Information – Knowledge – Learning

The 2007 Online Connectivism Conference has definitely got my head spinning. I still haven’t had a chance to post my thoughts on Will Richardson’s presentation on Monday! This post is an attempt to wrap my head around two concepts that came out of the message board during Stephen Downes‘ presentation yesterday.

There was a lot of buzz over the interchangeable use of the terms information and knowledge, how they relate to learning and whether connectivism was simply another term for pattern recognition. I see a difference, almost a continuum between the terms: information, knowledge and learning. I envision information as a discrete and independent entity. Information can exist even if it is not known – that the earth was round and not flat was information that existed prior to its “discovery.” Knowledge is the product of consuming, owning or internalizing information. Giving it a personal context. Learning is the application of that knowledge to a certain end.

Here are two examples, one is for the geeks out there, the other is for the rest of us.

The Database Example
In a database, information exists discretely but doesn’t become data until, as input, it is entered into a table. Once information is known by the system, it becomes data (or knowledge). And yet the simple storage of data in a table does not have use to us until we combine it with other data and apply the result to satisfy a query. So data/knowledge is inert until it is used and applied in a specific context to solve a problem (for some reason, this reminds me of potential versus kinetic energy). The application of knowledge to satisfy a problem constitutes learning and as this process recurs our learning expands geometrically.

The Lego Example
With the Lego example, information is much like a Lego block, in that you may not own Legos but they still exist. Holding a Lego block or a number of blocks in your hands, that’s knowledge. But until you apply a context and then use those Legos to build, to solve a problem – that to me is equivalent to learning. As is the process of disassembling and reassembling those blocks to create new objects based on new contexts and new problems seeking solutions.

Pattern Recognition
Much of the message board discussion spoke of pattern recognition as a fundamental component of connectivism. We agreed that pattern recognition is contigent upon context and is a basic aspect of the human learning process. However I am not sure that it is an innate feature as opposed to a learned skill. Is the ability to recognize patterns not dependent upon our ability to first develop associations, relationships, connections between seemingly disparate pieces of knowledge? It would seem to me that pattern recognition is a meta-skill built upon connective skills. But I’m still trying to wrap my mind around all of these incredible ideas that the participants have been throwing out there.

So Stephen, if you happen to come across this rant would you mind tossing your thoughts my way?

[tags] information, knowledge, learning, pattern, recognition, 0cc2007 [/tags]

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2 thoughts on “Information – Knowledge – Learning

  1. “However I am not sure that it is an innate feature as opposed to a learned skill.”

    It is both.

    Human neurons naturally form connections. That is, in fact, their sole function. Any time they are presented with input (such as experience) they will react by strengthening or weakening connections. Because these connections are sensitive to input, they will reflect patterns in that input. This is not a conscious act; it is not the same as saying we are looking for patterns. It’s more like the way you distinguish between red and blue. You just do it.

    After a certain period of time, this process results in a base of pre-existing patterns of connectivity in the mind. The child, for example, has learned to identify objects. Slightly older children, for example, have learned to recognize faces. These pre-existing patterns now influence the recognition of patterns in perception.

    There comes a point where the recognition of patterns in the environment will depend entirely on the influence of these pre-existing patterns. The distinction between subtle shades of red, for example, that the artist can make. The ability to identify a type of wine. The capacity to apply mathematical forumulas to equations. In such cases, it would be correct to say that pattern recognition is entirely a learned ability.

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