If You Build It, They Will Come

The concept of self-organization is a compelling topic. I liked the simulations on NetLogo with the birds, ants and slime. It was especially interesting to be able to read the information that related to each simulation. I was not aware that birds followed rules for flocking, that ants left a pheromone scent that would direct the efforts of other ants, or that slime (whatever that was) would eventually group given time. After observing these simulations, there was a greater “ahaaa” moment for me when Dave related the phenomenon of self-organization to OSLO and educational practice.

As it was, I was thinking that it would be nice to identify rules for the self-organization of learners. I appreciate Dave’s efforts to create such rules which he implemented with the simulation. These rules are much more complicated than those listed for the birds, ants and slime.

The following is an account of my experiences using the OSOSS NetLogo simulation:

I manipulated the variables multiple times and then set my screenshots. However, being the genius that I am, I did not capture my images correctly and had to start all over. This was the best thing that happened with this simulation, because, when I re-entered the simulation, it ran with the variables shown in the screenshot below. In my previous attempts, I had always ran it with more learners. In the attempt shown, it seemed that the outcomes were far better than any simulation I had run before with an inverse number of learners to learning objects. I was surprised to see such a positive effect on the knowledge of the average student; in addition to the far greater number of peer questions answered and posed, and the reduced number of “show-offs” responses. Refer to first screenshot posted below.

As a result, I ran two more simulations, one with the inverse relationship of learners (high) to learning objects (low) and the other with an equal relationship of learners to learning objects (high). In the first simulation, the average knowledge per learner reached a plateau just barely above where it was set. Furthermore, the number of peer posted and answered questions was moderate and the wait time for responses was low but steady, there was no decline in this rate.

In the next simulation, pictured below as second screenshot, there was an increase in learner knowledge, peer posted questions and answers and a decline in response wait time. However, the number of “show-off” responses was high, where they were lower for the other two simulations.

While these results seem good, I got the same results with the same number of learning objects and less learners. I would have thought that I would get a greater result with a greater number of learners. It may be that the combined knowledge of the learner is directly related to the number of learners. What does this mean? How can the variables be manipulated for the greatest outcomes? How can a simulation like this be used to improve education?

I don’t know. Those were the questions that Dave posed that I now understand and will continue to explore with my research and educational endeavors. In the end, I was most interested in the concept of the ants and the pheromone trail that they left to assist other ants in their quest for food. While humans can’t leave a pheromone trail in an electronic environment, they can leave a history trail which I think would be interesting to capitalize on to assist other learner in their quest for knowledge.