Kristin Sainani (née Cobb) (@KristinSainani) is a professor at Stanford University. She teaches statistics and writing; works on statistical projects in sports medicine; and writes about health, science and statistics for a range of audiences. She authored the health column Body News for Allure magazine for a decade. She is the statistical editor for the journal Physical Medicine & Rehabilitation; and has authored a statistics column, Statistically Speaking, for this journal since 2009. She is also the associate editor for statistics at Medicine & Science in Sports & Exercise. She teaches the popular Massive Open Online Course (MOOC) Writing in the Sciences on Coursera, and also offers an online medical statistics certificate program through the Stanford Center for Professional Development. She was the recipient of the 2018 Biosciences Award for Excellence in Graduate Teaching at Stanford University.
Episode Description
Rosemary and John both have a passion for teaching. Their experience with classes ranges from mentored studies with a few students, to face-to-face classes with close to 100 students. Recently online classes that might be held synchronously or asynchronously with classes somewhere in between. What if you wanted to offer classes too many more students, who might be based anywhere around the world. Teaching at scale is the focus of this episode of Stats+Stories with guest Kristin Sainani.
+Full Transcript
John Bailer
Rosemary and I both have a passion for teaching. Our experience with classes ranges from mentored studies with a few students to face to face classes with close to 100 students, and recently online classes might be held synchronously or asynchronously with classes somewhere in between. What if you wanted to offer classes to many more students who might be based anywhere around the world. Teaching at scale is a focus of our episode of stats and short stories. I'm John Bailer. Stats and Stories is a production of Miami University's departments of statistics and media, journalism and film, as well as the American Statistical Association. Joining me as regular panelist, Rosemary Pennington. Our guest today is Kristen Sainani. Sainani is a professor at Stanford University. She teaches statistics and writing works on statistical projects and sports medicine, and writes about health science and statistics for a range of audiences. She authored the health column body news for a lower magazine for a decade, and is the statistical editor for the Journal of Physical Medicine and Rehabilitation, and has authored a statistics column statistically speaking for the journal since 2009. She teaches the popular Massive Open Online Course MOOC writing in the sciences on Coursera, which is something we're going to talk about a little bit later. But first, you know, I teach a fair amount, and I really hate grading. And I gotta tell you, I mean, of all the things, it's the worst, worst ever. But you know, we're talking to someone today, who's taught a class to 10s of 1000s of people on scientific writing. And I guess the most important question, Kristen, is, how do you keep up with your grading? I don't? I know, that's not the real question. You know, the real question is, how did you get involved in the idea of teaching this massively open online course on scientific writing? To? What were the goals for it? And you really, you know, kind of what have you learned from this process?
Kristen Sainani
Right? I mean, it's interesting, I mean, Stanford likes to be on the cutting edge of anything technological, right. And so, when I started teaching, writing way back when one thing I used to do was to post all of my lectures, and this is writing for scientists, and I would post all of my, you know, PowerPoint slides in the old days online. And, like, get a lot of people coming across that website and say, Oh, that was really useful to me, because there weren't a lot of people teaching, writing, specifically to scientists. And so it turned out that a lot of people found this useful. And so when Stanford was getting into the massive open online courses and Coursera, and things, I was an early adopter of that, because I thought, well, this material is useful. And wouldn't it be better to actually put out my lecture, rather than just my static PowerPoint slides, and people might find that useful. So I put this up as one of the early courses on Coursera. And again, people found it very useful. Because there isn't we in grad school, you're kind of expected to learn writing through osmosis, I think. And it wasn't something really taught. And when I was a grad student, you know, I didn't learn a lot of this. I learned this when I went and studied journalism after grad school. And I always thought, I wish I had known that I was trying to write papers in grad school. And so it took off from there. And then I got really into these massive open online courses. It's fun getting people emailing you from all corners of the world and saying, Hey, that was really helpful to me, it made writing less intimidating to me, and it was useful.
Rosemary Pennington
How do you approach writing then since you sort of sound like you, you know, you went through grad school, then you studied journalism, and now you're teaching writing? So how do you approach the teaching of that? Because I'm going to steal some of your ideas.
