Big LearningHow We Need To Know Things,

And Why We Need To Know How

© 2014 Malcolm Ryder / archestra research

The internet has exposed us toa vast heterogeneous landscape of

content and ideas, with unprecedented ad hoc ability tonavigate, 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 deriveconsistent meaning from the

breadth of disparate information sources.

In that environment,learning increasingly occursthrough nothing less than

the employment ofthe intellectual mode used

as a basic cognitive instrument in art.

Concepts from one context are ported to another context, and received there as catalystsof modelling new knowledge.

Today, knowledge transferincreasingly means that

concepts are not just an end point product in one subject.

They are alsoa beginning of new product development

in another subject.

The “transfer of knowledge” is really a delivery –

of concepts from intelligence.

But more specifically it isa naturally emerging adaptive behavior,of increasingly self-propelled learners,

in the complexity and density ofthe current information landscape.

In Big Learning, exposing patterns of intelligence

and re-purposing themin multiple domains

becomes anordinary, continual, and ubiquitous

activity.

BIG LEARNING: The Notes

Mind MappingThe History of “Knowing”The Information Landscape

Getting EducatedBecoming “Learned”A Learning DynamicAcquiring ConceptsProducing Knowledge

The Ambiguity of ContentProcessing ContentProgramming LearningIntellectual “autonomy”Designing KnowledgeMeta-Knowledge

The Diversity of ApproachManaged LearningProof of Learning

Intellect, ideas and knowledge are different but systemically relatedThe value of knowledge is in adaptation across environmentsMulti-environment adaptability is the education agenda

The value of teaching is to predispose effective performanceSelf-service is a naturally emerging adaptive behaviorKnowledge develops through a managed information transferMeaning is derived through contexts and affinitiesIn learning, interpretation creates meaning from intelligence

Validation of content is a prerequisite of its value as knowledgeThe promoter’s purpose must be compatible with the user’s intentionLearning is a capability, and Education is developmentalAlternative paths of knowledge gain are the next normalSelf-service learning leverages frameworks and models for valueCritical thinking is the default paradigm of learning

Freedom of thought is natural and should become normal in practiceLearning requires both facilitation and authorityLearning 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

Mind Mapping

Idea Presentation

Production Value

Package Supply Value

SupplyEvent

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 andof 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.

©2014 Malcolm Ryder / archestra

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

Ide

a Pro

cessin

g

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

©2014 Malcolm Ryder / archestra

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

©2014 Malcolm Ryder / archestra

The History of “Knowing”

Beyond grand unification theories or religions, there is always

both “more” and “different” knowledge than we have already gained.

Hypothetically, we could stop learningat 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 purposesof 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 knowledgewas 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 tochange the environment.

When the environment was re-createdby 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 abilityto 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 includethe 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 extractproductive meaning from

heterogeneous sources of intelligenceis similar to the new achievement of “Big Data”,

but instead we must see it as Big Learning…

The Information Landscape

Now, more than ever before,thanks to information technology,

multiple environments are virtually created from information.

Each environment is a context withinAn overall information landscape.

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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 reappearin a different context.

That event raises the question of whether the meaning has thereby changed, and of whetherit 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 environmentsrequires 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.

Getting Educated

The value of knowledge is, ultimately, always measured in terms of some kind of performance.

But knowledge acquisition does notdetermine 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 toexecute 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 teachingis ultimately valuable

without effective learning.

Becoming “Learned”

In our information-saturated times,the teaching-learning relationship

is still indispensable todeveloping large targeted communities of

effectively educated individuals.

However, learning is becoming increasingly enabled byself-service technology

that both produces and exploresknowledge 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 changesthe individual’s ability to teach,

Thus it also means thatthe 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 optionin driving intellectual development.

But regardless of any deliberate intent,the self-service option is essentially anaturally emerging adaptive behavior

responding to the complexity and density ofthe current information landscape.

In that behavior, the bias (demand) is towards learning,and the key question is,

what is being learned, and how?

A Learning Dynamic

We normally think of a “learning event”as being

an acquisition of knowledge achieved bya 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 exampleas 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 itis given to teachers.

But today, an increasing reliance onself-service learning opportunities

means that the learners must take upmore 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©

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Acquiring Concepts

Prior experience is often documented as intelligence about a subject.

Collections of preserved informationpresent 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 indicatedifferent concepts simultaneously.

