A most interesting approach to thinking about ID! I'll keep my reactions brief and relevant to specific portions of the text:
[quoting Spector & Davidsen's paper] For the purposes of this discussion, learning refers to intentional learning and involves persisting, stable, and observable changes in an individual.
Are these "changes" equal to "acquisition of knowledge?" Shouldn't learning involve some knowledge acquisition? Don't numbers 4, 5, and 6 below imply this?
Learning environments can be described in terms of the following attributes: ... (4) a knowledge base pertinent to a subject matter area, possibly with links to other knowledge bases (e.g., a collection of learning materials or an instructional database); (5) learning goals, objectives, and desired outcomes (these may be negotiable); and (6) a set of possible relationships and interactions among the actors, roles, knowledge base, and settings which facilitate or contribute to accomplishment of goals and objectives.
These are nice distinctions, and I shall steal them, if I may!
SD suggests that a complex system can be described in terms of stocks (containers) of things or substances, the quantity of which may change over time.
Are "bits of knowledge" stocks, too? How do you divide up bits of knowledge into different "containers?"
The change in the levels of the stocks over time constitutes the behavior of a system.
May we assume, then, that an increase in the "level" (quantity?) of the knowledge stock (bits of it) indicates an increase in learning?
We might start with a rather vague learning goal, such as understanding how the population of a particular species is effected by a predator species in that environment. In other words, our task is to design a learning environment to facilitate learning about, for example, how a prey population fluctuates in relationship to a predator population.
Far from vague, this learning goal addresses a specific quantitative measure: the relative sizes of two populations. The behavior of the system can be described by the "principle" of inflow and outflow you mention.
To make this exercise more personally meaningful and motivating, we leave it to readers to supply their favorite prey/predator pairs (e.g., rabbits and cougars, rats and cats, little fish and big fish, etc.).
More than personally meaningful, is not the choice of species in fact the question of greater interest and value? Environmental policy making should not dismiss the differences between species and their unique patterns of interaction on the basis of personal favorites. The principle to be learned in this simplified scenario remains that of inflow and outflow.
Let's accept for the time being a small set of more or less well-established ID principles ... The problem then arises how best to implement such principles.
Is this really "the problem?" Shouldn't you be looking for a completely new approach with, hopefully, greater observable learning outcomes than those well-established but not demonstrably effective principles have thus far yielded?
Learners can create and modify SD models, and these models will reflect changes and growth in their knowledge about a subject.
Yes! But how will you divide up and measure the bits of this knowledge "stock" as called for by the SD model?
Suppose, for example, that in our imaginary learning environment we want to initiate learning about predator/prey dynamics with an epitomizing and motivating example.
Why do you think this example is motivating?
We may have a ready made SD model concerning some particular predator/prey situation that developed counter-intuitively.
Why counter-intuitively? (And what is it?)
This model could be presented to learners, identifying key components of the model...
How, exactly, would it be presented?
...(not all of the model components and certainly not all of its complexity).
Why not?
Learners might then be asked to predict the behavior of this model over time.
Yes...
Then, we run the simulation based on the underlying model to show how the model actually behaves over time.
Okay.
If the outcomes of the simulation are significantly different from expected outcomes (the learner has already been asked to predict the behavior)... then we will have implemented an important ID principle using SD as a methodology.
No. Your student has simply eliminated one hypothesis, and you have given him/her the correct solution in one step. This is nothing more than the "corrective feedback strategy" (Hannifin & Hooper, 1993, p. 221).
...situation, and the courseware will provide two kinds of support: tools for building the model... and hints for using the tools for the specific purpose at hand.
I do believe that this is a good design strategy. It is similar to the ID model I use with adventure games.
Our learning goal involved the possibility of introducing a new factor into the modelÑa policy aimed at controlling one of these populations so as to moderate or eliminate the spread of a disease, for example.
But you might want to know why you have this policy! For example, the disease may be an important "balancing" component of a natural environment--like forest fires, not all of which should be extinguished. You might decide not to control the population or moderate the disease. On what basis, in this example, would you decide? The learning goal you have selected results only in a quantitative analysis of several variables affecting relative population sizes over time. But how would you teach the more complex implications of this quantitative analysis that depend on subjects making explicit their own values vis-a-vis species preference and other factors--intuitive or counter-intuitive? Do you have "value stocks" too?
This can be thought of as a process of hypothesis formulation and testing (as could the model construction process), and it should now be obvious that SD provides very powerful support for this process (e.g., learners can now interact with models they have created and observe consequences).
What kind of simulation are you describing here? Will you have animations of animals roaming around in the woods near the town, some sick and some eating the others? Wouldn't a spreadsheet work just as well for this type of analysis and hypothesis testing? If so, why do you need to explain it in system dynamics terms?
This has been tried in some selected situations within the system dynamics community and in secondary education settings, and the results are generally very promising.
Some references to these results would be helpful.
Hannafin, M.J., & Hooper, S.R. (1993). Learning principles. In Fleming, M., & Levie, W. Howard, (Eds.), Instructional message design: Principles from the behavioral and cognitive sciences (2nd ed.). Englewood Cliffs, NJ: Educational Technology Publications.