Learning about systems can be made easier through modeling. In natural systems, we
often see patterns we want to explain or we often ask why things happen. This process starts with
narrowing the scope of the model to explain a specific question.
We frame this question in terms of the Phenomenon of interest: (P) (e.g., Dead fish in a pond).
To generate these explanations and in focusing on the
phenomena, we next can think about the mechanisms or processes that are both
generic to phenomena like ours but also specific to the phenomenon of
interest: (M). If for example, we are explaining why the fish died in our pond, our next step would be why they
might have died in a generic sense (that is, anywhere). We would then evoke generic mechanisms
(such as, lack of food, air, space, etc.—which are often generically taught as food webs, competition,
respiration and cellular respiration and students may already know about these. In addition, these are
concepts covered in content standards). After we have thought about generic mechanisms, we can next
think about the evidence that would need to be present
if such mechanisms happened (that is, is there food, oxygen, enough space,
and how would we know?—What kind of data could be collected?): (E). From
there, we can begin to build our model of the pond.
We would build a model including the components or parts that we see in
the pond, which likely relate to our mechanisms: (C). We will next build
explanations based on our components (which means we would look at specific
evidence from our pond) and we will discuss our evidence in terms of whether our
ideas make sense (i.e., are they plausible, likely to have occurred? –we
will use our ideas about the generic mechanisms and our evidence to support or
refute ideas).
Next we rule out explanations based on that specific evidence gathered. Once we feel that our
model provides a causal explanation for why the fish died, we can use evidence
gathered through simulation and raw data to refine our model based on
plausibility and parsimony to support/refute our ideas. We call this PMC-2E.
often see patterns we want to explain or we often ask why things happen. This process starts with
narrowing the scope of the model to explain a specific question.
We frame this question in terms of the Phenomenon of interest: (P) (e.g., Dead fish in a pond).
To generate these explanations and in focusing on the
phenomena, we next can think about the mechanisms or processes that are both
generic to phenomena like ours but also specific to the phenomenon of
interest: (M). If for example, we are explaining why the fish died in our pond, our next step would be why they
might have died in a generic sense (that is, anywhere). We would then evoke generic mechanisms
(such as, lack of food, air, space, etc.—which are often generically taught as food webs, competition,
respiration and cellular respiration and students may already know about these. In addition, these are
concepts covered in content standards). After we have thought about generic mechanisms, we can next
think about the evidence that would need to be present
if such mechanisms happened (that is, is there food, oxygen, enough space,
and how would we know?—What kind of data could be collected?): (E). From
there, we can begin to build our model of the pond.
We would build a model including the components or parts that we see in
the pond, which likely relate to our mechanisms: (C). We will next build
explanations based on our components (which means we would look at specific
evidence from our pond) and we will discuss our evidence in terms of whether our
ideas make sense (i.e., are they plausible, likely to have occurred? –we
will use our ideas about the generic mechanisms and our evidence to support or
refute ideas).
Next we rule out explanations based on that specific evidence gathered. Once we feel that our
model provides a causal explanation for why the fish died, we can use evidence
gathered through simulation and raw data to refine our model based on
plausibility and parsimony to support/refute our ideas. We call this PMC-2E.