As you can probably tell, this post is about teaching. I'm currently teaching a 300-level lab class (Developmental Biology Lab), which is full of seniors about to graduate. It's my first time actually teaching, as opposed to leading study groups or doing one-on-one tutoring sessions.
I led study groups for intro physics for a little over two years, and I still remember the first study group I ever had. First off, it was a class full of engineers, and there were still parts of the class I didn't fully understand (ahem, like the first few chapters?!). I was shaky on topics at the beginning of the course and had a better understanding of what came after, so things smoothed out as the semester wore on. As a result, I ended up learning quite a bit about the subject as well, and study group dynamics worked themselves out after a semester or two.
The biggest problem with actual teaching is the understanding (and acceptance) of the fact that not everyone who is in the class wants to be there. I never had a problem with it in the past, since study groups and tutoring sessions were made of people who wanted my help, not because they needed to get a tutor in order to fulfill some sort of requirement for their major. My own rationalization for this was that I was teaching an upper-level class, and that these were seniors who had already completed all the reqs for their degree.
The other difference is the authority problem. The class was off to a bumpy start, because I think no matter how prepared I thought I was, I still got a bit nervous standing there and talking to 22 people who I've never seen before. 22 kids. That's more than twice the size of my average study group.
I'm also physically the smallest in the class, which has the potential to compound the authority problem. But I think I have that problem under control with pop quizzes, lots of clarification, and randomly picking on people to answer questions.
I think I've also gotten some sort of reputation for being a tough grader as well as a walking advertisement for my advisor's lab. Just wait until they see the metamorphosis experiment in this course. :-)
Finally, teaching a class makes me realize that I most likely took my undergrad profs for granted. Students often complain about a badly worded test question, or just an overall impossible exam. But just as exams are hard to take, they're also pretty hard to write. After a particularly disruptive class one week, I went home and started putting together a pop quiz. Brainstorming a topic and a question for the quiz took me a long time. I wanted to cover "big picture" concepts and apply it to an organism that they never learned about, but at the same time, I didn't want them to be completely stumped (ok, I did; they made me angry!) and miss the concept at hand. The bottom line was that I did NOT want to write some multiple choice question that they're just gonna forget after they hand it in...but it was quite tempting to do so and get the writing over with.
All in all, teaching is enjoyable. The department requires two terms of teaching, but I wonder if my advisor will let me do more...
That being said, being a GSI isn't difficult.. provided that I continue to come prepared to class and refuse to take any shit from students twice my size.
Saturday, January 30, 2010
Sunday, December 13, 2009
reading, writing, and everything in between
Be skeptical.
That's what my advisor told me when I brought in a few papers a while back. And also what he said when it got to explaining my rationale behind my experiment predictions.
Part of my work involves an enzyme called AMPK, so I've been doing a bit of reading on it. But since it's such an ubiquitous protein, there's been a lot of work done on it already, even if I look into specifically what its role is in food intake control (another part of my project). So to organize my thoughts and see what parts I was still missing out on reading about, my advisor asked me to write a minireview (~5 pages) about AMPK and its role in feeding control in the brain, so that's what I've been doing in my "spare time" for the last two weeks.
Writing it was a little harder than anticipated. The first day of writing, it took me about three hours to write a paragraph. Not because I didn't know how to word things; it was because I was averaging one citation a sentence. Which means for every sentence I write, I have to read an entire paper written by someone else. I suppose I could read a whole bunch of abstracts and string them together to make an instant review, but that's out of the question. First off, my advisor is going to know whether or not I really read the papers; and second, sometimes the abstracts will sugarcoat things, and unless the reader looks into the methods, it'll seem like everything is legitimate.
