From MOOC Students to Researchers

Posted on 18 June 2013.

Much has been written about MOOCs, including the potential for its users to be treated, in effect, as research subjects: with tens of thousands of users, patterns in their behavior will stand out starkly with statistical significance. Much less has been written about using MOOC participants as researchers themselves. This is the experiment we ran last fall, successfully.

Our goal was to construct a “tested semantics” for Python, a popular programming language. This requires some explanation. A semantics is a formal description of the behavior of a language so that, given any program, a user can precisely predict what the program is going to do. A “tested” semantics is one that is validated by checking it against real implementations of the language itself (such as the ones that run on your computer).

Constructing a tested semantics requires covering all of a large language, carefully translating its every detail into a small core language. Sometimes, a feature can be difficult to translate. Usually, this just requires additional quantities of patience, creativity, or elbow grease; in rare instances, it may require extending the core language. Doing this for a whole real-world language is thus a back-breaking effort.

Our group has had some success building such semantics for multiple languages and systems. In particular, our semantics for JavaScript has come to be used widely. The degree of interest and rapidity of uptake of that work made clear that there was interest in this style of semantics for other languages, too. Python, which is not only popular but also large and complex (much more so than JavaScript), therefore seemed like an obvious next target. However, whereas the first JavaScript effort (for version 3 of the language) took a few months for a PhD student and an undergrad, the second one (focusing primarily on the strict-mode of version 5) took far more effort (a post-doc, two PhD students, and a master's student). JavaScript 5 approaches, but still doesn't match, the complexity of Python. So the degree of resources we would need seemed daunting.

Crowdsourcing such an effort through, say, Mechanical Turk did not seem very feasible (though we encourage someone else to try!). Rather, we needed a trained workforce with some familiarity with the activity of formally defining a programming language. In some sense, Duolingo has a similar problem: to be able to translate documents it needs people who know languages. Duolingo addresses it by...teaching languages! In a similar vein, our MOOC on programming languages was going to serve the same purpose. The MOOC would deliver a large and talented workforce; if we could motivate them, we could then harness them to help perform the research.

During the semester, we therefore gave three assignments to get students warmed up on Python: 1, 2, and 3. By the end of these three assignments, all students in the class had had some experience wrestling with the behavior of a real (and messy) programming language, writing a definitional interpreter for its core, desugaring the language to this core, and testing this desugaring against (excerpts of) real test suites. The set of features was chosen carefully to be both representative and attainable within the time of the course.

(To be clear, we didn't assign these projects only because we were interested in building a Python semantics. We felt there would be genuine value for our students in wrestling with these assignments. In retrospect, however, this was too much work, and it interfered with other pedagogic aspects of the course. As a result, we're planning to shift this workload to a separate, half-course on implementing languages.)

Once the semester was over, we were ready for the real business to begin. Based on the final solutions, we invited several students (out of a much larger population of talent) to participate in taking the project from this toy sub-language to the whole Python language. We eventually ended up with an equal number of people who were Brown students and who were from outside Brown. The three Brown students were undergraduates; the three outsiders were an undergraduate student, a professional programmer, and a retired IT professional who now does triathlons. The three outsiders were from Argentina, China, and India. The project was overseen by a Brown CS PhD student.

Even with this talented workforce, and the prior preparation done through the course and creating the assignments prepared for the course, getting the semantics to a reasonable state was a daunting task. It is clear to us that it would have been impossible to produce an equivalent quality artifact—or to even come close—without this many people participating. As such, we feel our strategy of using the MOOC was vindicated. The resulting paper has just been accepted at a prestigious venue that was the most appropriate one for this kind of work, with eight authors: the lead PhD student, the three Brown undergrads, the three non-Brown people, and the professor.

A natural question is whether making the team even larger would have helped. As we know from Fred Brooks's classic The Mythical Man Month, adding people to projects can often hurt rather than help. Therefore, the determinant is to what extent the work can be truly parallelized. Creating a tested semantics, as we did, has a fair bit of parallelism, but we may have been reaching its limits. Other tasks that have previously been crowdsourced—such as looking for stellar phenomena or trying to fold proteins—are, as the colloquial phrase has it, “embarrassingly parallel”. Most real research problems are unlikely to have this property.

In short, the participants of a MOOC don't only need to be thought of as passive students. With the right training and motivation, they can become equal members of a distributed research group, one that might even have staying power over time. Also, participation in such a project can help a person establish their research abilities even when they are at some remove from a research center. Indeed, the foreign undergraduate in our project will be coming to Brown as a master's student in the fall!

Would we do it again? For this fall, we discussed repeating the experiment, and indeed considered ways of restructuring the course to better support this goal. But before we do, we have decided to try to use machine learning instead. Once again, machines may put people out of work.