.

Discovery, selection, analysis, packaging, delivery,
and other “production” tasks
are increasingly gaining automation and integration
in their manipulation of ideas.
These provide opportunities to
“program” the overall effort that
takes an individual through a trip
from an initial exposure of chosen intelligence
to a final comprehension of concepts.

But with or without automation,
education operates on
the how plus the why of meaning.
As developed in education,
the ability to evaluate
Intelligence from whatever origins
is integrated with
the ability to interpret
content in whatever context.

The education effort coordinates
the directions, priorities and
levels of
attention to how
ideas have been managed
from the producer to the recipient user.
New technologies that help to
automate that coordination
continue to become available to individuals
for their independent personal efforts.
But the pattern of coordination is
the actual “program” affecting learning.

As arranged by education,
the pattern of coordination
is for a
designed experience
that develops and rehearses
the ability to learn.

We typically identify expertise as
the highest value product of
developing knowledge.
As a product,
its availability and variety
is distinguished for the user by
where it comes from,
what to do with it,
and who cares.

Expertise is usually acknowledged as
a goal or destination of
a special level of
knowledge acquisition
reached by
certifiable, prescribed approaches.
But now,
alternative paths to acquiring knowledge
are emerging as
viable new defaults and norms.

In any path, we say that
the acquisition occurs when
knowledge is transferred
from provider to recipient.
For an information consumer,
this “knowledge transfer” path
is really a delivery,
of concepts derived from intelligence.

Today, self-service raises the issue of what
aspects of the delivery should be
explicit to, or accomplished by,
the consumer.
Acquisition of content is one mode of receiving concepts,
But that does not equate to learning.

Thanks to new tools,
content users increasingly become
their own content producers.
But the continual redevelopment
of existing content
creates more and more
content in the traffic,
carrying ideas in a variety of ways.

Meanwhile, recipients increasingly decide
their own interpretations of what is received.
But for learning to be done
by the content recipient,
handling the burden of content volume
requires the content to be
in a state that
allows its composition
to be understood as well.

Content contains information
that composition has formed into
intelligence with intent to have impact.
That intended impact of the intelligence
is the meaning of the content,
and the form produced by the composition
is a concept.
Learning intends to “acquire”
the meaning of the content
by interpreting the concept.

Deriving a concept from intelligence involves
apprehending included ideas through
Identification and selection for interpretation.
In interpretation,
learning requires an active attitude
towards awareness of the composition,
which is manipulating ideas.
For example, this may be done with a framework.

And the “delivery” of the concept
concludes with
comprehending it, through
classification and prioritization for implementation.
In implementation,
learning requires an active attitude
towards applicability of the way that
composition was seen to have formed key concepts.
For example, this may be done with a model.

Both in apprehending intelligence
and in comprehending concepts,
the user of information
can sit on a spectrum between being
a passive “trusting” receiver or
an active “verifying” producer.
Among the powerful new tools,
that the user now has,
social media, open sourcing, and digitization
are especially important…
Each tool can be used
in a receiver role or in a producer role.

The multiple new technologies
have today explosively increased
the ordinary initial exposure
to information that may be
relevant to our key tasks,
compared to previous eras.
Meanwhile,
despite the risk of being overloaded
by volumes of already-available content,
users in the current information environment
increasingly employ new tools to facilitate
their additional desired enhancement, modification,
reproduction or repurposing
of the arriving and evident information.

The interactions of these technologies
generate an enormous range of content,
originating from a huge variety of
circumstances and contributors,
collecting at a receiver’s
single, shared, target point of access.
At the location of access to
increasingly abundant content,
learners have the significant problem of
handling and vetting the workload.
But the potential readily exists for
a wider scope of knowledge gain…
or for a more vigorous cross-referencing
of knowledge.