------
I'm also in the process of scribbling ideas to write for my written prelim exam. In general, my project has to be on leptin in the frog, but since it wasn't cloned until 2006, there's still a lot of basic info we don't know yet, like if it signals through particular pathways, or if it is produced in a certain way. These basic questions have already been answered in mammals, and it seems to me like a lot of the mammalian studies nowdays are focusing more on different brain regions that leptin acts on, how they "talk" to each other, where other signals might come from, etc. It would be pretty boring (not to mention very uncreative) of me to base my written exam off of the statement "This stuff about leptin is true in mammals. Please give me money so I can do the exact same thing in frogs."
So now I'm kinda stuck on how to use the frog as a model to advance the field. Which probably translates to "I should read a lot more..."
That's what my advisor told me when I brought in a few papers a while back. And also what he said when it got to explaining my rationale behind my experiment predictions.
Part of my work involves an enzyme called AMPK, so I've been doing a bit of reading on it. But since it's such an ubiquitous protein, there's been a lot of work done on it already, even if I look into specifically what its role is in food intake control (another part of my project). So to organize my thoughts and see what parts I was still missing out on reading about, my advisor asked me to write a minireview (~5 pages) about AMPK and its role in feeding control in the brain, so that's what I've been doing in my "spare time" for the last two weeks.
Writing it was a little harder than anticipated. The first day of writing, it took me about three hours to write a paragraph. Not because I didn't know how to word things; it was because I was averaging one citation a sentence. Which means for every sentence I write, I have to read an entire paper written by someone else. I suppose I could read a whole bunch of abstracts and string them together to make an instant review, but that's out of the question. First off, my advisor is going to know whether or not I really read the papers; and second, sometimes the abstracts will sugarcoat things, and unless the reader looks into the methods, it'll seem like everything is legitimate.
------
I'm also in the process of scribbling ideas to write for my written prelim exam. In general, my project has to be on leptin in the frog, but since it wasn't cloned until 2006, there's still a lot of basic info we don't know yet, like if it signals through particular pathways, or if it is produced in a certain way. These basic questions have already been answered in mammals, and it seems to me like a lot of the mammalian studies nowdays are focusing more on different brain regions that leptin acts on, how they "talk" to each other, where other signals might come from, etc. It would be pretty boring (not to mention very uncreative) of me to base my written exam off of the statement "This stuff about leptin is true in mammals. Please give me money so I can do the exact same thing in frogs."
So now I'm kinda stuck on how to use the frog as a model to advance the field. Which probably translates to "I should read a lot more..."
Friday, October 16, 2009
the hardest part
We had a lab meeting earlier this week, and I was the presenter, so I spent a good amount of time putting slides together last week. If the old rule of "a minute a slide" applies, my presentation should have been a little less than 20 minutes long.
It was an hour and a half.
I got interrupted at practically every slide.
And I didn't know the answers to a lot of the questions.
So to fill in some of the gaps, I spent a good chunk of time this past week adding, deleting, and rearranging some stuff so it addressed the comments made in the meeting, and I ran it by my advisor today. The verdict: read some more.
The slides I got the most interruptions at were the slides that described questions I wanted to address via experiments. I kept getting called on for clarifying what the question really was, what my hypothesis was, and the reason why I formed that hypothesis. It turns out that the vast majority of my background information came from studies done in mice, and while it's generally assumed that the results would be the same for frogs, that's obviously not a good enough reason to base a series of experiments off of. There were a lot of preliminary questions that needed to be addressed first, using the FROG as a model before moving on, and I didn't realize that, since I had automatically assumed that an important molecule would be evolutionarily conserved between frogs and mammals.
That being said, there's a surprising amount of information we DON'T know (or I don't know) about frogs. This information had been well-documented in mice, but I had taken this info from mice, extended it to frogs, and proceeded to the next step, without bothering to test to see if the information also held true for frogs.
--------------
Last term in that grant-writing/prelim-giving/paper-reading class, we had a professor come in and talk to us about what makes a good grant. Three basic parts:
1. whats the question?
2. how do you test it?
3. so what? (why should we give you money?)
