I sometimes get a self-depreciating response when I tell people what I do: “oh I could never do that,” “I wouldn’t even know where to begin,” or my least favorite; “I’m not smart enough to do that myself.” Sure, I’m intelligent, but more importantly I am interested in my work and I put a lot of time and effort into practicing it. I didn’t become a microbiologist overnight. And more than that, in my career path I discovered a lot of people and opportunities that helped me get here. I firmly believe that most people could do my job, given the right amount of education, determination, and support (and a heavy dose of enthusiasm for spread sheets). As I move up the ladder, I’m increasingly in a position to educate, help others network, and bring students closer to their career goals. One day I’ll be able to take on graduate and undergraduate researchers of my own, and I find myself asking, how will I find and recruit those students that just need an opportunity to become amazing scientists? The ones that weren’t told by their teachers that they should be microbiologists but still have an aptitude for it, the ones that think they aren’t “smart enough” when really they just aren’t confident enough?
Lessons from the rare biosphere
One of the emergent theories in microbial ecology over the last few decades is that of the “rare biosphere.” It’s the idea that microbial ecosystems are much more intricate than we realized, and there are a great many microorganisms present in any given environment that have very low populations. We just couldn’t see them under a microscope or grow them in culture because their presence was washed out by more abundant microorganisms. It wasn’t until the emergence of DNA-based technologies that we could really understand the depth of that diversity because this technology was able to sequence all or nearly all the DNA in the entire sample.
When culturing bacteria in the lab, one must try to mimic the original environment as closely as possible in order to get that microbe to grow. It is incredibly difficult to please “everyone” on just one or even dozens of different culture media types, so you end up getting a biased idea of “who” lives in a natural environment based on what species are able to survive in the mock environment you’ve created. DNA-based technologies don’t require live microorganisms; you can extract DNA or RNA strands directly from your environment and sequence them, although you will need a reference database of previously cultured and sequenced microorganisms to make the identification. Sequencing has its own problems, of course, namely being able to discern between a rare microorganism whose DNA represents a very small percentage of your data, and a random sequencing error inherent to your technology that turns a known sequence into a fake novel one. One way bioinformaticians tackle this is by removing rare sequences altogether, but as Sogin et al. argue, you might be getting rid of significant contributors to your ecosystem.
This is just one example of a major theme in science: how do we detect something if we don’t know it’s there? How to do we differentiate what is real (but rare) from the technological errors and background noise? We constantly improve our technology and revise our understanding of the physical world as we get better at investigating it. But as we rely more and more on technology that we have created (which may operate on the biases we have designed into it), and we want to collect more information with less human effort, we need to remember that it’s our intuition and reasoning skills that make humans so good at data analysis and investigation in the first place. This led me to wonder if we weren’t making the same mistakes in education.
One of the most common errors we commit is to mistake education for intelligence. Intelligence is partially a natural ability for learning and understanding, and partially cultivated by an atmosphere of curiosity and interest in learning. Education, on the other hand, has to be earned. While public schools and other learning resources in the United States exist to give all children an equal chance at education, in practice there are significant biases in quality and quantity in education.
The disparity between education and ability
Student to teacher ratio is correlated with student performance, and can vary widely by type of school (public, private, elementary or secondary), geographic location, urban or rural demographics, etc. Because of that, the national trend for student to teacher ratios in public schools appears to have only slightly increased (more students per teacher) from where it was in 2002, with that increase only since 2008. However, much of the increase in student to teacher ratios is localized, specifically in low-income districts, so there is a disproportionate affect by economic status. Many teachers in low-income school districts cite budget cuts that result in overwhelmingly large class sizes to be the main reason they quit education (discussed here). And a poor school budget does more than just crowd students, it depletes the school of educational resources which reduces the quality of the education and student performance.
Therefore, just because someone appears uneducated does not mean they are not intelligent. For example, Linus Pauling, who was competing with Britain’s Watson and Crick to discover the structure of DNA, didn’t obtain his high school diploma until after he won two Nobel Prizes simply because he didn’t finish some required high school history courses. A recent study looked at grade point average (GPA), SATs (previously the Scholastic Aptitude Test), graduate record examinations (GREs- the standardized tests that most schools use as a graduate entrance qualifier), and whether test scores predicted how well someone performed as a graduate student. Like undergraduate study, most graduate programs require a minimum GPA and GRE score even to be considered. However, the study found that students with higher test scores didn’t actually perform better as graduate students. In fact, here’s a whole website about geniuses that failed IQ or other aptitude tests that went on to change the world. Here’s another about artists, politicians, and business tycoons who failed repeatedly before becoming household names.
Another problem is our biased view of the quality of an education based on the country of origin. Indian mathematician and genius Srinivasa Ramanujan was born in a small village in the late 1880s. He started performing advanced geometry and arithmetic at just 13 years old, and began focusing on mathematics in secondary school and at a local college. At 26, he wrote to British mathematicians looking to discuss his ideas, and was dismissed out of hand by almost all of them. G.H. Hardy, however, wrote back, and began a collaboration of ideas that led to an incredible body of work between the two of them.
The Rare Knowledgesphere- The one that almost got away
This idea of overlooking greatness is important to keep in mind when ranking people by their resume or test scores instead of by an interview. After all, just because you attended Yale doesn’t mean you went to all your classes. This concerns me, because we may be passing over potential undergraduate or graduate students who appear less educated on paper, but aren’t less intelligent or less apt.
So, how do we as educators and mentors get beyond this bias and find the students and researchers-to-be that slip through the cracks? The ones that are out there that aren’t even on our radar. I’ll let you know once I’ve figured it out. But from my experience, it comes down to taking the time to interview and really get to know someone before accepting them as a graduate student, not just selecting the best looking resume. It especially means letting go of your ideas about the quality of someone’s education based on the type or location of their school, as well as stereotypes about their abilities.
And it means being creative about marketing your positions, to make sure you are reaching the individuals that aren’t actively looking for you. This may sound counter-intuitive; why try to recruit someone to graduate study if they aren’t interested? Again, I can speak from experience. My undergraduate degree is in Animal Science, and my interests in graduate study at the time centered vaguely around wildlife conservation. Instead, I entered a graduate program where my primary research and laboratory work were focused on microbiology, genetics, microbial ecology, and bioinformatics. I had no formal academic or practical training in these areas. But I joined, and I excelled, all because my mentor-to-be told me that I was capable. And here I am today, in love with my science.
With all this in mind, stay tuned for my post in the next few weeks on what makes a person a good graduate student, if it isn’t test scores.