Big Learning
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With spectacular self-service exposure to the diverse information landscape of the web, learning is from this point onward a personal skill, not a group event.

With spectacular self-service exposure to the diverse information landscape of the web, learning is from this point onward a personal skill, not a group event.

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Big Learning Presentation Transcript

  • 1. Big Learning How We Need To Know Things, And Why We Need To Know How © 2014 Malcolm Ryder / archestra research
  • 2. 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.
  • 3. 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.
  • 4. 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.
  • 5. 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.
  • 6. In Big Learning, exposing patterns of intelligence and re-purposing them in multiple domains becomes an ordinary, continual, and ubiquitous activity.
  • 7. 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
  • 8. 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
  • 9. Mind Mapping
  • 10. 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. ©2014 Malcolm Ryder / archestra
  • 11. 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 ©2014 Malcolm Ryder / archestra
  • 12. 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
  • 13. The History of “Knowing”
  • 14. 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.
  • 15. 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.
  • 16. “Knowledge” is not actions; knowledge is ideas. But the value of knowledge is, ultimately, always measured in terms of some kind of performance.
  • 17. 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.
  • 18. 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.
  • 19. 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.
  • 20. 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.
  • 21. 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…
  • 22. The Information Landscape
  • 23. 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.
  • 24. ©2014MalcolmRyder/archestra
  • 25. 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.
  • 26. 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.
  • 27. 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.
  • 28. Getting Educated
  • 29. 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.
  • 30. 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.
  • 31. 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.
  • 32. Becoming “Learned”
  • 33. 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.
  • 34. 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.
  • 35. 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?
  • 36. A Learning Dynamic
  • 37. 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.
  • 38. 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.
  • 39. 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.
  • 40. 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.
  • 41. 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
  • 42. Acquiring Concepts
  • 43. 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.
  • 44. 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.
  • 45. 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.
  • 46. 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.
  • 47. 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.
  • 48. 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
  • 49. Producing Knowledge
  • 50. 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.
  • 51. 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.
  • 52. “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.
  • 53. 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?”
  • 54. The Ambiguity of Content
  • 55. 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.
  • 56. 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.
  • 57. 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.
  • 58. 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.
  • 59. 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.
  • 60. 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”…
  • 61. 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.
  • 62. 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.
  • 63. 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.
  • 64. 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.
  • 65. 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.
  • 66. Processing Content
  • 67. 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.
  • 68. 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.
  • 69. 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.
  • 70. 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
  • 71. 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.
  • 72. 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.
  • 73. 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.
  • 74. 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.
  • 75. Programming Learning
  • 76. 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.
  • 77. 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.
  • 78. 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.
  • 79. 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.
  • 80. 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.
  • 81. 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”. ©2014 Malcolm Ryder / archestra
  • 82. 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.
  • 83. 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.
  • 84. 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.
  • 85. As arranged by education, the pattern of coordination is for a designed experience that develops and rehearses the ability to learn.
  • 86. Intellectual “autonomy”
  • 87. 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.
  • 88. 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.
  • 89. 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.
  • 90. 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.
  • 91. 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.
  • 92. 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.
  • 93. Designing Knowledge
  • 94. 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.
  • 95. 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.
  • 96. 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.
  • 97. 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.
  • 98. 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.
  • 99. 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.
  • 100. 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
  • 101. “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
  • 102. 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.
  • 103. Meta-Knowledge
  • 104. 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.
  • 105. 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.
  • 106. 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
  • 107. 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) ©2014MalcolmRyder/archestra
  • 108. The Diversity of Approach
  • 109. 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.
  • 110. 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.
  • 111. 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.
  • 112. 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.
  • 113. 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.
  • 114. 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.
  • 115. Managed Learning
  • 116. 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.
  • 117. 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.
  • 118. 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.
  • 119. 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.
  • 120. 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.
  • 121. 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.
  • 122. 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.
  • 123. 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.
  • 124. 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”.
  • 125. 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.
  • 126. 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.
  • 127. 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.
  • 128. 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. ©2014MalcolmRyder/archestra
  • 129. 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. ©2014MalcolmRyder/archestra
  • 130. Proof of Learning
  • 131. 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.
  • 132. 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.
  • 133. 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.
  • 134. © 2014 Malcolm Ryder / archestra research mryder@malcolmryder.com