Archive for March 5, 2018

The state of the field in pre-college computer science education: Highly recommended Google report

Google has just released a report: Pre-College Computer Science Education: A Survey of the Field (available here).  The report is authored by Paulo Blikstein of Stanford.  The report is innovative, developed with an unusual method.  It’s terrific, and I highly recommend it.

Paulo started out with a pretty detailed survey document about the state of the literature in computer science education. He covered from the 1967 launch of Logo to modern day.  Then he interviewed 14 researchers in the field (I was one). These were detailed interviews, where the interviewees got to review the transcript afterwards.  Paulo integrated ideas and quotes from the interviews into the document.  Here comes the really cool part: he put the whole thing on a Google doc and let everyone comment on it.

When I got the call to review the document, I just skimmed it.  It looked pretty good to me.  But then the debates started, and the fights broke out.  That Google doc had some of the longest threads of comments I’ve ever seen.  After a few weeks, Paulo closed the comments, and then integrated the threads into the document.  So now, it’s not just a serious survey paper, brought up to date with interviews.  It’s also a record of significant debate between over a dozen researchers, where the tensions and open questions were surfaced.

This is the document to read to figure out what should come next in computing education research.  I will recommend it to all of my students.

Of course, it’s not perfect.  The researchers interviewed tended towards the Logo/MIT/constructionist perspective.  The emphasis was on the US, though there were a couple of non-US interviewees.  If someone was to do this again (which I don’t recommend for a few years — it’ll take us awhile just to work on this agenda), I’d recommend including interviews with a wider range of folks:

  • We need to hear more voices from the evidence-based learning perspective, those inspired by Carl Wieman.  I’m thinking about people like Beth Simon, Leo Porter, Cynthia Lee, Christine Alvarado, and Dan Zingaro.
  • There’s no one on this list that I think would label themselves as a cognitive tutors or Learning at Scale researcher.  We need to hear from people like Mehran Sahami and Ken Koedinger.
  • I’m so glad that we have voices from the UK in this document, but if you’re going to go international, you have to include voices from the Nordic (e.g., Michael Caspersen, Jens Bennedsen, Lauri Malmi, Juha Sorva, and probably several from Upcerg, the world’s largest academic CS Ed research group), from Israel (e.g., Moti Ben-Ari, Judith Gal-Ezer, Yifat Kolikant, and Orit Hazzan), and from Australasia (e.g., Katrina Falkner, Ray Lister, Tim Bell).  The reality is that CS Ed Research is far larger outside the US than inside the US. There are more CS Ed researchers with a more diverse range of opinions outside the US.

I’m sure that I’m forgetting important voices, but this is enough to say that this report is a good first effort at bringing in a range of perspectives.  There are other important voices needed, if you really want to understand the state of CS education research at the pre-college level.

As it is, it’s still a fascinating and important report.  I’m biased — my thoughts and words are in there.  There is a range of opinions in there. I don’t agree with everything in there.  Paulo did a good job capturing the tensions around computational thinking, and I’m much more positive about blocks-based programming languages than are other voices in the report.

I highly recommend reading the report.

 

March 5, 2018 at 1:00 pm 3 comments

What can the Uber Gender Pay Gap Study tell us about improving diversity in computing?

The gig economy offers the ultimate flexibility to set your own hours. That’s why economists thought it would help eliminate the gender pay gap. A new study, using data from over a million Uber drivers, finds the story isn’t so simple.

Source: What Can Uber Teach Us About the Gender Pay Gap? – Freakonomics

A fascinating Freakonomics podcast tells us about why women are paid less than men (by about 7%) on Uber.  They ruled out discrimination, after looking at a variety of sources.  They found that they could explain all of that 7% from three factors.

They found that even in a labor market where discrimination can be ruled out, women still earn 7 percent less than men — in this case, roughly 20 dollars an hour versus 21. The difference is due to three factors: time and location of driving; driver experience; and average speed.

The first factor is that women choose to be Uber drivers in different places and at different times than men.  Men are far more often to be drivers at 3 am on Saturday morning. The second factor is particularly interesting to me.  Men tend to stick around on Uber longer than women, so they learn how to work the system. The third factor is that men drive faster, so they get more rides per hour.

When someone from Uber was asked about how they might respond to these results, he focused on the second factor.

But for example, you could imagine that if we make our software easier to use and we can steepen up the learning curve, then if people learn more quickly on the system, then that portion of the gap could be resolved via some kind of intervention. But that’s just an example. And we’re not there yet with our depth of understanding, to just simply write off the gender gap as a preference.

Improving learning might help shrink the gender pay gap.  Obviously, I’m connecting this to computing education here.  What role could computing education play in reducing gaps between males and females in computing?  We have reason to believe that our inability to teach programming well led to the gender gap in computing.  Could we make things better if we could teach computing well?

Here are two thoughts exploring that question.

  1. We know (e.g., from Unlocking the Clubhouse) that men tend to sink more time into programming, which can give them a lead in undergraduate education (what Jane Margolis has called ‘preparatory privilege‘).  What if we could teach programming more efficiently?  Could we close that gap?  If we had a science of teaching programming, we could improve efficiency so that a few hours of focused effort in the classroom might lead to more effective learning of tens of hours of figuring out how to compile under Debian Linux.
  2. When I first started thinking about the “phonics of computing education” and our ebooks, I was inspired by work from Caroline Simard that suggested that helping female mid-level managers keep up their technical skills could help them to progress in the tech industry.  Female mid-level managers have less time to invest in technical learning, and at the mid-level, technical education still matters.  If you have a project that needs a new toolset, you’ll more likely give it to the manager who knows that toolset.  If we could teach female mid-level technical managers more effectively and efficiently, could they make it into the C-suite of tech companies?

Maybe better computing education could be an important part of improving diversity, along multiple paths.

March 5, 2018 at 7:00 am 6 comments


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