It’s the first week of semester 1 of our new Biomedical Sciences degree programme, and the first cohort of students have been through their first few lectures and tutorials. They are wonderful young people, bright, optimistic, maybe a bit anxious, but nonetheless ready to throw themselves into this new adventure. These days, I think a lot of what it was like for me when I was in my first year of undergraduate. And about what I was like.
When I was a postdoc, my university had a biology reading course for undergraduates. It was structured as a small seminar, and any postdoc or faculty member could choose a topic and lead a discussion group, provided enough students showed up. Excited, I wrote up a proposal on one of my favourite topics and handed it in. Time passed. Zero undergraduates signed up. Zero. I talked to the professor overseeing the course about possible reasons. “Maybe,” she said, “you should have chosen a sexier title.” I was astonished. How is “Computational Models in Biology and Biochemistry” not sexy enough?
Have you heard of the Dunning-Kruger effect? It’s an interesting psychological insight about how people’s actual knowledge and skill aligns with their perceived knowledge and skill.
People who are not very skilled at a particular task find it very hard to understand the level of skill needed to do that task well. As a consequence, they fail to recognise a truly skilled practitioner when they see one, and they tend to overestimate their own skill. As a people get better at a particular skill, they also get better at accurately estimating how good they are at it. This is true for a wide range of skills and knowledge areas, including grammar, logical thinking, and even humour. It was first described in a 1999 paper by Kruger and Dunning entitled Unskilled and Unaware of it.
Now, here is the problem.
Many biologists grew up liking animals. That’s why they became biologists. Not me. I never had a particular interest in animals, never had pets, never brought in animals from outdoors. My interest in the local wildlife only emerged after I had been working as a (molecular and computational) biologist for some time. Walking to and from the lab, you start to notice things.
I love making resolutions for the New Year. I usually have about 50 of them. They make me feel all virtuous and hopeful and as if I can really change and become a fundamentally better person starting on January 1st. Of course, most of my good resolutions usually fall apart within weeks. Or days. Or hours, it really depends. And then I am stuck being my usual old self (except a year older) for another year, until it is resolution-making time again.
Now, there are several ways around this problem.
I have been in my new job for four months now, and the learning curve has been steep … Wait a minute.
What has the learning curve been?
I never understood the expression “a steep learning curve”. When people say “a steep learning curve” what they mean is that something is difficult to learn and requires a large amount of effort (typically at the beginning). But presumably a learning curve plots the amount of whatever it is you have learnt on the y axis and time or effort or something similar on the x axis.