Tag: teaching


Sunday data/statistics link roundup (1/20/2013)

  1. This might be short. I have a couple of classes starting on Monday. The first is our Johns Hopkins Advanced Methods class. This is one of my favorite classes to teach, our Ph.D. students are pretty awesome and they always amaze me with what they can do. The other is my Coursera debut in Data Analysis. We are at about 88,000 enrolled. Tell your friends, maybe we can make it an even 100k! In related news, some California schools are experimenting with offering credit for online courses. (via Sherri R.)
  2. Some interesting numbers on why there aren't as many "gunners" in the NBA - players who score a huge number of points.  I love the talk about hustling, rotating team defense. I have always enjoyed watching good defense more than good offense. It might not be the most popular thing to watch, but seeing the Spurs rotate perfectly to cover the open man is a thing of athletic beauty. My Aggies aren't too bad at it either...(via Rafa).
  3. A really interesting article suggesting that nonsense math can make arguments seem more convincing to non-technical audiences. This is tangentially related to a previous study which showed that more equations led to fewer citations in biology articles. Overall, my take home message is that we don't need less equations necessarily; we need to elevate statistical/quantitative literacy to the importance of reading literacy. (via David S.)
  4. This has been posted elsewhere, but a reminder to send in your statistical stories for the 365 stories of statistics.
  5. Automatically generate a postmodernism essay. Hit refresh a few times. It's pretty hilarious. It reminds me a lot of this article about statisticians. Here is the technical paper describing how they simulate the essays. (via Rafa)

Why we are teaching massive open online courses (MOOCs) in R/statistics for Coursera

Editor’s Note: This post written by Roger Peng and Jeff Leek. 

A couple of weeks ago, we announced that we would be teaching free courses in Computing for Data Analysis and Data Analysis on the Coursera platform. At the same time, a number of other universities also announced partnerships with Coursera leading to a large number of new offerings. That, coupled with a new round of funding for Coursera, led to press coverage in the New York Times, the Atlantic, and other media outlets.

There was an ensuing explosion of blog posts and commentaries from academics. The opinions ranged from dramatic, to negative, to critical, to um…hilariously angry. Rafa posted a few days ago that many of the folks freaking out are missing the point - the opportunity to reach a much broader audience of folks with our course content. 

[Before continuing, we’d like to make clear that at this point no money has been exchanged between Coursera and Johns Hopkins. Coursera has not given us anything and Johns Hopkins hasn’t given them anything. For now, it’s just a mutually beneficial partnership — we get their platform and they get to use our content. In the future, Coursera will need to figure out a way to make money, and they are currently considering a number of options.] 

Now that the initial wave of hype has died down, we thought we’d outline why we are excited about participating in Coursera. We think it is only fair to start by saying this is definitely an experiment. Coursera is a newish startup and as such is still figuring out its plan/business model. Similarly, our involvement so far has been a little whirlwind and we haven’t actually taught courses yet, and we are happy to collect data and see how things turn out. So ask us again in 6 months when we are both done teaching.

But for now, this is why we are excited.

  1. Open Access. As Rafa alluded to in his post, this is an opportunity to reach a broad and diverse audience. As academics devoted to open science, we also think that opening up our courses to the biggest possible audience is, in principle, a good thing. That is why we are both basing our courses on free software and teaching the courses for free to anyone with an internet connection. 
  2. Excitement about statistics. The data revolution means that there is a really intense interest in statistics right now. It’s so exciting that Joe Blitzstein’s stat class on iTunes U has been one of the top courses on that platform. Our local superstar John McGready has also put his statistical reasoning course up on iTunes U to a similar explosion of interest. Rafa recently put his statistics for genomics lectures up on Youtube and they have already been viewed thousands of times. As people who are super pumped about the power and importance of statistics, we want to get in on the game. 
  3. We work hard to develop good materials. We put effort into building materials that our students will find useful. We want to maximize the impact of these efforts. We have over 30,000 students enrolled in our two courses so far. 
  4. It is an exciting experiment. Online teaching, including very very good online teaching, has been around for a long time. But the model of free courses at incredibly large scale is actually really new. Whether you think it is a gimmick or something here to stay, it is exciting to be part of the first experimental efforts to build courses at scale. Of course, this could flame out. We don’t know, but that is the fun of any new experiment. 
  5. Good advertising. Every professor at a research school is a start-up of one. This idea deserves it’s own blog post. But if you accept that premise, to keep the operation going you need good advertising. One way to do that is writing good research papers, another is having awesome students, a third is giving talks at statistical and scientific conferences. This is an amazing new opportunity to showcase the cool things that we are doing. 
  6. Coursera built some cool toys. As statisticians, we love new types of data. It’s like candy. Coursera has all sorts of cool toys for collecting data about drop out rates, participation, discussion board answers, peer review of assignments, etc. We are pretty psyched to take these out for a spin and see how we can use them to improve our teaching.
  7. Innovation is going to happen in education. The music industry spent years fighting a losing battle over music sharing. Mostly, this damaged their reputation and stopped them from developing new technology like iTunes/Spotify that became hugely influential/profitable. Education has been done the same way for hundreds (or thousands) of years. As new educational technologies develop, we’d rather be on the front lines figuring out the best new model than fighting to hold on to the old model. 

Finally, we’d like to say a word about why we think in-person education isn’t really threatened by MOOCs, at least for our courses. If you take one of our courses through Coursera you will get to see the lectures and do a few assignments. We will interact with students through message boards, videos, and tutorials. But there are only 2 of us and 30,000 people registered. So you won’t get much one on one interaction. On the other hand, if you come to the top Ph.D. program in biostatistics and take Data Analysis, you will now get 16 weeks of one-on-one interaction with Jeff in a classroom, working on tons of problems together. In other words, putting our lectures online now means at Johns Hopkins you get the most qualified TA you have ever had. Your professor.