Am I a scientist?
That’s a somewhat existential question that I, and others in positions like mine, often find myself asking. I’m sure my friends, and the K-12 teachers I work with, generally think of me as a scientist without any hesitation. To them, it’s seemingly obvious. After all, I work at a research university and in the patently STEM field of oceanography no less.
Of course, not everyone who works in our department is a scientist. We have accountants, business specialists, and other technicians who support our oceanographic research. And I would hazard to guess that many of their friends think of them as scientists too. After all, they are involved in the research process, albeit tangentially.
By my story is more complicated. In my role, I often contribute to research projects by processing data, developing data visualizations, and occasionally helping to write papers. But I also work on the outreach side, developing web sites and training courses to bring the *joy* of oceanography and working with data to students and teachers.
I’m not a faculty member, nor do I constantly work on grants, plan research projects, write peer-reviewed papers, or advise students. Those are all activities I’ve come to think of as what “true” scientists do. But that’s largely because that’s the academic environment (and perhaps bias) I work in.
Thus, to my mind I’m not a “practicing” scientist. But am I misguided in this thinking?
What does it really take to be a scientist?
One of my key roles (and joys) is working with undergraduate and pre-college educators who are dedicated to training the next generation of scientists. Of course, to do that, we really need to know what knowledge, skills and abilities students need to become successful scientists in the future.
To that end, I love the list of Science and Engineering Practices that were used to develop the Next Generation Science Standards. The practices originally proposed by the NRC in A Science Framework for K-12 Science Education. Here’s the key part:
The eight practices of science and engineering that the Framework identifies as essential for all students to learn and describes in detail are listed below:
- Asking questions (for science) and defining problems (for engineering)
- Developing and using models
- Planning and carrying out investigations
- Analyzing and interpreting data
- Using mathematics and computational thinking
- Constructing explanations (for science) and designing solutions (for engineering)
- Engaging in argument from evidence
- Obtaining, evaluating, and communicating information
In the past, students (especially at the K-12 level) were often taught that scientists followed the Scientific Method, as if they worked in this proscribed hypothesis-prediction-experiment-analysis stricture all their lives. After all, most scientific papers are written in this idealistic format – after the fact – obscuring the vagaries and realities of actual research in practice. So many of the lab experiments I mimicked in high-school and college also followed this linear approach, and to be honest, it never made sense to me. I’ve since found that scientific research, in the moment, is inherently non-linear.
But the practices above do make sense to me. They reflect what I actually see scientists doing on a day to day basis. Each part is essential to driving research forward, and more importantly for educators, each part can also be fun to experiment with when it comes to educating students and exciting them in the research process.
In the Data Labs project, our primary goal is to help faculty utilize data form ocean observatories like the OOI in their classrooms. And from the practices above, it should be obvious how these datasets can provide a great opportunity for students gain first-hand experience in science.
OOI data on its own can’t help students practice every practice, after all, that’s what teachers and mentors are for. But working with real-world datasets, even just a simple graph interpretation activity, can engage students in several of the practices, most notably #4, 5 and 6. Tied together with other active learning teaching strategies, you can easily involve students in argumentation (#7), communicating results (#8) and developing questions (#1) as part of a larger sequence of activities involving OOI data.
So am I a scientist?
Given this list of practices, and my day-to-day activities, I would now give this question a qualified “yes.” (I’m hedging here, because we are talking about scientists after all 😉) While I may not write scientific papers, I certainly spend a lot of time communicating with others about the results I find in my data analyses. And while I may not plan and carry out large research projects, I do develop and investigate questions with the datasets I work with. So while I may not be a big-S “Scientist,” I certainly am a scientist in practice.
The practices provide a great framework for us to think about the skills and experiences we need to provide students in order to help them develop as scientists. Or if they’re not going to become scientists themselves, at the very least, they should better appreciate what scientists do, and ideally carry these skills to their future careers.
It is my belief that ocean observatory datasets provide a wonderful opportunity to engage students in real-world science, allowing them to develop questions and analyze data to figure out some of the small mysteries of the natural world we live in. Hopefully, with these experiences they will build confidence and feel, even if only in a small way, like scientists themselves. And that, ultimately, is what I hope the students we work with get out of this project.