I'm reverting back to random posts

The weekly post schedule was nice for encouraging me to write, but I feel like my pool of academic topics are a bit dry at the moment. Therefore, I'm going to revert back to posting at random as thoughts develop, even if that means that the blog may fail to draw in new readers. I may go back to regular posts in the future, but as of now proposal writing, research, and the demands of personal life (i.e. too much time spent at the climbing gym) are taking their toll on my creative faculties.

Meanwhile, here are some recent thoughts. I've had browser tabs to these two sites (OpenScience and Science 2.0) open for some time now but still haven't explored what they're about. I intend to do this soon. I like the idea of free and open source science. Opponents and critics to the movement exist (sorry, I don't have a reference handy), but in my mind the spirit of science is best captured here. Perhaps I'm just cranky and fed up with the politics of academia.

Also, I visited UCF's high performance computing center, a.k.a. Stokes HPCC, yesterday. I don't quite follow the jargon of IT people, but from what I could understand this is a big-ass computer cluster. Pleasingly, it's used by many different people at UCF and isn't just a PR ploy that serves little academic utility. Nice job, UCF.

Bottom-up or top-down understanding?

The past few days I've been modeling the dynamic light scattering (DLS) signal from particles embedded in a complex fluid. The following question keeps floating around in my mind, and I don't quite have a good answer to it.

Is it better to first find an empirical model to explain an observed phenomenon and then infer the physical, microscopic reasons for why the model works? Or is it better to first understand the microscopic makeup of a system to arrive at an empirical model that explains the data?

I'm inclined to think that establishing an empirical model first is better. We often have little-to-no idea of the microscopic details of a system. However, empirical models often apply to systems with similar microscopic behaviors, so once the correct model is found, we can establish a stronger intuition for the details.

Teachers vs. students: when interests don't coincide

I recently had a discussion with a close friend who is not a physicist by training but is very interested in many popular physics ideas. I was attempting to explain several paradoxes and non-intuitive scenarios such as Schroedinger's cat, the twins paradox, and the relativity of simultaneity. However, my friend was not impressed by some of these ideas, like Schroedinger's cat, and proclaimed them as "stupid."

I was of course exasperated by her failure to appreciate these concepts. Out of frustration I refused to explain any further ideas despite her questioning. As you could imagine, my refusal angered her; she interpreted my actions as pompous and arrogant. Fortunately, we are both open-minded to our own faults and quickly apologized to one another.

This bout made me realize that a teacher can't always convey why a topic is interesting. Interest is, after all, a personal attribute that varies between individuals. This mismatch of interests can cause a great deal of friction between a teacher and student and should be recognized by both sides for education to be successful.

Teachers must concede that sometimes students just aren't interested in a topic. A good teacher will be patient, even with difficult students, when they encounter a lack of interest. Eventually, the student will show interest in something that the teacher can help them learn about. Students should acknowledge when they are uninterested but still respect their teachers' enthusiasm. More importantly, they should not interpret their lack of interest as a failure to understand subtleties of a topic.

What's not so clear to me is with whom the greater responsibility for learning should lie. I'm inclined to place the greater burden on the student.