[mncharity]

> More oral defense of arguments. [...] More [...] work in which students must explain not only what a result shows but what it does not.

More rough-quantitative reasoning? Fermi questions. Especially if done by collaborative iterative bounding "Who can suggest another soft/hard upper/lower bound? ... What do you think of that argument?"

In contrast to a plug-and-chug theme, illustrated by an ideal gas law problem in a popular textbook, which despite years of use, and qc passes for multiple editions, has numbers for solid Argon. Reality checking, a feel for reasonable values, a "Is this approximation plausible here?", being pervasively "not on the exam".

[jfengel]

Terrible word problems bug me. They seem to say, "this isn't actually useful in the real world, and we couldn't come up with even one realistic example."

I know some of it is the result of the incremental way we teach, such that there isn't any meaningful use of this step until it's combined with others. Still, students can only find the amount of fence required for a field before concluding that it does not in fact matter.

[mncharity]

> and we couldn't come up with even one realistic example

This worries me about how we approach teaching rough-quantitative reasoning. It's widely thought of great importance for expertise. But even when there seems outlier opportunity (a first-tier institution emphasizing teaching, a dedicated class, for non-freshman, in physics), the questions... "Calculate how long to cook a turkey for, without reading the label or googling?" Really? Getting to a clear payoff may require a broader scope, or a different emphasis, or a reimagining.

> isn't any meaningful use of this step until it's combined with others

A kindergarten science educator suggests their students have a human right to understand their physical world then, not a lifetime (for them) later. Intriguing to think what that might look like as a goal... at any level.

Science education research, like teachers, is focused on the possible. Existing students, with existing resources, and constraints, and objective metrics. There's little incentive to stack counterfactuals: what if my incoming students had been taught A successfully, and B and C successfully, then done together, they enable D, and oh wow. Not when we're struggling to manage even one with any consistency. Disaster-triage chain-of-care doesn't encourage funding of population heath research. Especially when the possibility space is so little explored, that we don't even have a vision of what an E and F might look like. A incentive bootstrap challenge.