I appeared on Maryland Public Television's Direct Connection with Jeff Salkin last Monday to talk about MOOCs (along with our Dean Mike Klag).
Jeff and I talk about Jeff's recently completed MOOC on Data Analysis.
It is no secret that faculty evaluations at top universities weigh research much more than teaching. This is not surprising given that, among other reasons, global visibility comes from academic innovation (think Nobel Prizes) not classroom instruction. Come promotion time the peer review system carefully examines your publication record and ability to raise research funds. External experts within your research area are asked if you are a leader in the field. Top universities maintain their status by imposing standards that lead to a highly competitive environment in which only the most talented researchers survive.
However, the assessment of teaching excellence is much less stringent. Unless they reveal utter incompetence, teaching evaluations are practically ignored; especially if you have graduated numerous PhD students. Certainly, outside experts are not asked about your teaching. This imbalance in incentives explains why faculty use research funding to buy-out of teaching and why highly recruited candidates negotiate low teaching loads.
Top researchers end up at top universities but being good at research does not necessarily mean you are a good teacher. Furthermore, the effort required to be a competitive researcher leaves limited time for class preparation. To make matters worse, within a university, faculty have a monopoly on the classes they teach. With few incentives and practically no competition it is hard to believe that top universities are doing the best they can when it comes to classroom instruction. By introducing competition, MOOCs might change this.
To illustrate, say you are a chair of a soft money department in 2015. Four of your faculty receive 25% funding to teach the big Stat 101 class and your graduate program's three main classes. But despite being great researchers these four are mediocre teachers. So why are they teaching if 1) a MOOC exists for each of these classes and 2) these professors can easily cover 100% of their salary with research funds. As chair, not only do you wonder why not let these four profs focus on what they do best, but also why your department is not creating MOOCs and getting global recognition for it. So instead of hiring 4 great researchers that are mediocre teachers why not hire (for the same cost) 4 great researchers (fully funded by grants) and 1 great teacher (funded with tuition $)? I think in the future tenure track positions will be divided into top researchers doing mostly research and top teachers doing mostly classroom teaching and MOOC development. Because top universities will feel the pressure to compete and develop the courses that educate the world, there will be no room for mediocre teaching.
I'm happy to announce that my course Computing for Data Analysis will return to Coursera on January 2nd, 2013. While I had previously announced that the course would be presented again right here, it made more sense to do it again on Coursera where it is (still) free and the platform there is much richer. For those of you who missed it the last time around, this is your chance to take it and learn a little R.
I've gotten a number of emails from people who were interested in watching the videos for the course. If you just want to sit around and watch videos of me talking, I've created a set of four YouTube playlists based on the four weeks of the course:
The content in the YouTube playlists reflect the content from the first iteration of the course and will not reflect any new material I add to the second iteration (at least not for a little while).
I encourage everyone who is interested to enroll in the course on Coursera because there you'll have the benefit of in-video quizzes and other forms of assessment and will be able to interact with all of the great students who are also enrolled in the class. Also, if you're interested in signing up for Jeff Leek's Data Analysis course (starts on January 22, 2013) and are not very familiar with R, I encourage you to check out Computing for Data Analysis first to get yourself up to speed.
I look forward to seeing you there!
Jeff and I talk with Brian Caffo about teaching MOOCs on Coursera.
As the entire East Coast gets soaked by Hurricane Sandy, I can’t help but think that this is the perfect time to…take a course online! Well, as long as you have electricity, that is. I live in a heavily tree-lined area and so it’s only a matter of time before the lights cut out on me (I’d better type quickly!).
I just finished teaching my course Computing for Data Analysis through Coursera. This was my first experience teaching a course online and definitely my first experience teaching a course to > 50,000 people. There were definitely some bumps along the road, but the students who participated were fantastic at helping me smooth the way. In particular, the interaction on the discussion forums was very helpful. I couldn’t have done it without the students’ help. So, if you took my course over the past 4 weeks, thanks for participating!
Here are a couple quick stats on the course participation (as of today) for the curious:
I’ve received a number of emails from people who signed up in the middle of the course or after the course finished. Given that it was a 4-week course, signing up in the middle of the course meant you missed quite a bit of material. I will eventually be closing down the Coursera version of the course—at this point it’s not clear when it will be offered again on that platform but I would like to do so—and so access to the course material will be restricted. However, I’d like to make that material more widely available even if it isn’t in the Coursera format.
So I’m announcing today that next month I’ll be offering the Simply Statistics Edition of Computing for Data Analysis. This will be a slightly simplified version of the course that was offered on Coursera since I don’t have access to all of the cool platform features that they offer. But all of the original content will be available, including some new material that I hope to add over the coming weeks.
If you are interested in taking this course or know of someone who is, please check back here soon for more details on how to sign up and get the course information.
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.
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.