He said that the reason why a lot of grants are tossed out is because they don't have a good question in mind, and that the "what's the question?" part of the grants is the most difficult to answer. Originally I had disagreed; I thought "so what" was the hardest, since not everyone is going to be utterly fascinated in learning about social behavior in purple carnivorous snails (just an example). However, after the past two weeks of working on these slides, looking over at the comments made, and going back to revise things, "so what" is actually a pretty easy question to answer... once I figure out the question I want to test in my experiments.
So I'll be writing up a list of things that need to be answered, and try to weed out which question is the most fundamental...which will be the starting point for subsequent months.
It was an hour and a half.
I got interrupted at practically every slide.
And I didn't know the answers to a lot of the questions.
So to fill in some of the gaps, I spent a good chunk of time this past week adding, deleting, and rearranging some stuff so it addressed the comments made in the meeting, and I ran it by my advisor today. The verdict: read some more.
The slides I got the most interruptions at were the slides that described questions I wanted to address via experiments. I kept getting called on for clarifying what the question really was, what my hypothesis was, and the reason why I formed that hypothesis. It turns out that the vast majority of my background information came from studies done in mice, and while it's generally assumed that the results would be the same for frogs, that's obviously not a good enough reason to base a series of experiments off of. There were a lot of preliminary questions that needed to be addressed first, using the FROG as a model before moving on, and I didn't realize that, since I had automatically assumed that an important molecule would be evolutionarily conserved between frogs and mammals.
That being said, there's a surprising amount of information we DON'T know (or I don't know) about frogs. This information had been well-documented in mice, but I had taken this info from mice, extended it to frogs, and proceeded to the next step, without bothering to test to see if the information also held true for frogs.
--------------
Last term in that grant-writing/prelim-giving/paper-reading class, we had a professor come in and talk to us about what makes a good grant. Three basic parts:
1. whats the question?
2. how do you test it?
3. so what? (why should we give you money?)
He said that the reason why a lot of grants are tossed out is because they don't have a good question in mind, and that the "what's the question?" part of the grants is the most difficult to answer. Originally I had disagreed; I thought "so what" was the hardest, since not everyone is going to be utterly fascinated in learning about social behavior in purple carnivorous snails (just an example). However, after the past two weeks of working on these slides, looking over at the comments made, and going back to revise things, "so what" is actually a pretty easy question to answer... once I figure out the question I want to test in my experiments.
So I'll be writing up a list of things that need to be answered, and try to weed out which question is the most fundamental...which will be the starting point for subsequent months.
Monday, September 7, 2009
faith in science
I was laboring on Labor Day; apparently my cloning experiment didn't work again. The concept is simple enough: cut plasmid/inserted gene, purify, and then glue together. So why has it taken me over two months to do this? (aside from losing the DNA, breaking machines, fighting the Incredible Shrinking Insert...)
Molecular biology (or science in general) at its best (read: textbook figures) is a great concept. We have machines we didn't have a decade ago, protocols that have been refined multiple times, and reagents that can be mass-produced so that they aren't nearly as expensive (although $150 for a tiny bottle of Taq is still way too expensive, for my taste), but even with the technology we have now, everything is far from foolproof. PCRs will just fail for no apparent reason, and the only "logical" explanation was that whatever day the experiment was done on was just a "bad lab day." Likewise, the first sentence in our lab protocol for ligations (where two pieces of DNA are glued together) is "sometimes it works, sometimes it doesn't," making it seem like you should mix the necessary ingredients together, follow the guidelines, and *hope* that it works.
Science is like a religion in itself; sometimes things just happen and nobody knows why. I've been restriction digesting and purifying "my favorite gene," which is supposed to be 500 basepairs long. Somehow after purification, the gene "shrinks" to 300 basepairs. I've ruled out every possiblity I can think of, and there's nothing that can really be done except start over (and maybe whine about it for a while). Yes, cloning sucks, but I suppose I just have to *believe* that whatever I'm doing is the right thing, and that one day after tapping my foot 55 times while wearing a red tshirt and singing along to some given song on my iPod, I'll get my ONE much-needed clone.
