Rules work as a way of communicating computation at a mechanistic level without teaching programming

Sometimes as a reviewer, you get to read a paper that you wish was published immediately. That’s how I felt when I got to review Eliane Wiese and Marcia Linn’s paper “It Must Include Rules”: Middle School Students’ Computational Thinking with Computer Models in Science. It was published in ACM TOCHI in April (see link here).

Eliane and Marcia offer a solution to a problem that teachers face when they want to teach about computational models, but they don’t want to teach programming. How do you get students to reason about the models underlying the simulations they’re exploring without talking about program code? And if you do talk about some notation, some representation of the model, what can you expect students to reason about without teaching them the notation or representation first?

Eliane and Marcia show that rules work. They have students interact with simulations, and then show them rules that might be in that model. Like in a simulation of light, photosynthesis, and glucose levels in plants, a rule might be: When light is on, total glucose made increases.. Eliane and Marcia show rules to students and ask “Are these in the model?” In their abstract, they write:

In our sample, 99% of students identified at least one key rule underlying a model, but only 14% identified all key rules; 65% believed that model rules can contradict; and 98% could not distinguish between emergent patterns and behaviors that directly resulted from model rules. Despite these misconceptions, compared to the “typical” questions about the science content alone, questions about model rules elicited deeper science thinking, with 2–10 times more responses including reasoning about scientific mechanisms. These results suggest that incorporating computational thinking instruction into middle school science courses might yield deeper learning and more precise assessments around scientific models.

The misconceptions don’t bother me. Students will have misconceptions about models — that’s part of teaching science with models. What’s fascinating to me is that the rules worked. Students reasoned mechanistically about the computational models.

My favorite result in this study was where they asked students to predict what would happen if they added a new rule to the model. Basically, “What happens if we change the program like this?” Students were way better at playing these what-if games if the question was posed as a rule. Quoting from the paper:

Asking students to make predictions about the implementation of incorrect rules led to more scientific reasoning about mechanisms than simply asking students about a causal relationship portrayed in a correct model. This pattern was evident for both model contexts, with twice as many workgroups proposing mechanisms with the New Rule question compared to the Typical question for Global Climate (29% vs. 14%) and ten times as many workgroups doing so for Chemical Reactions (53% vs. 5%).

Students can reason about computational models described as rules, even without instruction on rules. That’s a terrific result. It’s one that I’m thinking about how to use in my task-specific programming languages.

Now, this isn’t saying that students can’t reason with function or with imperative statements. Maybe functional or procedural programming paradigms would work, too. Eliane and Marcia have found one approach that does work. They offer us a way to integrate computational modeling into science education, with real discussion of the mechanism of the models, without teaching programming first.