George Siemens at TEDxNYED (3 June 2010)

A few of my favorite excerpts from George Siemens’s Knowing Knowledge (2006)

Reda SadkiTheory

My own practice (and no doubt yours) has been shaped by many different learning theorists. George Siemens, for me, stands out articulating what I felt but did not know how to express about the changing nature of knowledge in the Digital Age. Below I’ve compiled a few of my favorite excerpts from his book Knowing Knowledge, published in 2006, two years before he taught the first Massive Open Online Course (MOOC) with Alec Couros and Stephen Downes.

Learning has many dimensions. No one model or definition will fit every situation. CONTEXT IS CENTRAL. Learning is a peer to knowledge. To learn is to come to know. To know is to have learned. We seek knowledge so that we can make sense. Knowledge today requires a shift from cognitive processing to pattern recognition.

Figure 5 Knowledge types

Construction, while a useful metaphor, fails to align with our growing understanding that our mind is a connection-creating structure. We do not always construct (which is high cognitive load), but we do constantly connect.

We learn foundational elements through courses…but we innovate through our own learning.

Figure 17 Learning and knowledge domains

The changing nature of knowledge

The Achilles heel of existing theories rests in the pace of knowledge growth. All existing theories place processing (or interpretation) of knowledge on the individual doing the learning. This model works well if the knowledge flow is moderate. A constructivist view of learning, for example, suggests that we process, interpret, and derive personal meaning from different information formats. What happens, however, when knowledge is more of a deluge than a trickle? What happens when knowledge flows too fast for processing or interpreting?

Figure 23 Knowledge as process, not product

Knowledge has broken free from its moorings, its shackles. Those, like Francis Bacon, who equate knowledge with power, find that the masses are flooding the pools and reservoirs of the elite. […] The filters, gatekeepers, and organizers are awakening to a sea of change that leaves them adrift, clinging to their old methods of creating, controlling, and distributing knowledge. […] Left in the wake of cataclysmic change are the knowledge creation and holding structures of the past. The ideologies and philosophies of reality and knowing—battle spaces of thought and theory for the last several millennia—have fallen as guides.

Libraries, schools, businesses—engines of productivity and society—are stretching under the heavy burden of change. New epistemological and ontological theories are being formed, as we will discuss shortly with connective knowledge. These changes do not wash away previous definitions of knowledge, but instead serve as the fertile top of multiple soil layers. […]

Or consider email in its earlier days—many printed out a paper copy of emails, at least the important ones, and filed them in a file cabinet. Today we are beginning to see a shift with email products that archive and make email searchable and allow individuals to apply metadata at point of use (tagging).

Knowledge has to be accessible at the point of need. Container-views of knowledge, artificially demarcated (courses, modules) for communication, are restrictive for this type of flow and easy-access learning.

Everything is going digital. The end user is gaining control, elements are decentralizing, connections are being formed between formerly disparate resources and fields of information, and everything seems to be speeding up.

“Know where” and “know who” are more important today that knowing what and how.

Figure 16 Know Where

Once flow becomes too rapid and complex, we need a model that allows individuals to learn and function in spite of the pace and flow.

We need to separate the learner from the knowledge they hold. It is not really as absurd as it sounds. Consider the tools and processes we currently use for learning. Courses are static, textbooks are written years before actual use, classrooms are available at set times, and so on.

The underlying assumption of corporate training and higher education centers on the notion that the world has not really changed.

But it has. Employees cannot stay current by taking a course periodically. Content distribution models (books and courses) cannot keep pace with information and knowledge growth. Problems are becoming so complex that they cannot be contained in the mind of one individual—problems are held in a distributed manner across networks, with each node holding a part of the entire puzzle. Employees require the ability to rapidly form connections with other specialized nodes (people or knowledge objects). Rapidly creating connections with others results in a more holistic view of the problem or opportunity, a key requirement for decision making and action in a complex environment.

How do we separate the learner from the knowledge? By focusing not on the content they need to know (content changes constantly and requires continual updating), but on the connections to nodes which continually filter and update content.

Here is what the connectivism implementation cycle looks like as a mind map. (Click on the image to download the PDF).

Connectivism implementation cycle (George Siemens, 2006)

Source: George Siemens, Knowing Knowledge (2006).

Image: TEDxNYED

How to solve it

How to Solve It

Reda SadkiCulture, Learning, Quotes

Understanding the problem

First. You have to understand the problem.

  • What is the unknown? What are the data? What is the condition?
  • Is it possible to satisfy the condition? Is the condition sufficient to determine the unknown? Or is it insufficient? Or redundant? Or contradictory?
  • Draw a figure. Introduce suitable notation.
  • Separate the various parts of the condition. Can you write them down?

Devising a plan

Second. Find the connection between the data and the unknown. You may be obliged to consider auxiliary problems if an immediate connection cannot be found. You should obtain eventually a plan of the solution.

  • Have you seen it before? Or have you seen the same problem in a slightly different form?
  • Do you know a related problem? Do you know a theorem that could be useful?
  • Look at the unknown! And try to think of a familiar problem having the same or a similar unknown.
  • Here is a problem related to yours and solved before. Could you use it? Could you use its result? Could you use its method? Should you introduce some auxiliary element in order to make its use possible?
  • Could you restate the problem? Could you restate it still differently? Go back to definitions.
  • If you cannot solve the proposed problem try to solve first some related problem. Could you imagine a more accessible related problem? A more general problem? A more special problem? An analogous problem? Could you solve a part of the problem? Keep only a part of the condition, drop the other part; how far is the unknown then determined, how can it vary? Could you derive something useful from the data? Could you think of other data appropriate to determine the unknown? Could you change the unknown or data, or both if necessary, so that the new unknown and the new data are nearer to each other?
  • Did you use all the data? Did you use the whole condition? Have you taken into account all essential notions involved in the problem?

Carrying out the plan

Third. Carry out your plan.

  • Carrying out your plan of the solution, check each step.
  • Can you see clearly that the step is correct?
  • Can you prove that it is correct?

Looking Back

Fourth. Examine the solution obtained.

  • Can you check the result? Can you check the argument?
  • Can you derive the solution differently? Can you see it at a glance?
  • Can you use the result, or the method, for some other problem?

Summary taken from G. Polya, “How to Solve It”, 2nd ed., Princeton University Press, 1957, ISBN 0–691–08097–6.