Friday links: Being Black, why science news doesn’t go viral, MOOC fail, and more

From Meg:

This article describes the Being Black at the University of Michigan (#BBUM) hashtag that took off on twitter this week, receiving national attention. The hashtag is a great way to hear the voices of black college students – obviously it’s focused on UMich, but I suspect there are commonalities with the experiences of black students at other universities. Indeed, the article linked to above also includes a link to this video of a powerful spoken word poem performed by black students at UCLA.

From Jeremy:

Why well-reported science news doesn’t go viral: it falls in “ambiguity valley“. Amusing, but there’s something to it.

Relatedly: there’s a lot of actual data on what works and what doesn’t when it comes to communicating science to the public. Dan Kahan’s blog is one entry point. (h/t Sociobiology)

My friend Greg Crowther says that professors should share their self-assessments of their teaching with their students. I sort of do this–I talk to the students about why I teach in the way I do, tell them advance about new things I’m trying, and explain my reasons if I make any mid-course adjustments to my approach. Even to the point of saying to them (as I had to recently) “The way I’m teaching this class clearly isn’t working, as an instructor I’m letting you down, here’s what I’m going to do to fix it.” I find that students appreciate this, and that it encourages them to give me more and better feedback on my teaching (both during the course, and at the end).

College textbooks are expensive. But that may be about to change, thanks to free open source online texts and other new initiatives. We’ll see–many instructors, including me, are still picky about textbook choice, and don’t necessarily see “cost to students” as an overriding consideration. Actually, I haven’t ever had to worry too much about cost to students, as in the courses I teach I either don’t use a textbook or my first-choice textbook isn’t insanely priced. But if Case’s Illustrated Guide to Theoretical Ecology or Whitlock & Schluter’s introductory biostats text were to ever jump in price, I’d have tough decisions to make. (h/t

Udacity is getting out the business of providing “massive open online courses” (MOOCs) and switching to corporate training, with their founder admitting that the courses were often lousy. And he finally seems to have recognized something he should’ve recognized sooner, namely that students from disadvantaged backgrounds often are the students least well-prepared to learn via MOOCs. Which of course doesn’t resolve the serious issues of higher education access that MOOCs purportedly were going to solve. Much more on Udacity and its founder here. Relatedly, it’s worth remembering that the access and cost issues MOOCs were intended to address ultimately reflect policy choices. There’s nothing inevitable about the fact that even public universities are increasingly expensive. (h/t Jeremy Yoder, Brad DeLong)

Andrew Gelman with a very clear, non-technical post on the hidden dangers of noninformative priors in Bayesian statistics. Reminds me of a remark of Larry Wasserman’s, comparing noninformative priors to perpetual motion machines: everyone wants one, but they don’t exist.

And finally, science news may not go viral, but this has: an amazing interactive video of Bob Dylan’s “Like a Rolling Stone“. You can flip through a bunch of fake cable tv channels, with the people on each show lip syncing the song. It’s a bit of a one-trick pony–but when your one trick is this cool, you only need the one trick. Just click through already! 🙂

5 thoughts on “Friday links: Being Black, why science news doesn’t go viral, MOOC fail, and more

    • Will do my best Jim, but I’m no expert on Bayesian stats and I have a lot on my plate right now. My quick reaction is that Johnson’s solving the wrong problem. I do think there are some problems with current statistical practice, but I don’t think they’re to do with the choice of Bayesian vs. frequentist stats or that they’re best fixed by just being more conservative and choosing a lower type I error rate.

      I also think that more important than our choice of statistical methods are our choices of approaches–which and how many lines of evidence we look at to test substantive scientific claims. This is related to Meg’s recent post on the power of combining multiple approaches.

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