There are some differences, however. Modeling is explicitly a way to train students in scientific thinking and approach to problems. The “models” they are coming up with are quite a bit broad — eg., a mathematical and physical formulation of an understanding of an Atwood Machine — supported by data and applied to different situations. Schwartz’s Invention Activities are a bit more targeted to key concepts, such as “density” or or “variance” or “vectors.” It’s a way to get students to think about a situation and (like modeling) recognize the key elements that go into creating a definition, before the expert comes and gives the common way to approach that term. His activities tend to be very visual.

So, in a way, invention activities are helping students wrestle with the key terms that would go into creating a model in Modeling Physics. They’re smaller, more fundamental chunks than are dealt with in Modeling, and less tied to phenomena.

Here’s a nice summary from the website that Frank included:
http://modeling.asu.edu/modeling/mod_cycle.html

http://modeling.asu.edu/

I plan on looking into Dan Schwarz’s research more closely. I think it could have a profound impact on my teaching.

I’m so glad you liked the post! And actually your responses are a perfect example of the usefulness of the Preparation for Future Learning activities. Your arguments are very similar to what college students argue when given this task, without having first struggled with issues of variance in another task. They say that IQ tests aren’t valid, that there are other things important than hiring in IQ, etc.

However, if you look at the SPREAD in the scores for the blue versus the green people, you’ll see that while, on average, the blue people have a higher IQ, the green people actually have more people with an IQ above 104. In fact if you took the average of the top 20% of the green people, it would likely be higher than the blue people. Another thing to look at is the variability around the median. If you find the middle person (that’s #22), you’ll see that in the blue people, all the people around #22 have IQ’s of 101. The green people around #22 have a much higher spread of IQ.

Most people who haven’t been prepared to think about variability give more meta-arguments about whether or not it’s useful to compare people on IQ, as you did, and don’t look at the data. I’m not saying your arguments aren’t good ones, but there are other arguments based on the data that can be made. Your comment about sample size is a good one based on the data, but there are other data-based arguments to be made.

Thanks for your post!

Stephanie

0. your logic is deeply flawed, Sir, because:
1. the difference (98 to 101) is too small for any conclusion especailly since
2. the sample population is too small and
3. sorting people by color is unwarranted and
4. not correlated with the (small) data and
5. offensive and furthermore
6. IQ and “smarter” are not synonyms and
7. you clearly need to revise what you expect from your employees or the smart ones will stay away from you.

Great post. I couldn’t resist solving the task. Did I get it right?