Amanda, powerful ideas in science

Collisions, like in Simpson et al. (2005), are one example of a potentially powerful idea in science. Children witness different kinds of collisions in everyday life, particularly 1-D collisions or two colliding objects, so the idea of collisions can be powerful in its roots by building on students’ prior experiences. The authors even say that “it is more productive from a pedagogical perspective to focus attention on knowledge that is contiguous with students’ prior understandings” (Simpson et al., 2005, p. 6). Genetics is another example from science that connects to children’s roots. It’s something children witness every day and talk about, like eye color, hair color, skin color, height, weight, gender, etc. Levels, in the way it’s presented in Wilensky & Resnick’s (1999) paper, is another powerful idea because it connects to problems in a variety of situations that involve systems, including lots of things in science.

Doesn’t every idea have the potential to be powerful, depending on how it is used or how a learner is exposed to it? In other words, are there really any ideas that could never be considered powerful? It seems like ideas could be disempowered based on how they’re used or taught, but they could certainly be empowered again if approached in a different way. That’s probably the point, anyway. It’s not whether the idea itself is powerful because it can’t really be powerful by itself, it’s only powerful when you consider the idea in context with the people engaging with the idea, the tools available to explore the idea, etc.

On another note, the notion of reaching “more students more quickly by getting science teachers to add computers into their classes” rather than “developing a nationwide cohort of computer science teachers” is interesting (Wilensky, Brady, Horn, 2014, p. 24). It may be quicker because you don’t have to wait for a bunch of new teachers to be trained and to take the time to restructure the school day to figure out how to fit a separate computing course into the agenda. But is it actually better? They say that they’ve experienced that some teacher training and materials are sufficient for bringing computational modeling into science classrooms, but what about other kinds of computing in other classrooms/subjects? For instance, is it really quicker or even better to train existing math teachers to bring programming into their math classes? Maybe. I would think that to do it well, it requires strong disciplinary knowledge. So good math teachers with strong mathematical understandings and an interest in learning more would probably be able to incorporate computing concepts into their classes faster and better than people trained to be computer science teachers. But it’s a difficult balance to find.  

I think a bigger argument for this strategy of incorporating computing into existing classes is that it exposes more students to computing than if it was a separate, possibly elective course. Students who wouldn’t normally sign up for a computer science course would be exposed to computing concepts, which is really valuable. And it exposes them to computing ideas in a potentially more powerful way because students might make more connections to situations outside of computing, like mathematics or science (whatever course it’s included in), which they might find useful for solving problems that they’re interested in, and which might build on students own knowledge and identity with the discipline of focus.