Technical Architecture of A Learning Community

What’s Actionable on this Page!

  • Understand the key foundational activities of a Learning Community: Focusing, Filtering, Dynamic Course Updates, & Defined Learning Communication Codes.
  • Begin to see how you can apply the Learning Community approach to your life, the lives of your family, friends, and/or co-workers.


Summary Video

Why we need “Learning Communities!”

In the old days, inquisitive minds, like Ben Franklin and Isaac Newton before him, could conceivably have read all the important books on a particular subject.

That is impossible today.  There is just too much information within easy reach of everyone.

The Blind Researchers Describing an Elephant

There is a classic parable about blind researchers describing an elephant.


This parable suggests that while all of the individual researcher’s perspectives could be correct, their reality is inherently bounded by the physical limitations of human perception.  And this problem, of really knowing stuff, is greatly exacerbated today because there is so much more stuff available to us.

But, despite the hurdles it is very possible to overcome some of our perceptual limitations by using the same tools that are creating all this information.  By using 21st Century tools we can take practical steps to optimally manage all the information.

3 Step Process to Help Manage all the Information Available and overcome the perceptual limitations of blind researchers trying to describe an Elephant:


In other words, one could actually experience truth, but that experience of truth does not rule out other experiences and other truths.





There is more information than any one person can optimally process.  So, we need a Learning Community to help us optimally manage all this information!

 A Learning Community is identified by:

  1. An agreed upon communication process that facilitates the optimum movement of information between people.
  2. Small groups of individuals that connect with other small groups of individuals.  (How small depends on the context of the learning desired.)

Combining these two things into a learning structure allows any larger community to maximize its ability to grow and succeed.

I’ll describe each one in detail.

#1 – The Learning Communities Communication Process That Facilitates Optimum Information Movement

The Community captures as much raw data (in the form of Text, Pictures, and Videos) as they can.  Then the data is broken up into three buckets: Facts, Conclusions, and Recommended Actions.  These buckets are given “meta-tags” that help the community process the information optimally.

The key is the “meta-tags” associated with each fact, conclusion, and/or recommended action.  I describe these meta-tags in more detail in Learning Community Communication Architecture.  But, let me provide a high level overview here.

  • Data is the easiest to describe.  Data are simply the 1’s and 0’s in a computer.  They are the PDFs, JPEGs, MP3s, MP4s, or any text, pic, or video formate.  Meta-tags for data would be all the usual things one would expect, creation date, creator, name, keyword descriptors, etc.  Additionally there would be multiple copies to help ensure integrity.
  • Recommended Actions are also pretty easy to describe.  They are simple statements of what do with the data.
  • Facts are somewhat subjective, so there must be a method in place to remove all, or at least, some of the subjectivity.
  • Conclusions are totally subjective.  They should be taken as such and we should not try to make them objective.
  • Actions test of the process.  If the actions yield the desired result then the conclusions can be consider “valid.”

Option 1 – Individual members of the community study the raw data for facts.  The facts are accumulated and based on those facts conclusions are drawn with the intent of action.



Option 2 – Individual members of the community study the data and generate conclusions.  Then based on those conclusions facts are found to support those conclusions.



Option 2 is an interesting option.  It is the basis of the “Scientific Method.”  In the “Scientific Method” one asks a question, constructs an hypothesis, conducts experiments to generate facts that support the hypothesis.  Yet, this option has the potential for an extremely serious flaw in that it lends itself to the emotion-biased decision-making phenomenon known as “Motivated Reasoning.”   Fortunately Learning Communities can offset the negative consequences of this option by insuring that all relevant facts are considered.

Let me give you an example here.  Go back to the parable of the blind researchers describing the elephant.  Let’s say Bob is the blind researcher holding the trunk.  Bob concludes that an elephant is long and thick.  Under motivated reasoning, the Bob would stop there because the facts support Bob’s conclusion.  But, because there are other researchers providing other data, if Bob is reasonable than Bob would have to admit that the facts as Bob knows them are not the only facts that influence the conclusions of what an elephant is.




Two Examples of “Learning Communities” In Physical Systems – The Telescope and Cellular Networks!

Fortunately there are two excellent physical models that describe the basic architecture of a Learning Community, The Telescope and Mobile Networks.  Both illustrate how we can improve information gathering and processing by linking smaller groups together.

