Due to the density of the paper I believe the best route for
me is to talk about where students fail. In my starlogonova models one of the
biggest problems I have is to get my models to predict or to reflect actual trends.
I often see a disconnect for instance with the predator prey model. How does on
translate for instance the predator prey relationship of sardines of South Africa’s
Cape Horn as they migrate to the gathering of predators i.e. (Black tip sharks, Common Dolphins, and flying
birds) ? How can I get these models to show the Okavango wet season where
Zebras and wildebeest surge through the plain and travel to the river where an
armada of crocodiles feed all season? (Animal Fights Netflix).

The implied fudge factors
for this mathematical equation can vary anywhere from the ocean currents to the
rains no coming at the particular time of a normal season causing a shortage in
wildebeest birth. What type of algorithm can be made for this? In addition how
can we translate this to a geometric or calculus side and promote inter
disciplinary learning and build a better student?

Another Issue I can definitely state is the low
math i.q. I have even though I’ve passed
and excelled in numerous math courses. They don’t teach this in schools enough.
The common application of the math we are learning or how to translate that.
How much of this is time based and how much of this is student /pto driven? One of my biggest fears for this class is that
I do the work but miss the essence of the class. In my opinion it is as in any language to be able to recreate and
reteach in an easier way then one learned. I just hope and pray that this is
not my last chance to use this system and full explore it
I agree that learning new modeling languages and applying math in totally different ways from what we are used to is a challenge. I like your comment about fudge factors - students make the common mistake of "curve-fitting" or using "fudge factors" to make the model look the way they think it is supposed to look like. Unfortunately, the "curve-fitted" model may look valid on the surface, but the underlying instructions that it follows may not match the phenomenon being modeled. Students need to be taught problem solving strategies explicitly so that they can avoid this common mistake.

ReplyDeleteReally interesting perspective! I too have had the feeling of simply going through the motions with classes and then thinking back and noticing I truly missed out on the class. I think VanLehn does a good job of stressing knowledge before practice and the importance of knowing how you know what you know and why it is important.

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