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Teaching Natural Sciences To Social Science Students?
An anonymous reader writes "As a calculus professor for a small undergraduate institution, I normally lecture students who are majoring in the natural (or 'hard') sciences, such as mathematics, physics, and computer science. In fact, I have done so for almost thirteen years. However, for the first time this fall semester, we have a shortage of professors on our hands. As a result of this, I have been asked to teach a general education statistics class. Such classes are a major requirement for the large psychology student body we have here. I have never lectured social science students in any mathematics-related classes. My question to the Slashdot community is as follows: What are your experiences with teaching natural science classes to social science students? How is the experience the same or different in comparison to natural science students who may be more adept to the nuances of mathematics and other similar fields?"
Some will be apt and mentally up to speed with whatever you through at them.
Some will be unable to comprehend every third word.
Some will be uninterested. Others will be interested, but incapable.
I'm sorry, am I misreading or are you saying statistics is a "soft science"? If you're that confused about things, then just go to the textbook, and teach one chapter a week.
As someone who's been on both of those academic sides (I started in hard, and moved into soft four years later), I never thought it was a lack of comprehension when fellow students have trouble with hard sciences. Instead, it's an appreciation for numerical conclusions.
Hard sciences basically tend to conclude three steps earlier than soft sciences -- because the math ends there. Hard sciences tend to describe a scenario, detail it numerically, hypothesize a numerical result, experiment numerically, solve for x, and x=n is the answer. The issue for soft science students is really that nobody ever cared about x. Hard sciences very quickly forget where x came from, because the entire scenario was translated into numbers. This affords hard sciences a certain level of abstraction, making problems faster to solve, easier to solve, and more widely relevant to re-apply.
Soft sciences tend to be industries where some aspect of the scenario can't be translated into numbers. It's usually a black-box scenario, and psychology is a good example. Such experiments don't attempt to describe certain behavioural anomalies numerically. Instead, 40% - 80% of a scenario is translated into numbers, leaving the remaining 20% - 60% as mysterious elements. Imagine a hard science equasion where six linear constants simply cannot be merged into a single constant -- for no seemingly good reason. As a direct result, after solving for x, the numerical abstraction must then be de-abstracted back into whatever the real-world scenario actually is. This procedure is not only an effort to grasp, but it's also a a major point of interpretation at the end of an experiment -- usually because x isn't the number of grams diluted; instead x is the likelihood that a person might turn left.
The nice part about de-abstracting at the end is that you wind up with a real-world answer, not a mystery number.
So my point is, that for a social science student used to walking in with a scenario, and walking out with a conclusion, you need to teach them how to appreciate the hard-science "datum result" without having a one-question-one-answer conclusion.
You can see this same effect in the business world. Big business corporate C.E.O.'s often make decisions from numbers in, to predict numbers out, without ever knowing where the numbers came from, nor how they'll be used on the way out. But if you've seen anyone go through "board of director" training, you know that the skills wind up applying to any business anywhere because they are all done at the hard-science executive level.
Constrast that to the entrepreneur of a small business, who needs to make all of the same decisions, but simply doesn't have the sample-size of data coming in to ever be able to make decisions numerically like the corporate guy -- which is one of the primary reasons that he has an advisory board instead of a board of directors. The decision-making process is very different, even though they are the same questions and the same answers. And each has a very difficult time in the other's business world.
Here's hoping someone else's response details a good way to actually teach that appreciation.
Avoid using overly abstract concepts, and try to put things in terms they can understand.
Arts major here, who's been working for about 20 years in IT. I'd offer a qualified agreement here. I found some science subjects innately easy, because I was able to visualise the forces at work. Vector equations in physics, geometry, etc. were dead easy, even when they became more advanced. But the moment the teacher began to fall back on jargon and symbolic shorthand, I'd get lost.
The reason is pretty straightforward. I am extremely good at certain kinds of pattern-identification, but quite poor at others. Among the ones I'm poor at are mathematical equations, which are not evaluated in the same way natural languages are. It's merely a left brain/right brain thing, and I can compensate by using different approaches. I thrived under teachers who understood this, and died under teachers who spent their entire time writing equations on the board without attempting to contextualise them.
Crumb's Corollary: Never bring a knife to a bun fight.
You make it sound like you are teaching physics to special ed classes.
They are as smart as everyone you've had so far. You may see some differences in their backgrounds, but that's easy enough if you make allowances to give more basics or point them to appropriate resources. I'd give an example, but I have no idea what "natural science" is to you. Geology and oceanography are natural sciences, same as physics, but they share little in common.
One thing you may notice is that arts students in hard classes may want more "why" than "how" answers. So be prepared for more philosophical discussions, or correct, if silly, comments (i.e., the "why" for valence electrons is that the stable ones are like a comforable couch, and the unstable ones are hard benches. You want the better seat, but you don't really want to get up, and the worse the chair, the sooner you'd move) or something like that. The "why" as an expression of potential energy in MeV won't get the point across as well as a discussion of musical couches, and they'll remember it better, isn't that the goal, over the goal of the hard science students where accuracy is above all.
Learn to love Alaska
I think some of it is getting the big picture / motivation as well. A lot of students don't have the background many Slashdotters have in documentaries, natural-science museums, even sci-fi, which can lay the big-picture groundwork, with which you can then dive right into equations and methods in the courses. When it comes to physics, for example, a large number of students probably first need to be brought up to "read some Carl Sagan" levels of understanding, which would put them in a lot better position to learn more quantitative aspects.
10 PRINT CHR$(205.5+RND(1)); : GOTO 10
Always interesting to see the categories different parts of academia place each other in. The post's author is calling math, physics and comp-sci "natural sciences" and apparently considers statistics to be "social science". I'm a geology professor and, as far as I'm aware, my colleagues and I tend to consider Earth, environmental, and biological sciences to be the "natural sciences"; physics, chemistry, engineering, and any math to be "physical science"; and psychology, sociology, (cultural) anthropology, etc. to be "social sciences". Everything else is art and/or humanities.
I wonder how other groups categorize one another? Right off the bat I'd suspect that mathematicians don't always consider themselves scientists. Perhaps ditto for engineers. People tend to form and place each other in groups of varying degrees of subjectivity. How you place others probably says something about the standards and values of one's own group.
This sounds like it'd make a great piece of social-psych research! They love this kind of fluff, right? (j/k)
I will try to inform you a little about economics (speaking as the holder of both a BSc and PhD in Economics):
The key difference is that economics and social sciences are mostly non-experimental (people don't take kindly to you arbitrarily changing their parents, education, or wealth - which is the 'experimental' way of establishing cause and effect). This means that the statistical issues are orders of magnitude larger than those that exist in experimental sciences. In an experimental science you can go off and get new data where you have controlled for most everything except the effect you are interested in and a simple regression will generally be all you need. In a non-experimental science you are stuck with the data that nature has given you. As a result you need to be very careful to get meaningful results. But, in case you are doubting, you can get meaningful results if you are careful enough.
Thus, my second point: Economics is not soft headed. In fact, it is very hard headed because you need to be when you are dealing with data that are generally speaking - crap. There are so many ways you can be mislead by non-experimental data and you need to be very hard-headed to avoid this. I won't claim mistakes haven't been made, but those mistakes are the reason economics has gotten much better at dealing with this than many people might realise. But, there is only so much you can do when the data are the way they are.
So don't assume the difficulty of getting solid results in economics reflects the ability of the practitioners rather than the raw materials you are dealing with.