This is a really great initiative, not least as it will provide a space within which to clarify different understandings of learning in the context of citizen science. We all tend to use the concept of learning without necessarily defining it. And there are many different theories of how learning occurs, whihc are not always made explicit, even among educators! So a space to explore and evidence this multiplicity and difference is very valuable.
And as a provocation, personally I am wary of the notion of learning as either necessarily or inherently transformative. Most learning might be considered mundane – and that is not a negative judgement.
Too many possibilities for one blog comment! But a couple of questions that would really benefit from more experimental data, IMHO:
1) How do implicit versus explicit learning goals affect scientific process skills?
2) A lot of work has focused on the degree of participation in the process of science among different cit sic endeavors, but does that spectrum affect students’ ability to transfer scientific reasoning across domains? Presumably yes, but I think the evidence is still shaky!
3) When students create experimental designs, does it matter if it is part of a citizen science project in which the data are meant to be shared/used instead of a more traditional lab experience where the is no expectation of authentic science and useful data?
4) Along those same lines, is there a tradeoff when using citizen science in the classroom between depth of engagement with the experimental design and the fate of the data (e.g. you can collect some “real” data for this project that has already been designed OR you can design a randomized, controlled experiment whose data will never leave the classroom)? Obviously the tradeoff doesn’t have to exist, but in reality it may exist, and if it does, does that translate into learning tradeoffs?
A ripe research culture is so exciting!
Great ideas David! I would love to see the questions you raise above put to study. In particular, I am interested in the notion of trade-offs as you suggest but also on the project level. When do projects require of volunteers, in depth engagement versus enacting topical tasks? How are resources allocated differently between projects that invest more in learning and learning transfer versus those that rely on more minimally trained volunteers and how does this differential allocation of resources affect outcomes?
As David said, lots to comment on here. What’s interesting is that usually educators and learning scientists ask “we want students to learn X, how should we best accomplish that?” With learning in CS, we’re asking “We have an approach that might teach some people something. What is it that they’ll be learning?”
Also of note: the post makes a distinction between “mastering the program protocols for participation” and other kinds of learning. In many cases, yes, I agree that the “training” part of participation is not the kind of learning that we’re interested in. But what if fully participating in the CS program actually DOES require “the participant [to] expand his or her ideas, interests, sense of self, skills, or understanding of scientific concepts”? There are some cases where I think that’s absolutely the case.
I’d like to see discussions of specific projects in the blog – both what the participants are doing and what the learning goals are/could be. Then we can discuss in the comments how we think the learning is taking place, how we might measure the learning, and whether this is the best way to accomplish these learning goals.
Thanks for getting the conversation started!
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