This NASA video updates us on the search for Earth-like planets around other stars. It overviews what’s been found, and outlines some upcoming missions.
The key to finding a planet hospitable to life (as we know it) is finding one with water at the surface. We’ve found large waterworlds that are too large and hot, with “thick, steamy atmosphere[s]”.
We’ve also found Earth-sized planets but they’re, mostly, too close to their stars to support liquid water, and it’s hard to tell what their atmospheres are like because they’re so far away. One of NASA’s upcoming missions, one will look at the light reflected off Earth-sized planets to determine the composition of atmospheres: the technique is called transit spectroscopy, and is based on detecting the emission spectra of the gasses in the atmosphere.
An excellent series by the American Chemical Society starts with the basics of, “How to Write a Paper to Communicate Your Research,” but also addresses the question of why publish your research. It ought to help my students understand why I’m so insistent on lab reports.
Cedar Riener and Daniel Willingham expand on the argument (previously discussed here and here) that learning styles do not exist. They do not, however, deny that different people learn differently and this needs to be taken into account in teaching.
Real differences that affect learning:
Different talents: “[W]hether we call it talent, ability, or intelligence, people vary in their capacity to learn different areas of content.” (ᔥRiener and Willingham, 2012). Some of this is probably genetic, while some of it is probably due of nuture., which leads to:
Different interests: Students with an interest in a subject are more motivated to learn and will learn it faster.
Different background knowledge: Student retain more then they are able to fit new knowledge into an existing mental scaffolding.
Learning disabilities: There are neurological differences that result in things like dyslexia that have a strong influence on how some students learn.
Riener and Willingham argue that while students do have preferences for ways they learn (visual vs. auditory vs. kinesthetic etc.) these have no real effect students’ learning. Information should be presented in ways that are appropriate to the content:
If I were to tell you “I want to teach you something. Would you rather learn it by seeing a slideshow, reading it as text, hearing it as a podcast, or enacting it in a series of movements,” do you think you could answer without first asking what you were to learn—a dance, a piece of music, or an equation? While it may seem like a silly example, the claim of the learning styles approach is that one could make such a choice and improve one’s learning through that choice, independent of content.
We all agree that some kids show more interest in math, some start their education more interested in poetry, and others are more interested in dodgeball. The proof that the learning-styles theorist must find is that for some sort of content—whether it be math, poetry, or dodgeball—changing the mode of presentation to match the learning styles helps people learn. That evidence has simply not been found.
Finally, they assert that, “it is a waste of time to assess learning styles rather than, for instance, background knowledge.”
Still, even with learning styles taken out of the equation, it seems to me that presenting information in multiple modes remains beneficial. It forces the teacher to approach the subject matter from different perspectives, and presents students with multiple opportunities to encounter information in a way that would fit into their existing knowledge scaffolding. However, it is useful to recognize that we don’t have to force ourselves too fit content into incongruent learning styles (although that in itself might be a useful mental exercise for the teacher, or a good way for students to demonstrate that they can apply their knowledge into other domains).
Jonah Lehrer’s has an excellent interview on Fresh Air about his new book on how creativity works, called Imagine.
There are three key components:
Relaxed state of mind: Like when you’re in the shower and your mind is free to wander. It’s another reason not to be afraid of a little boredom, and setting aside personal time time in the day.
Hard work: But the relaxed mind needs to have something to work with, and that’s all the hard work that came before. When you’re relaxed the mind processes things in different ways, it mulls over the things you’ve been thinking of, and makes unexpected connections.
Uninhibited, childlike perspective: You need to allow your brain the opportunity to be creative. All the hard work requires good focus and persistence; things the pre-frontal cortex develops the ability to do (and something we train it to do) during adolescence. But the ultimate, creative insight often requires you to turn off that part of the brain so you can thing uninhibited, creative thoughts.
One of my physics students is working on a project to demonstrate interference in sound waves, so I generated a few sound files with different wavelengths for her to experiment with.
A sound wave with a frequency of 347 cycles per second (347 Hz), which has a wavelength of approximately 1 meter. Waveform captured using the WaveWindow program.
Using SoX, you can generate waves by inputing the frequency you want (using the synth command). The frequency () depends on the wavelength () and speed () of the sound waves through air.
The speed of sound through the air depends on the temperature (it’s a linear relationship). Hyperphysics has a nice Speed of Sound in Air calculator, which tells me that at room temperature (about 25 ºC):
The Correlated website asks people different, apparently unrelated questions every day and mines the data for unexpected patterns.
In general, 72 percent of people are fans of the serial comma. But among those who prefer Tau as the circle constant over Pi, 90 percent are fans of the serial comma.
Two sets of data are said to be correlated when there is a relationship between them: the height of a fall is correlated to the number of bones broken; the temperature of the water is correlated to the amount of time the beaker sits on the hot plate (see here).
A positive correlation between the time (x-axis) and the temperature (y-axis).
In fact, if we can come up with a line that matches the trend, we can figure out how good the trend is.
The first thing to try is usually a straight line, using a linear regression, which is pretty easy to do with Excel. I put the data from the graph above into Excel (melting-snow-experiment.xls) and plotted a linear regression for only the highlighted data points that seem to follow a nice, linear trend.
Correlation between temperature (y) and time (x) for the highlighted (red) data points.
You’ll notice on the top right corner of the graph two things: the equation of the line and the R2, regression coefficient, that tells how good the correlation is.
The equation of the line is:
y = 4.4945 x – 23.65
which can be used to predict the temperature where the data-points are missing (y is the temperature and x is the time).
You’ll observe that the slope of the line is about 4.5 ºC/min. I had my students draw trendlines by hand, and they came up with slopes between 4.35 and 5, depending on the data points they used.
The regression coefficient tells how well your data line up. The better they line up the better the correlation. A perfect match, with all points on the line, will have a regression coefficient value of 1.0. Our regression coefficient is 0.9939, which is pretty good.
If we introduce a little random error to all the data points, we’d reduce the regression coefficient like this (where R2 is now 0.831):
Adding in some random error causes the data to scatter more, making for a worse correlation. The black dots are the original data, while the red dots include some random error.
The correlation trend lines don’t just have to go up. Some things are negatively correlated — when one goes up the other goes down — such as the relationship between the number of hours spent watching TV and students’ grades.
The negative correlation between grades and TV watching. Image: ᔥLanthier (2002).
Correlation versus Causation
However, just because two things are correlated does not mean that one causes the other.
A jar of water on a hot-plate will see its temperature rise with time because heat is transferred (via conduction) from the hot-plate to the water.
On the other hand, while it might seem reasonable that more TV might take time away from studying, resulting in poorer grades, it might be that students who score poorly are demoralized and so spend more time watching TV; what causes what is unclear — these two things might not be related at all.
Which brings us back to the Correlated.org website. They’re collecting a lot of seemingly random data and just trying to see what things match up.
Curiously, many scientists do this all the time — typically using a technique called multiple regression. Understandably, others are more than a little skeptical. The key problem is that people too easily leap from seeing a correlation to assuming that one thing causes the other.
) depends on the wavelength (
) and speed (
) of the sound waves through air.
