Tag: MOOC

06
May

Talking about MOOCs on MPT Direct Connection

Watch Monday, April 29, 2013 on PBS. See more from Direct Connection.

I appeared on Maryland Public Television's Direct Connection with Jeff Salkin last Monday to talk about MOOCs (along with our Dean Mike Klag).

25
Mar

Podcast #6: Data Analysis MOOC Post-mortem

Jeff and I talk about Jeff's recently completed MOOC on Data Analysis.

08
Jan

By introducing competition open online education will improve teaching at top universities

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.

 

14
Dec

Computing for Data Analysis Returns

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!

19
Nov

Podcast #5: Coursera Debrief

Jeff and I talk with Brian Caffo about teaching MOOCs on Coursera.

29
Oct

Computing for Data Analysis (Simply Statistics Edition)

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:

  • 50,899: Number of students enrolled
  • 27,900: Number of users watching lecture videos
  • 459,927: Total number of streaming views (over 4 weeks)
  • 414,359: Total number of video downloads (not all courses allow this)
  • 14,375: Number of users submitting the weekly quizzes (graded)
  • 6,420: Number of users submitting the bi-weekly R programming assignments (graded)
  • 6393+3291: Total number of posts+comments to the discussion forum
  • 314,302: Total number of views in the discussion forum

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.

23
Sep

Sunday Data/Statistics Link Roundup (9/23/12)

  1. Harvard Business school is getting in on the fun, calling the data scientist the sexy profession for the 21st century. Although I am a little worried that by the time it gets into a Harvard Business document, the hype may be outstripping the real promise of the discipline. Still, good news for statisticians! (via Rafa via Francesca D.’s Facebook feed). 
  2. The counterpoint is this article which suggests that data scientists might be able to be replaced by tools/software. I think this is also a bit too much hype for my tastes. Certain things will definitely be automated and we may even end up with a deterministic statistical machine or two. But there will continually be new problems to solve which require the expertise of people with data analysis skills and good intuition (link via Samara K.)
  3. A bunch of websites are popping up where you can sign up and have people take your online courses for you. I’m not going to give them the benefit of a link, but they aren’t hard to find these days. The thing I don’t understand is, if it is a free online course, why have someone else take it for you? It’s free, its in your spare time, and the bar for passing is pretty low (links via Sherri R. redacted)….
  4. Maybe mostly useful for me, but for other people with Tumblr blogs, here is a way to insert Latex.
  5. Brian Caffo shares his impressions of the SAMSI massive data workshop.  He raises an important issue which definitely deserves more discussion: should we be focusing on specific or general problems? Worth a read. 
  6. For the people into self-tracking, Chris V. points to an app created by the University of Indiana that lets people track their sexual activity. The most interesting thing about that app is how it highlights a key and I suppose often overlooked issue with analyzing self-tracking data. Despite the size of these data sets, they are still definitely biased samples. It’s only a brave few who will tell the University of Indiana all about their sex life. 
12
Aug

Sunday data/statistics link roundup (8/12/12)

  1. An interesting blog post about the top N reasons to do a Ph.D. in bioinformatics or computational biology. A couple of things that I find interesting and could actually be said of any program in biostatistics as well are: computing is the key skill of the 21st century and computational skills are highly transferrable. Via Andrew J. 
  2. Here is an interesting auto-complete map of the United States where the prompt was, “Why is [state] so”. It seems like using the Google auto-complete functions can lead to all sorts of humorous data, xkcd has used it as a data source a couple of times in the past. By the way, the person(s) who think Idaho is boring haven’t been to the right parts of Idaho. (via Rafa). 
  3. One of my all-time favorite statistics quotes appears in this column by David Brooks: “…what God hath woven together, even multiple regression analysis cannot tear asunder.” It seems like the perfect quote for any study that attempts to build a predictive model for a complicated phenomenon where only limited knowledge of the underlying mechanisms are known. 
  4. I’ve been reading up a lot on how to summarize and communicate risk. At the moment, I’ve been following a lot of David Spiegelhalter’s stuff, and really liked this 30,000 foot view summary.
  5. It is interesting how often you see R popping up in random places these days. Here is a blog post with some clearly R-created plots that appeared on Business Insider about predicting the stock-market. 
  6. Roger and I had a post on MOOC’s this week from the perspective of faculty teaching the courses. For a more departmental/administrative level view, be sure to re-read Rafa’s post on the future of graduate education
10
Aug

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.