In both cases we found that one large Lens or Mirror in a Telescope or one large radio transmitter in mobile networks, while effective at first, was not optimal for maximum information processing.  In both cases we found that by breaking the process down to small cells and then linking the cells together we could improve the information processing of Telescope and mobile networks by many orders of magnitude.


Telescopes went through three distinct designs.

The first design was a “lens”

Problem was that a “Lens” could only get so big.

So we invented the “Reflecting” Telescope.  The Reflecting Telescope uses a large Mirror to gather more light.

But, as with the lens based telescope, the mirror could only get so big.

So the next development was to break the one large mirror (no pun intended) into smaller mirrors and network them together.

This same architecture is seen in the Telescope mirror at the Keck Observatory in Hawaii.  In the Keck Mirror, a lot of individual mirrors networked together.



Cellular Networks

Before there was what we know of as the “cell” phone, there was the “car phone.”  They both worked using radio waves.

But, the first car phones had only 1 large “Cell” that covered a large area.

The problem was that, like the telescope, that architecture could only handle a small amount of traffic.  (This traffic could also be called “bits of information.”  One could say that early telescopes and the first car phone system could only handle a small amount of “Bits of Information.”)

The problem of limited traffic was solved the same way we solved the telescope problem, but using a lot of small cells and networking the cells together.


Applying this to Learning

The same can be applied to learning.  At one time a Classroom with a few basic books and a teacher was sufficient for any community.  One teacher could teach all the necessary stuff a student would need.

But, as the number and diversity of students increased and the quantity of information and the ability to access information exploded having one large class was no longer optimal.

The evolution from first single room schools to next classes divided by subject and level and then next to  “Learning Communities” follows the same evolutionary path as telescopes and Cellular Networks.  Rather than having one large learning institution, we need to break learning into a lot of small communities.  Each one can then focus on much more narrow interests.

The challenge for learning communities is the same as the challenges for cellular networks and telescopes in that the challenge is linking all these communities together optimally.  Again, this is where the telescope and the cell networks are informative.  The way both of these technologies accomplished this linkage is through a communication “Standard.”  Once the “Standard” is defined, the number of “cells” or “learning communities” that can be linked together become quite large.  (It may even be infinite.)

So, just as the telescope and mobile networks found that breaking down the information processing to small cells and linking the cells together, so to, can learning be improved by breaking the learning down into small communities of interest and then linking those communities together.


The Learning Community Architecture is made up of three fundamental activities:

  1. Filtering & Focusing – Concentrating on the important stuff and blocking out the noise

  2. Dynamic Course Updates – Improving the course continually

  3. Defined Learning Communication Codes – Standardizing the way people share information

While traditional education uses these same activities, it is the use of 21st Century “Electronic Tools” that sets the Learning Community apart from traditional education structures.  Using 21st Century tools allow the community to implement this new educational paradigm. (Here is a link to a Post that will provide some additional support for this idea)

Filtering and Focusing help us sift through all the information available to find the nuggets of actionable information.  Much like a miner filters and focuses the mining efforts to create a new end product, filtering and focusing information helps a learner create new learning.

Dynamic Course Updates is the process where the benefits of the filtering and focusing are stored and paid forward to the next class by updating the course real time as each learner contributes to the class.

In the past, a class would be created and taught to successive classes virtually unchanged.  Any learning that might have accrued is lost the moment that learner leaves the class.

Rather than seeing individual learning as the end product of a class, a Learning Community sees the end product as the overall learning of the Community.  In a Learning Community a learner and teacher are both contributing to the entire educational process.  The goal of the learner is not only learn for themselves, but also to contribute to the learning of all other community members that come after them.

Defined Learning Communication Codes are the specific “Meta-Communication” codes used by the Learning Community to move bits of information between learners.  In order for optimal learning to occur the members of a Learning Community are better served if they use an agreed upon method of communication, which includes syntactic, semantic, and cultural codes.  This is exactly why scientists use the Scientific Method.



The Learning Community is set up to seek out new knowledge and add that knowledge to the total accumulated knowledge of the community.

Every learner is encouraged to, not only find new learning that helps them personally, but new learning that helps the entire community.  In that way classes are dynamic in the extreme.  Every new learner taking a class should be able to add value to the next learner taking that class.

The idea that learning exists only in a classroom and disappears as soon as the class ends is anathema to a Learning Community.



Putting It All Together

The key here is that “Learning Communication” that flows between the various @lantis Learning Communities be as “transparent” as possible so that each Learning Community can value that information optimally for them.



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