Pictographs were our first language. We've used symbols to
express ourselves long before alphabets were created. We sought guidance
through the arcane reading of thrown yarrow sticks or engraved animal bones. We
looked for patterns in the randomness of the night sky with it's brilliant
backdrop of stars and planets and called them constellations. Today the
symbolic language of mathematics has brought a sophistication of technology
that changes and advances in moments instead of months and years.
It is clear to me, as a long-time student of both mythology
and technology, that our minds seek out symbols first. This is where we are
most comfortable, particularly when we're attempting to learn new things. The
non-linear randomness of a symbolic language speaks to us clearer, and deeper,
than a linear alphabet can ever do. While we cannot escape the use of
words altogether (nor should we), we can organize and present our thoughts in a multitude of ways (images, diagrams, audio, video, interactivity exercises) that are more palatable for the mind's learning schema.
It seems to be nature's way to move from chaos to order. We see it everywhere, from the formation of hurricanes to the self-organizing properties of ant colonies. Thanks to the advent of Complexity theory, and particularly the work of Dr. Christopher Langton and The Santa Fe Institute, we know that there exists a place in nature where chaos and order meet known as the Edge of Chaos. This is also the optimal place for learning to occur because it provides just enough order for the brain to file new information and just enough chaos to spawn spontaneity and creativity.
In Meta-mapping™, the instructional designer mimics the natural flow of chaos into order by breaking apart the subject of the proposed course into it's smallest constituient parts, randomizing them, and watching for new patterns to emerge. This is related to Robert Gagne's Learning Hierarchy, which is constructed by working backward from the course objective, dividing the process into smaller and smaller parts. Meta-mapping adds randomization and pattern-recognition to this well-known technique.
Randomization
Once the high level goal of a project is known, the process of understanding how best to arrive at that objective must be broken down into it's disparate parts and randomized. One of the best ongoing examples of this process can be found in Genetic Programming, as developed by John Koza of Stanford University. In the introduction to his book Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Koza reminds us that the two criteria for a patentable invention are novelty and creativity. In other words, if an idea is submitted for a patent, and if that idea can be logically arrived at by facts already known, then a patent is not granted. There must be an illogical step somewhere in the process which, when followed, leads to a new and innovative product, process or service. In Koza's own words, "genetic programming often unearths novel solutions to problems because it does not travel along the well-trod paths of previous human thinking." The genetic algorhythms used in Genetic Programming are based on the evolutionary work of Charles Darwin, as well as certain naturally-occuring processes like mutation, gene duplication, and sexual recombination (also known as Crossover).