Knowledge “capture” (or more correctly, apprehending ideas from intelligence)
is a step on the delivery path in the transfer of knowledge. In the ”delivery” of
concepts, provided intelligence is operated on to derive relevant, reusable ideas.
Today, sources and availability of intelligence are at all-time high volumes; and
operations performed on the intelligence are increasingly automated, integrated
and/or improvised in new or ad hoc ways.
Intelligence
channels
Identification
(definition)
Selection
(relevance)
Interpretation
(purpose)
social networks subjects recommendations opinions
open source resources validations functions
digitization signals locations messages

“Receiving” knowledge (or more correctly, comprehending ideas from
intelligence) is another step on the delivery path in the transfer of knowledge.
In the ”receipt” of concepts, anticipation of a usage generates predispositions,
attractions and preferences that together become a user’s affinities with some
ideas more than with others. Regardless of whether the anticipated usage is the
content provider’s or the content user’s, these factors can make the information
user’s attention more exclusive or more inclusive of differing ideas.
Diversification of
Intelligence
Classification Prioritization Implementation
expectations proofs guidance methods
types categories standards models
sources references validations citations

In effect, by exposing
the mechanisms of composition,
knowledge about the knowledge
is part of the actual final
“delivery” of knowledge from content.
The more that the self-service individual
can consciously consider the mechanisms,
the more autonomous the person is
as a learner and as
an eventually knowledgeable
individual.

Concepts in one domain
can be abstracted as recognized patterns
and transported
into another domain.
The pattern is something that can be
maintained for reference and re-presentation.
Patterns are formed by
the manipulations of information.

These manipulations include
definitions, rules, and behaviors
that account for why information was
included, arranged, used and maintained.
In the target domain
they provoke new recognition of
forms (structures or conditions)
that were latent there
but not previously cultivated or exposed.
In turn, these “newly evident” forms
drive and populate new knowledge.

Meta-Knowledge
“Big Data” assumes the ability to analyze vast amounts of data for detecting patterns (such as conditions) that, due to their
influence on other events, are “effectively real”. These “mined” virtual phenomena have the same status that “concepts” do in
collections of knowledge content. Similarly, we realize that concepts are “discovered”, as outcomes of some previous activity.
Big Learning features the phenomenon of concepts in one domain being abstracted as patterns and transposed into another
domain, where they provoke new recognition of forms (structures or conditions) in the target domain that were latent there but
not previously cultivated or exposed. These “new” forms in turn drive and populate new knowledge.
In this abstraction and transposition, a variety of dynamics are involved, including analogy, superimposition, proximity, and
others that we know are casual or even accidental occurrences – not just as intentional ones. In Big Learning, involvement in
the dynamics is especially through the vehicles of networking, collaboration and modeling that are now intensively proliferating
in the open, social, digitized traffic of the internet, and which are crossing, challenging and creating disciplines.
The abstraction and transposition processing includes identifying the definitions, rules, and behaviors that have generated the
state of the presented information. These indicate why the information should be important as “knowledge”. Where the
presentations actually allow the information to be re-presented in circumstances other than its origins, the processing that is
needed (not mere communication) must be incorporated in the teaching/learning dynamic.
Forms Models Disciplines Schools
definitions structures types techniques themes
rules boundaries relations policies styles
behaviors distinctions approaches methods perspectives
∆ ∆ ∆ ∆
represented:
recognized:
renovated:
• objects
• artifacts
• collation
• requirements
• organization
• improvisation
• preferences
• priorities
• collaboration
• producers
• product
• integration
©2014MalcolmRyder/archestra

What we expect in teaching is that conveying information will occur in a way that acknowledges the
recipient’s abilities for recognition. If we search the web using the query “the definition of recognition”,
the result returned will be statements such as this: “Identification of a thing or person from previous
encounters or knowledge.” In that statement the important factor to note is not the thing or person from
previous experience; rather, it is the “identification” from previous experience. In Big Learning, what also
arrives from previous experience is the mode by which things or persons had been identified.
Meanwhile, what we expect in learning is that understanding information will occur through
acknowledging how the information is intended to indicate concepts. This critical aspect of translation is
nothing less than the point where an architecture and design of knowledge development must exist.
Modes of identification Types of indication
Analogy (resemblance) Symbolic (proxies)
Superimposition (matching) Mimetic (examples)
Proximity (correlation) Inferential (relatives)

Producing meta-knowledge
has always been a responsibility
of educators of knowledge
and managers of knowledge.
Those parties work in collaboration with
subject matter experts.
Their ability to provide meta-knowledge
is critical to guiding
the recognition of ideas by learners.

