Things cannot be forever separate. Hence there follows the hexagram of Limitation.
Limitation. Success. One should not engage in bitter limitation.
Above the lake is water: This is the image of Limitation. Thus the superior person establishes measures and limits, and deliberates about the nature of virtuous conduct.
I used to hate drawing this hexagram; takes a while to understand the way things work, which is why it’s 60 out of 64, and why what I hope will work finds Limitation as the governing concept. I should point out that all that follows is an evolution of the 5 Friends proposal (search 5F here on LA for details); Limitation being what I was missing in terms of making it work. (Interestingly, return would be the logical transformational hexagram, with lines in either direction warning not to miss the moment.) I should also point out that 5 was a minimum number and not an upper limit; as we’ll see 9 may be a better bet.
Table Stakes: One of the problems with Social Media platforms generally and MOOCs in the educational particular, is that they’re just too damn big. Dunbar’s Number comes into play here, but even that 150 low-ball estimate strikes me as way too big for anything like a learning cell. The 5F proposal was based on this assumption, but I was basically skipping over how cells were going to be created. Before we get to that however a disclaimer and some tentative definitions.
This is not a full blown proposal, more like development notes and brainstorming. More than ever the target audience is myself; if I seem to be addressing a more general audience, write it off too another art-flake thing, like corespondinc with long ago imaginary playmates over on Live. You are, however, invited to ease drop and I’ll be grateful for comments and corrections, but don’t expect too much in the way of linearity.
Cellular Learning Network: This one is fairly obvious, but attractors vary significantly. Some networks are discipline specific, some more general, but two qualities are central: While individual cells may die, Networks persist, and Networks are interconnected. This last is important because it is how networks become cultures, which are the logical type directly above Networks in the proposed schema.
Learning Cells: A group of individuals who study/conduct research within a knowledge domain. Cells are connected to other cells via agent members; connections are primarily with other domain members, but occasional connections to other Knowledge Domains are important to overall network health. The minimal size for a Leaning Cell is 5 members; I suspect that the optimal size is 9 members, with 12 being the theoretical maximum size.
Cell Members: All participants but used specifically to indicate participants who have no connection with other cells.
Agents: Cell members who have a connection (perhaps membership in) another cell in the Knowledge Domain.
Strange Attractors: Cell members who have connection (again membership) to External Knowledge Domains.
The Set Up: Again we’re in Draft mode here and much of what I’m proposing needs a great deal of fleshing out. Membership in Learning Cells will be machine assigned, the the governing rules will vary, but will in all cases attempt to balance consistency with innovation, or perhaps better, rigor and imagination. We can imagine applicant profiles that include: how ofter he or she has voted since reaching their majority, whether they have taken biology, what type of music they prefer, sexual orientation, income, degree or accreditation status, etc. In certain instances testing, computational, vocabulary, or domain specific competencies. In other words pretty much any input parameters you could dream up. The key will be figuring out how much and what kind of diversity will give the best results.
Play: Here’s where thing get really odd. In thinking about all of this I was taken back to fractals and cellular automata. From there it’s a short trip to Conway’s Game of Life and Moore’s Neighborhood. Here are the basics:
The universe of the Game of Life is an infinite two-dimensional orthogonal grid of square cells, each of which is in one of two possible states, alive or dead. Every cell interacts with its eight neighbours, which are the cells that are horizontally, vertically, or diagonally adjacent. At each step in time, the following transitions occur:
Any live cell with fewer than two live neighbours dies, as if caused by under-population.
Any live cell with two or three live neighbours lives on to the next generation.
Any live cell with more than three live neighbours dies, as if by overcrowding.
Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.
We can’t directly map Learning Networks onto this schema, but it opens the door to some interesting possibilities. (You may have noticed that I use Learning Network and Knowledge Network interchangeably. I’m starting to think that we’d be better off talking about Knowledge Networks and Learning Cells.) If we assume that Knowledge Networks persist while Learning Cells are to one degree or another transitory, we will need rules (machine enforced rules) thad determine the viability of cells, and basing this on the number of Active Agents and Strange Attractors seems sensible.
I’m going to break off here, because I want to get this posted on the off chance that I can some external feedback, and I need to start thinking in both more general and more specific terms.