Back in November, Justin Reich wrote a post titled “Will Free Benefit the Rich?” (re-posted as “Open Educational Resources Expand Educational Inequalities“). In it, he outlines two possible futures. In scenario 1, everyone benefits from free online resources in ways that narrow the gap between the rich and the poor. In scenario 2, everyone benefits, but the well-off benefit more, so the gap widens. What caused a fuss was his claim that the empirical data we have so far supports scenario 2: in the year of Occupy, saying that something is going to increase inequality is going to stir up strong emotions.

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Several people have written some useful comments on my recent “where are we going?” posts. It’s exactly the kind of feedback I was after, so here are my answers.

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As George Pòlya said, sometimes the best way to solve a problem is to solve a more general one. In that spirit, this post was originally going to be about the mechanics of helping thousands of people a year (which is the first of our five-year goals). After getting feedback from a few people on early drafts, though, it has morphed into a discussion of something that I hope you’ll find more interesting [1].

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Prakash Prabhu and others recently published “A Survey of the Practice of Computational Science” based on information from 114 researchers at Princeton University. The emphasis is different from that of the survey Hannay and others (including me) did in 2008-09, but the findings are broadly similar.

Having talked about what I’ve learned and how well our teaching measures up, I’d like to explore what success would actually look like for Software Carpentry. Our long-term objective is to make productive and reliable computing practices the norm in science and engineering. My model is the way statistics became a “normal” part of most scientists’ training in the 20th Century. Most psychologists and geologists are emphatically not statisticians, but most of them know (or at least have known, at some point) what a t-test is, what a correlation coefficient means, and how to tell when they’re out of their depth. Equally, most scientists should know how to pipe a few simple filters together to clean up their data, how to use version control to track and share their work, whether their computational results are as trustworthy as their experimental results [1], and when to go and talk to either a real numerical analyst or a professional software developer.

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I had breakfast a couple of days ago with Jon Pipitone, a former student who has helped out with Software Carpentry off and on in the past. When we discussed my post summarizing what I’ve learned so far about online education, he had several questions and suggestions (thanks, Jon). I’m still digesting everything he said, but there is one point I’d like to act on now.

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I received email a few days ago from someone who had just found this site (reproduced here with permission):

I am working on getting myself set up to do scientific programming in Python on a MacBook Pro. I plan to use mySql and Pentaho. I am new to open source, Mac, and Python. Where I’ve run into the most problems is getting a working environment established with all the tools in place. For instance, I ran into a huge problem trying to install mysqldb, the Python/MySQL interface package. Trying to resolve that, I’ve encountered a bewildering array of alternative ways to do things, each accompanied by strong opinions and each entailing a different group of packages. One example being package managers: easy_install, pip, various brews, macports, etc. I don’t really care about some absolute right way—I just need working, consistent tool set.

I responded:

I wish I could help you—I really wish I could help you, but installation is Python’s weakest point. Every time we run a class it seems as if students spend almost as much time wrestling with packages as they do with everything else put together. “Bewildering” is an accurate description, and while there’s some hope of things getting better, we aren’t there yet. Virtualenv + pip isn’t a complete solution, but it does make it easier to back out failed installations (which I guess is something). I’m sorry I don’t have anything better to offer.

And a colleague’s response was:

Instead of having a systematic package installation solution, I mostly just plow through a series of failures. That is, if I find I don’t already have a module via the EPD [the Enthought Python Distribution], I usually also find it’s not available through macports. Next, I usually end up trying to easy_install it. If that doesn’t work, I resort to pip. Doing this for each new package that I need is not pretty. I wouldn’t recommend it, and I applaud you for trying to bring order to your life instead… Anyway, unfortunately for you, neither pentaho or mysqldb are installed with the EPD, and neither apears to be available through macports, so my only pieces of positive advice here are really quite useless.

This continues to be the biggest headache our students face, not least because they have to deal with it on day zero, before they’ve learned what they need to know in order to diagnose and fix problems. I’m slowly coming around to the notion that we should just give students a virtual machine to use for the first couple of days, and only then try to get things installed natively on their machines. Opinions on this (particularly ones backed up by experience) would be appreciated.

I worked on Software Carpentry full-time for a year starting in May 2010. In that time I created over 140 PowerPoint decks, about 12 hours of video, and taught the course six times (twice online and four times in person). I also learned a few things that I hope other people will find useful:

1. Online teaching isn’t as effective as in-person teaching.
2. Today’s tools suck.
3. If content is king, then community is queen, pope, emperor, and president-for-life.
4. If you don’t know how to tell if you succeeded, you’re going to fail.
5. If you don’t take the time to explore what other people have done, you deserve to fail.

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Carly Strasser, of the Digital Curation for Excel Project, has posted some ideas about new features that could be added to Excel to make it more useful for scientists. In brief, the list is:

1. Generate metadata.
2. Generate a data citation for the data file.
3. Check the spreadsheet for export compatibility.
4. Link to archive services.

Feedback and suggestions would be welcome…

I will be running a P2PU course starting in January on teaching free-range learners how to program and build stuff on the web. The blurb is below; anyone who wants to can sign up to follow along or take part (we expect it will require 3-4 hours/week from mid-January to some time in April). I’m not an expert on these subjects by any means, but I’ve learned a few things from running Software Carpentry that I think are worth sharing, and hope that this course will give me a chance to learn more. If you’re interested in teaching scientists how to do things with computers, please come and join us.

How to Teach Webcraft and Programming to Free-Range Students

What do we know about how novices learn webcraft and programming, why do we believe it, and how can we apply that knowledge to free-range learners?

Right now, people all over the world are learning how to write programs and create web sites, but or every one who is doing it in a classroom there are a dozen free-range learners. This group will focus on how we, as mentors, can best help them. Topics will include:

• What does research tell us about how people learn?
• Why are the demographics of programming so unbalanced?
• What best practices in instructional design are relevant to free-range learners?
• What skills do people need in order to bake their own web?
• How are grassroots groups trying to teach these things now?
• What’s working and what isn’t?