In learning, the use of
approaches that drive recognition
can still be a specialized pursuit.
But this drive may not need to be
a conventional “domain expert” effort.
More and more,
the approaches are borrowed from one area
where they have been previously established,
and tried out in other areas
where they are unusual or unprecedented.

Today, broader application of
recognition approaches
is increasingly a default opportunity
instead of the exception.
For one thing, within a given area,
recognition approaches are far more often
collaboratively determined, including
collating or combining the different procedures
of multiple parties.

And for another, these approaches are
applied not only within
a given area of specialization,
but also
both across areas (simultaneously or synchronously)
and in multiple areas (asynchronously and independently).
This flexibility is a strongly liberalized attitude
towards the environment of information.

We already know this liberty of practice,
an “intellectual freedom”,
in several familiar and even embraced ways.
We expect young Children to do it,
at least while they have few inhibitions.
We expect “Innovators” to do it,
because they are consciously experimenting.
And we expect “Solvers” to do it
either when they are in trouble
or when they are working for us while we are in trouble.

Yet normally, except in the arts,
we have still considered
these liberalized productive thinking situations
to be relatively extra-ordinary,
relegating them for exceptional duty.
Most of the time,
most people are expected to be doing
something less improvisational,
typically for more prescribed results.
However, new tools let us
dramatically confront this limitation.

The self-service opportunity
to learn independently
is now more sustainable at increased levels.
But the value of acquired knowledge
remains predisposed (not predetermined) by
the delivery of processed content.
That delivery is what comprises
the development effort that is
the individual’s actual learning.

More than knowledge acquisition,
learning is an experience, of
intellectually redeveloping
the material information provided
in the source.
That redevelopment requires
real-time awareness and validation
of why information has been presented
by content the way it was.

Practicing this awareness and validation means
both interacting with the presentation,
and exploring or validating
its context and production.
The result of this kind of involvement is
an editorial consciousness driving
the formulation of meaning by the learner.
This involvement is not a new requirement,
but the mechanisms and options for doing it
are now permanently changed
by new technologies.

Nonetheless, the biggest challenge
to that editorial involvement is
the malleability
of today’s available content.
And the sheer bulk and diversity
of today’s accessible information
is both a cause and an effect
of that malleability.
For those reasons,
learning cannot in practice rely on just
receiving, accepting and retaining
declarations and assertions
for future repetition.

Instead, the content recipient must intervene in a way that
makes sense of it in context.
Historically, interactive contextual learning
has been inhibited by
factors built into much teaching practice.
These factors have been
both technical and procedural.

BEFORE:
For delivery of content-as-knowledge,
Individuals usually have not had
personal power tools sufficient to serve as
a reasonable alternative to
institutional (and corporate) information vehicles
that had economies of scale
and, therefore, predominant availability.
Validation of the individual’s own learning
also remained primarily institutional.

NOW:
Individuals have pocket-sized information tools
that provide them with personal access to
the equivalent practical processing power of
the “supercomputers” from 15 years ago.
Widespread provision of this access
creates the conditions in which
both individual teachers and individual learners,
rapidly proliferate in numbers, and
independently of each other,
quickly jump to new accepted norms
of production levels and interaction options.

BEFORE:
In parallel, for the most part,
authority has been a primary objective
of teaching practice.
This is because being authoritative has been
the primary basis for approval of teaching –
a social “knowledge requirement” trumping even
the goal of expertise.

NOW:
Approval of teaching is increasingly
a function of consumer consensus,
which mainly tracks a correlation of
teaching approaches with
effective use of content
Consequently,
the quality of teaching is less evidently
a “cause” of the quality of learning,
and instead is more evidently a “success factor”.