Kristen Sainani
Yeah, so I, you know, I my main message is, you know, try to not write in an academic style, drop all of your bad habits that you've learned in academia, and I'm really teaching a lot of the things that I learned in journalism. And my style of teaching is that I've pre taped all my lectures. And so even for my on campus students, I use the pre taped lectures in a flipped classroom model. Because the two things that I teach statistics and writing are not things you learn in a lecture, you learn those by doing you know, you don't learn to write by hearing a lecture on writing. So I use the class time to do hands-on work. We write, we edit in my writing class, you know, my stats class, we get our hands on data. And so I really liked that flipped classroom model.
John Bailer
I'm curious about your, your kind of journey, this career journey of yours, where you know, you were doing this stat and epi stuff and then you said, you know, I kind of think I want to do journalism too. You know, what, what is it? What led to this idea of, here's some, here's something that I really feel compelled to do, and what changed in terms of what ends writes that you gain from journalism. I mean, you mentioned a couple of them in terms of certainly your writing style. But there are other things.
Kristen Sainani
Yeah, I think my career process is one of figuring out what I didn't like to do and figuring out what I did like to do. And I have always loved the idea of science. But I figured out both as an undergraduate and as a grad student that I don't really like doing science. I don't like the part where you have to pipette cells, I did that as an undergraduate and found that to be very boring. As a grad student, I ended up running a study on competitive runners, because I happen to be a competitive runner. And it was of interest to me. And I realized I didn't like the part where you had to recruit participants and you know, deal with IRBs. But there were two parts of that study that I loved. And one of them was that I wrote a monthly newsletter to keep participants engaged. And I found that really fun writing about current science, and thinking about science. And then I love the part where I got data. And I got to play with data. So I knew that graduating, I was either gonna be a writer or a statistician. And so somehow, I've managed at Stanford to do both, and I get to do both things that I love.
John Bailer
That's, you know, as someone who started out as pre med, and had chemistry convinced him that he liked doing the data analysis more than he liked to do data. You know, your comments resonate with me as well. So if you're gonna give a, you know, kind of a, I tried to think about what I'm going to do differently. I mean, you know, Rosemary was saying about her classes about how she's going to do things differently. And, you know, we have certainly a lot of classes where there's data practicum, or consulting kind of classes, if there are some some nuggets of wisdom that for for writing about science that you would extract from, from your, your course your you know, whether it's your Coursera course, or your your on campus course, what can can you suggest a couple of things that we might have as takeaways.
Kristen Sainani
Yeah, I mean, some of the interesting things that people have found useful, and I wasn't sure whether or not they would find them useful. What of the set of videos that I've published is me sitting over, you know, a 500 word essay and editing it in real time. And I thought, you know, I'm not sure anybody's gonna want to watch this, it's 20 minutes of me with track changes. And I can't tell you only about the positive feedback. I've gotten on that, because it's me talking through the process, and how I learned to write while sitting with an editor. And so I'm trying to mimic that, but not on the one to one level. And so you know, things like that. I've done the same thing with stats, which is, here's a, I'm going to talk you through how I approach data analysis and a video. And those are long, but surprisingly useful to people.
Rosemary Pennington
That's so interesting, because it makes this thing that feels unknowable, very accessible and transparent.
Kristen Sainani
I feel like it's when so what if I have to build an Ikea piece of furniture? I am bad at that kind of thing. What do I do? I go and look for a YouTube video where somebody did it in real time. It's the same kind of practical thing like I need to see somebody doing it for it to know how to do it myself.
John Bailer
Well, I mean, you clearly were an inspiration in terms of scientific writing, and you've had a tremendous impact. Thank you for that, that gift that you've given to the community. Well, if I'm afraid that's all the time we have. I mean, that's just not fair. I mean, it's a great conversation. It's not fair to cut it off. But we, but we have to, so I want to thank you for taking the time to join us today. Stats and Stories is a partnership between Miami University's Department of Statistics and media, journalism and film in the American Statistical Association. You can follow us on Twitter, Apple podcast, or other places where you can find podcasts. If you'd like to share your thoughts on our program. Send your email to StatsAndStories@miamioh.edu Or check us out at statsandstories.net and be sure to listen for future editions of Stats and Stories, where we discuss the statistics behind the stories and the stories behind the statistics.