New programming languages are important to develop as we improve our knowledge of how students learn computing

I was at a workshop at Google a couple weeks ago where someone asked me, “Do you still think that there’s a place for developing new programming languages in computing education?” I said, “ABSOLUTELY!”.

We know little about how people learn programming, and developing new programming languages is important for improving usability, learnability, and productivity of programmers (professional, novice, end-user, casual, or conversational). The interplay between design of programming languages and research into how people learn programming languages is a hot and important research topic. (See, for example, the recent Dagstuhl seminar on empirical data for programming language design.)

My Blog@CACM post for this month (see link here) is based on the cover story for the March Communications of the ACM (CACM), on “A Programmable Programming Language.” The (interesting and recommended) article is on building problem-specific programming languages. My post was about the educational questions raised by these languages. Would they be easier or harder to learn if they’re problem-specific? Will novices be willing to put in the effort to learn a programming language that is specific to a problem? Do problem-specific languages make it harder or easier to find (or train) programmers to work on old software (built in these problem-specific languages)? If a programmer learns a problem-specific programming language created at Company X, then leaves for Company Y and creates a similar problem-specific programming language, was intellectual property stolen?

Barbara Ericson’s defense was March 12 (as mentioned here). It was very successful — not only did she pass, but all of her committee signed off on the same day. She’s Dr. Ericson!

Alan Kay was on her committee and asked some insightful questions about her work with Parsons problems. In a Parson problem, students are ordering lines of code into a correct solution. Barb did her research using Python, and she’s also done work with Parsons problems in Java. These are pretty similar languages in terms of notional machines.

What’s the influence of the programming language on student success with Parsons problems? What if the underlying notional machine was simpler to understand? Would students find it easier to sequence a program? In general, we explore non-imperative programming paradigms so rarely in computing education research. We change modality (e.g., Scratch), but not the underlying computational model. The work with Racket is a rare example. Alan mentioned HyperCard in his comments, which was explicitly designed to be easy to learn. Would HyperCard programs be easier for students to order correctly?

I hope that we continue to invent new programming languages and explore the educational implications of them. There’s a big space of possible designs, and we have only started evaluating them empirically.