Seriously though, howcome superstition isn't more blatant in the research field?
Molecular biology (or science in general) at its best (read: textbook figures) is a great concept. We have machines we didn't have a decade ago, protocols that have been refined multiple times, and reagents that can be mass-produced so that they aren't nearly as expensive (although $150 for a tiny bottle of Taq is still way too expensive, for my taste), but even with the technology we have now, everything is far from foolproof. PCRs will just fail for no apparent reason, and the only "logical" explanation was that whatever day the experiment was done on was just a "bad lab day." Likewise, the first sentence in our lab protocol for ligations (where two pieces of DNA are glued together) is "sometimes it works, sometimes it doesn't," making it seem like you should mix the necessary ingredients together, follow the guidelines, and *hope* that it works.
Science is like a religion in itself; sometimes things just happen and nobody knows why. I've been restriction digesting and purifying "my favorite gene," which is supposed to be 500 basepairs long. Somehow after purification, the gene "shrinks" to 300 basepairs. I've ruled out every possiblity I can think of, and there's nothing that can really be done except start over (and maybe whine about it for a while). Yes, cloning sucks, but I suppose I just have to *believe* that whatever I'm doing is the right thing, and that one day after tapping my foot 55 times while wearing a red tshirt and singing along to some given song on my iPod, I'll get my ONE much-needed clone.
Seriously though, howcome superstition isn't more blatant in the research field?
Saturday, August 1, 2009
Information Overload
It's generally a good idea to read up on papers related to your research topic as preparation for prelims, and a rule of thumb is to read about two papers a week. My advisor told me about two weeks ago that I could probably be more ambitious than that and read a paper a day.
He has a good point, since my undergrad career consisted of a wide smattering of classes with no particular emphasis on one subfield in bio. But a paper a day? I'm not sure I can absorb all that info at once; I have to read a paper at least twice to get the most out of it. The first time I read it, I skip the methods section and all the figures. Second time around I'll actually look at the pictures, and (maybe) come up with more questions.
Hopefully I get better at info absorption over the next month; once school starts, my schedule is gonna be insane.
He has a good point, since my undergrad career consisted of a wide smattering of classes with no particular emphasis on one subfield in bio. But a paper a day? I'm not sure I can absorb all that info at once; I have to read a paper at least twice to get the most out of it. The first time I read it, I skip the methods section and all the figures. Second time around I'll actually look at the pictures, and (maybe) come up with more questions.
Hopefully I get better at info absorption over the next month; once school starts, my schedule is gonna be insane.
Tuesday, July 7, 2009
Year 1 in retrospect
I've been meaning to write this post for about two weeks now... first off, I finally decided to stay in this current lab (working with frogs). That doesn't mean I'm not intimidated by my PI; I've gotten a bit used to it, and plus, being a little more intimidated by this PI than the other one would push me to work a little harder and really know what I'm doing.
That being said, I don't think I'll ever get over this "being terrified of my boss" phase. Hopefully by prelim time? If not, it better be by defense time...
***
Several weeks ago, one of the professors down the hall was talking about the learning curve in grad school. He said it's pretty amazing how much of a difference one year of grad school makes on a student's reasoning, presenting, and writing skills. It's hard to say where the learning curve is the steepest, but I would guess somewhere in the first two years. I was archiving my rotation presentations and written summaries the other day and before putting them away, I was reading through them. Frankly, my first written summary in grad school sounds like an 8th grader wrote it. :-/
It's gotten easier for me to see the connections between theory and experiment design (something that took me literally forever to do in my undergrad lab), but I still have a ton of stuff to learn. I thought of a possible thesis project about three weeks ago, and talked to my PI about it. There doesn't seem to be too many people working on that particular topic as far as my PubMed searches tell me, but I suppose I could go off on a tangent and search for somewhat related topics...
