I had the opportunity to introduce MATLAB to two groups of 30 graduate students fitting the following profile:

  1. Non-majors in Computer Science

  2. Novice MATLAB programmers

  3. Programming experience in some language (C/Java)

Learners came in with a broad range of expectations:

  • What is MATLAB, and what are people doing with it?,
  • How to do X with MATLAB?,
  • How can I use MATLAB more effectively than I already am?

I'm a huge fan of Software Carpentry and their evidence-based approach to teaching. The argument makes sense: we are scientists, and founding our teaching methods on hard research--or at the very least, some data--is likely a better idea than using arbitrary lesson plans and teaching techniques. Accordingly, as a starting point, I chose the Software Carpentry lesson material on Python These lessons have the immense value of feedback from several workshops that have been run by trained instructors all over the world. Of course, the first step would have had to be translating all that lesson material from Python to MATLAB. Which I did. This doubled up as my very first contribution to the open-source community (yay).

The concepts covered are, in this order:

  • Loading, analyzing and visualizing data from a file
  • Writing MATLAB scripts and loops
  • How and why to write MATLAB functions
  • Conditionals in MATLAB
  • Writing tests for functions

The lessons are built around a central task: analyzing and visualizing the data from several .csv files. With every lesson, we make our code for doing this a little better. For example, in lesson 1, we load, analyze and visualize the data interactively (from the command line). In lesson 2, we put those commands in a script, and discuss the pros and cons over computing interactively. We proceed to introduce loops, and modify our script to analyze several datasets. And so on.

I've done sessions on MATLAB before, and I used to follow a "textbook" approach: exposing ideas in the same order that they would appear in a textbook on MATLAB, for example:

  • Variables and Statements
  • Vectors
  • Plots
  • Matrices
  • Loops
  • Conditionals
  • Scripts
  • Functions

So, in one of my earlier sessions, the first few lines of code we would type in to the command line would be something like this:

>> a = 1
>> b = 2
>> a + b
>> a * b
>> c = [a, b]

Compare that to the first couple of lines of code that we type in now:

>> patient_data = csvread(`inflammation-01.csv`);
>> imagesc(patient_data)

I think that exposing this sort of powerful functionality early is important: it makes learners feel like "this might actually be worth my time" and encourages them to participate more.

Getting novice programmers to follow along command-by-command is slow: they're going to meet with a lot of errors, even with the simplest of commands. The most common mistakes I've seen learners make in workshops:

  • Typos
  • Calling scripts/functions from the wrong directory

This is natural and expected; a new programming environment takes time to get used to, and learners simply don't have enough context to make sense of error messages. It's tempting then, to demo-ize the whole thing and keep participants from writing too much code. Of course, that's a bad idea, and I prefer an approach that's somewhere in-between:

Commands

Have learners type out commands on the shell while introducing ideas for the first time and demonstrate commands when expanding on them or explaining subtleties.

Scripts/functions

Have learners type out stripped-down, simple versions of more complex scripts. For example, instead of having learners write a script that loops over several datasets, performs analyses, and plots various figures for each, have them type out and execute a script that performs a single analysis on a single dataset, and produces a single figure. Then ask them to look at a more complex version of that script that was distributed to them at the beginning of the session.

I also experimented with a workshop etherpad, and gave learners the option of taking notes there instead of on their personal notepads/computers. Most learners preferred not to interact too much with the etherpad, I'm not sure why - maybe this should be part of the feedback - but here are some possible reasons:

  • Not enough time
  • Not familiar with etherpad
  • Not as convenient/useful as personal notes

I gave participants the option of providing feedback, either on the public etherpad, or on pieces of paper. Feedback on paper was generally more specific and comprehensive. Response was positive, with complaints about not having enough time and not covering enough/specific material. The structure and content of the lessons was generally appreciated, although there were mixed opinions on the section on testing.