Approved teaching has long been
a presumed prerequisite for
valid (“correct”) learning.
Now, with the practical users’ new level of intensity,
approval of teaching and validation of learning
are still criteria of a
“targeted” quality of education.
But those evaluations have vigorously evolved.
They are less captive than ever before
to older institutions.
Meanwhile they each use a
broader variety of means.

In other words,
the relationship between teaching and learning
is an area of need for increased facilitation.
Facilitation spans
the delivery and interpretation of
content-as-knowledge.
We might call this facilitation
“learning management”,
and as education becomes
more of an effort of self-service,
learners must take on
more of the responsibility to facilitate.

Additionally,
because learners now have
far more power to
self-determine their education opportunity,
they also have the
increased responsibility to translate that power
into self-authority.
This means
learning how to learn:
understanding how learning occurs,
then managing activity to self-promote it
towards a purpose.

Authority reserves the rights to decide, define or change the instructions, orders and proofs that pass as
the vast majority of “practical” knowledge. In effect, authority is expected to manage “meaning”.
Overall, this authority is editorial. Editorial
authority has most frequently been
hoarded or made “exclusive” by
institutions and individuals whenever
there was a “public” in a “client”
relationship with the provider.
Teaching Training
Directing
instructions
proofs orders
testing
monitoringgrading
aBut the emergence of privately-driven
communities through the internet and
social networking has popularized the
notions and attempts of self-help, self-
organizing, and ad-hoc collaboration.
Self-development immediately inherits key challenges, namely: to understand
the differences, relationships, and implications of three pairs:
instructions/orders; orders/proofs; and, proofs/instructions. This
understanding results in, respectively, the utility, certainty and reliability – i.e.,
the practical meaning – of the knowledge.
In effect, those notions and efforts have
moved authority out of dependencies on
institutional pre-requisites, and have
cultivated self-development of all three
key knowledge delivery practices:
teaching, training and directing.

Source Forms Issue Meanings Objectives Knowledge Value
Teaching proofs/instructions why vs. how certainty perspectives confidence
Training instructions/orders how vs. what utility purposes relevance
Directing orders/proofs what vs. why reliability impacts effectiveness
When we look up the definition of cognition, we generally get statements describing it as an ability or faculty for processing
received information, along with the state achieved as a result. But our circumstances now tend to surround us with information
that is unattributed, excerpted, repurposed, or in other ways unpredictably sourced and ambiguously targeted. This changes the
baseline function for obtaining credible at-large knowledge, from cognition to re-cognition.
By function, we mean that knowledge candidates are triaged systematically. In practice, Objectives, as identified in the framework
above, represent the reason why information is presented as knowledge. In the course of knowledge recognition, the ability to
detect, decode and apply the reasons behind the presentation can be described as a set of actions making up “critical thinking”,
which is the essence of the editorial authority emerging in (or as) Big Learning. Recognition is, in fact, reformulation.
Within organically developing autonomous communities, this set of actions can be identified in a generic pattern, as shown here.
Environment Filtered search Classification Editing Re-presentation Environment
→ formalization contextualization re-composition expression →
Recognizing Knowledge through Critical Thinking
It is interesting and very instructive that throughout history, the broadest visible community of such practitioners has been artists.
Also noteworthy is the ongoing stream of scientific breakthroughs attributable to the knowledge transformation and transpositions
already characteristic of the arts, including a measurable “independence” from certain conventions of external authority.

The biggest demonstration
of having “learned”
is the ability to
produce knowledge on demand –
typically, a form for expressing
meaning that is
appropriate to the occasion.
Conventionally, that demonstration is offered
under conditions of testing or production.

But the conventional emphasis on
test results or productivity
are not the measures of successful learning.
Instead, those are measures of
the value of applying knowledge.
The correct proof of learning
is found in the evidence of
the thought process used
to interpret and reformulate
available intelligence.

In Big Learning,
exposing and re-purposing
patterns of intelligence
across multiple domains
becomes an ordinary, continual,
and ubiquitous activity.
This activity is
a natural adaptive behavior
in the current information landscape.
The goal is to be able to
cultivate it, train it and direct it
with a level of disciplinary awareness
that increases the probability
of beneficial effects.

Big Learning

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