That being said, I don't think I'll ever get over this "being terrified of my boss" phase. Hopefully by prelim time? If not, it better be by defense time...
***
Several weeks ago, one of the professors down the hall was talking about the learning curve in grad school. He said it's pretty amazing how much of a difference one year of grad school makes on a student's reasoning, presenting, and writing skills. It's hard to say where the learning curve is the steepest, but I would guess somewhere in the first two years. I was archiving my rotation presentations and written summaries the other day and before putting them away, I was reading through them. Frankly, my first written summary in grad school sounds like an 8th grader wrote it. :-/
It's gotten easier for me to see the connections between theory and experiment design (something that took me literally forever to do in my undergrad lab), but I still have a ton of stuff to learn. I thought of a possible thesis project about three weeks ago, and talked to my PI about it. There doesn't seem to be too many people working on that particular topic as far as my PubMed searches tell me, but I suppose I could go off on a tangent and search for somewhat related topics...
Tuesday, June 16, 2009
doing science properly
Last term I took a grant-writing/prelim-giving/paper-reading class, and one of the sessions was dedicated to writing a good grant. The guest speaker was a professor in the department who has never had to resubmit an NIH grant in his 30+ years of tenure as professor. He brought in his experiences as a grad student at MIT and how there were two professors there who did science very differently, but both ways were ideal, depending on your personal style.
The first way is to go off on crazy tangents from one root project. It's like saying, "mutant A has an obvious eye defect as heterozygotes, but homozygous mutants for A die." You could have someone in your lab working on what this eye defect is and how it relates to the mutated A, and another person working on the timing of death. Why do the homozygotes die? What stage? What went wrong?
The second way is to keep working on one central question, discovering things that relate to it along the way, but never fully answering the question. Going back to mutant A, you could ask "why do homozygotes die?" Then you could find out that they have defects in gene A, which is part of pathway B. But that doesn't mean pathway B is the ONLY one in the system that is making mutant A unhealthy. In other words, is there anything working with pathway B? Does it act alone? Is there a mutant A that doesn't have problems in pathway B, but has problems with receptor C instead?
The unfortunate part about getting grants is that the government expects neatly-laid out experiments, with not much wiggle room for failure (one of the main laments of one of the professors I talked to). But since NIH grants are stretched over a five year period, how is anyone supposed to have the insight to know that some project that looks promising now won't be a dead end? How do you account for pop-ups that are related to your project, but are completely unanticipated?
Science is already a competition for funds, but the restrictions on what you can write in your proposal and what you can't possibly foresee makes it more and more difficult. We can't possibly outsource research, but will the sheer difficulty of getting money drive down the population of scientists?
The first way is to go off on crazy tangents from one root project. It's like saying, "mutant A has an obvious eye defect as heterozygotes, but homozygous mutants for A die." You could have someone in your lab working on what this eye defect is and how it relates to the mutated A, and another person working on the timing of death. Why do the homozygotes die? What stage? What went wrong?
The second way is to keep working on one central question, discovering things that relate to it along the way, but never fully answering the question. Going back to mutant A, you could ask "why do homozygotes die?" Then you could find out that they have defects in gene A, which is part of pathway B. But that doesn't mean pathway B is the ONLY one in the system that is making mutant A unhealthy. In other words, is there anything working with pathway B? Does it act alone? Is there a mutant A that doesn't have problems in pathway B, but has problems with receptor C instead?
The unfortunate part about getting grants is that the government expects neatly-laid out experiments, with not much wiggle room for failure (one of the main laments of one of the professors I talked to). But since NIH grants are stretched over a five year period, how is anyone supposed to have the insight to know that some project that looks promising now won't be a dead end? How do you account for pop-ups that are related to your project, but are completely unanticipated?
Science is already a competition for funds, but the restrictions on what you can write in your proposal and what you can't possibly foresee makes it more and more difficult. We can't possibly outsource research, but will the sheer difficulty of getting money drive down the population of scientists?
Subscribe to:
Posts (Atom)