Conventionally, designated domainshost and cultivate certain conceptswithin 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 asverified, maintained “material” (content)

that we may call knowledge.In effect, they “administer” knowledge.

In that way, the disciplines and domains managethe 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 individualallows content – and therefore concepts –

to be freely transported and includedacross their different circumstances,

not just across different peoplein the same circumstance.

The diversity of circumstances adds uncertainty, variety, and/or noveltyto how concepts will be recognized

by an individual receiving information.

Differences in recognitioncause differences in meaning.

An individual receiver’s circumstanceis 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

Producing Knowledge

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 Ambiguity of Content

The development effort called educationis itself an experience.

The value of the education experienceincreases along with

the complexity that comes from today’s super-abundance

of information and intelligence.

Abundant availability of intelligencedoes not necessarily causecommensurate usability,

and it may even complicate it or reduce it.

The challenge created by this abundanceis due to both

packaging and delivery.

The process of learning that leads to the occurrence of knowledge

presumes thatinformation has a usability

based on itscredibility, persistence and authority –

in other words, reliability.

But the portability of information can readily create

ambiguity about how or whyinformation is reliable.

The massive volumes of information in the current landscape

increasingly make “cross domain” and “cross discipline”

exposure of informationnormal, not exceptional.

That exposure of information gives itmore chances to be valuable

but not necessarily more likelihood of being valuable.

The mass exposure means thatconfusion 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 isinformation 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 ofexcerpts, proxies, relatives, and derivatives

of requested information,built into daily presentations along with original material.

Individualshave 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 emphasizesthe active decision-making that validates available information

as beingcredible and appropriate for

purposeful adoption.

Processing Content

In both context and content,a presentation of information

uses and provides the informationin a given form.

Learning requiresinterpreting 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 learningmust also train an explicit applicability

of that awarenessthrough techniques that extract and determine

the relevanceof the information.

The development of awareness and applicabilitywill 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

©2014 Malcolm Ryder / archestra

The exposure of compositionhelp 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 informationas further-usable ideas.

The main challenges to that exposurenow come from

the ease of hit-&-run content consumption…

Today’s super-high availability of contentand speed of access to it

encourage an impatience in usage (applicability)that results in

taking content at apparent “face value”.

The superficial encounter with informationappears to suffice in the moment, but

content acquisition winds up substituting for learning.

A second challenge to the learning-oriented exposure

is thatoriginal 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 betweenwhat 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 misalignmentare 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.

Programming Learning

The result of compositionis the relationships of

the elements and componentsthat 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 automatetechniques that perform those constructions.The automation in turn makes the techniques

far more widely usable byindependently 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 insteadto have it reinforce appropriate applicability.

This reflects the goal of producingknowledge that has value.

As one example ofproduction automation,we now have Big Data –

the computerized analysis of relationshipswithin high volumes ofheterogeneous data.

Data is commonly seen as one level of element that isprocessed as a component

of knowledge.

We imagineBig Data having “constructive” impacton 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 aboutwhat “processing” occurs.

Big Data actually operates onunprecedented volumes of information

and offers intelligence as its product.Much of that product is then

packaged by other meansas “concepts”

which in turn are deliverable asknowledge and expertise.

In effect, the packager largely decides what the intelligence will appear to “mean”.

©2014 Malcolm Ryder / archestra

Discovery, selection, analysis, packaging, delivery,and other “production” tasks

are increasingly gaining automation and integrationin their manipulation of ideas.

These provide opportunities to “program” the overall effort thattakes 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 originsis integrated with

the ability to interpret content in whatever context.

The education effort coordinatesthe directions, priorities and

levels of attention to how

ideas have been managedfrom the producer to the recipient user.

New technologies that help to automate that coordination

continue to become available to individualsfor 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 adesigned experience

that develops and rehearsesthe ability to learn.

Intellectual “autonomy”

We typically identify expertise as the highest value product of

developing knowledge.

As a product, its availability and variety

is distinguished for the user bywhere 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 thatthe acquisition occurs when

knowledge is transferredfrom 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 whataspects of the delivery should beexplicit 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.

Designing Knowledge

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 contentby 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 conceptconcludes with

comprehending it, throughclassification and prioritization for implementation.

In implementation,learning requires an active attitude

towards applicability of the way thatcomposition was seen to have formed key concepts.

