Holding ourselves to a higher standard: “Language-independent” just doesn’t cut it

My CACM blog post this month (see link here) is a retraction of the term “language-independent” in our work on the FCS1 and SCS1:

There is no language independence here. The FCS1 and SCS1 are multi-lingual which is a remarkable achievement. We might also call them pseudcode-based assessments, which is how they can be multi-lingual, but since a pseudocode-based test isn’t necessarily validated across other languages, “multi-lingual” is a stronger claim than “pseudocode-based.” We do not cover all of any of those languages (Java, MATLAB, or Python), but we do cover the subset most often appearing in an introductory CS course.

They are clearly not language independent. In the great design space of programming languages, Java, MATLAB, and Python cluster together pretty closely. There are much more different programming languages than these — I’m sure it’ll take any reader here just a few moments to generate a half-dozen candidates whose learners would score poorly on the FCS1 and SCS1, from Scratch to Haskell to Prolog.

I only vaguely remember the discussion about using the term “language independence” with Allison many years ago.  I remember her asking me if we should worry about the (relatively few) classes that used languages other than Python, MATLAB, and Java.  I think I told her she needed to graduate. I judged from the perspective of what was being published at the SIGCSE Symposium — Python, MATLAB, and Java was “language independent” enough for the paper to be seen as valuable to SIGCSE reviewers. I don’t remember the details, but I’ll accept the blame for the decision to call FCS1 (and SCS1 later) language independent.

That was a long time ago, before the International Computing Education Research Conference (ICER) was invented.  Since then, we have a computing education research community that aims to answer questions about how people learn computing — period. We’re not just about undergraduate introductory computer science classes. Even at the undergraduate level, we should study the classes (no matter how few) doing something different to see what’s powerful and interesting about them. We should explicitly be exploring unusual (even purpose-invented) languages to understand more of the interaction between programming languages and human cognition.  An insightful PPIG paper from Clayton Lewis (see link here) was recently circulated on Twitter (see tweet) that makes great points about the complexity of measuring that interaction:

The PPIG community should be proud that cognitive dimensions analysis emerged from the work of people in its ranks, Thomas Green, Marian Petre, Alan Blackwell, and others. We should be skeptical of calls to replace its use with A-B trials or other quantitative methods that cannot cope with the complexity of the language design landscape. When results of A-B trials and similar studies are presented, we should diplomatically ask for the mechanisms that are involved to be described. Colleagues who present the results of such trials should be prepared to respond to this request, so that the generalizability of their results can be assessed.

We should not be driven by what’s in classrooms today (see previous post making that argument). We should hold ourselves to a higher standard. Our goal is to create a lasting record of exploration and research for a research community.

That’s why it’s past time for this retraction.