For example, this may be done with a model.

Both in apprehending intelligenceand in comprehending concepts,

the user of informationcan 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 digitizationare 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 exposureto 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 technologiesgenerate 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 toincreasingly abundant content,

learners have the significant problem of handling and vetting the workload.But the potential readily exists fora 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

©2014 Malcolm Ryder / archestra

“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

©2013 Malcolm Ryder / archestra

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 individualcan consciously consider the mechanisms,

the more autonomous the person isas a learner and as

an eventually knowledgeable individual.

Meta-Knowledge

Concepts in one domain can be abstracted as recognized patterns

and transportedinto another domain.

The pattern is something that can be maintained for reference and re-presentation.

Patterns are formed bythe 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 theirinfluence on other events, are “effectively real”. These “mined” virtual phenomena have the same status that “concepts” do incollections 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 anotherdomain, where they provoke new recognition of forms (structures or conditions) in the target domain that were latent there butnot 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, andothers that we know are casual or even accidental occurrences – not just as intentional ones. In Big Learning, involvement inthe dynamics is especially through the vehicles of networking, collaboration and modeling that are now intensively proliferatingin 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 thestate of the presented information. These indicate why the information should be important as “knowledge”. Where thepresentations actually allow the information to be re-presented in circumstances other than its origins, the processing that isneeded (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

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What we expect in teaching is that conveying information will occur in a way that acknowledges therecipient’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 previousencounters or knowledge.” In that statement the important factor to note is not the thing or person fromprevious experience; rather, it is the “identification” from previous experience. In Big Learning, what alsoarrives 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 throughacknowledging how the information is intended to indicate concepts. This critical aspect of translation isnothing 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)

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The Diversity of Approach

Producing meta-knowledgehas always been a responsibility

of educators of knowledgeand managers of knowledge.

Those parties work in collaboration withsubject matter experts.

Their ability to provide meta-knowledgeis 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 situationsto 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 usdramatically confront this limitation.

Managed Learning

The self-service opportunity to learn independently

is now more sustainable at increased levels.

But the value of acquired knowledgeremains predisposed (not predetermined) by

the delivery of processed content.

That delivery is what comprisesthe 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 meansboth 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 changedby new technologies.

Nonetheless, the biggest challenge to that editorial involvement is

the malleabilityof 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 toinstitutional (and corporate) information vehicles

that had economies of scaleand, 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 tothe 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 increasinglya function of consumer consensus,which mainly tracks a correlation of

teaching approaches witheffective 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 beena 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 abroader 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 meanslearning 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. Editorialauthority has most frequently beenhoarded or made “exclusive” byinstitutions and individuals wheneverthere was a “public” in a “client”relationship with the provider.

Teaching Training

Directing

instructions

proofs orders

testing

monitoringgrading

aBut the emergence of privately-drivencommunities through the internet andsocial networking has popularized thenotions and attempts of self-help, self-organizing, and ad-hoc collaboration.

Self-development immediately inherits key challenges, namely: to understandthe differences, relationships, and implications of three pairs:instructions/orders; orders/proofs; and, proofs/instructions. Thisunderstanding results in, respectively, the utility, certainty and reliability – i.e.,the practical meaning – of the knowledge.

In effect, those notions and efforts havemoved authority out of dependencies oninstitutional pre-requisites, and havecultivated self-development of all threekey knowledge delivery practices:teaching, training and directing.

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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 processingreceived information, along with the state achieved as a result. But our circumstances now tend to surround us with informationthat is unattributed, excerpted, repurposed, or in other ways unpredictably sourced and ambiguously targeted. This changes thebaseline 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 frameworkabove, represent the reason why information is presented as knowledge. In the course of knowledge recognition, the ability todetect, 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.

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Proof of Learning

The biggest demonstration of having “learned”

is the ability toproduce knowledge on demand –

typically, a form for expressing meaning that is

appropriate to the occasion.

Conventionally, that demonstration is offeredunder conditions of testing or production.

But the conventional emphasis ontest results or productivity

are not the measures of successful learning.

Instead, those are measures of the value of applying knowledge.

The correct proof of learningis found in the evidence of the thought process used

to interpret and reformulate available intelligence.

In Big Learning, exposing and re-purposing

patterns of intelligenceacross 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.

© 2014 Malcolm Ryder / archestra research

mryder@malcolmryder.com