If there's computational thinking, there's computational learning
Slides from the keynote talk at the Scientific Python conference, SciPy 2014, Austin, TX.
In a departure with academic custom, this talk is dedicated to my grandparents. On Sunday, July 6, as I was packing my bags to fly out to Austin for SciPy, I learned that my grandmother is gone. She was a very important figure in my life, and left us just four months after my grandad. This work is dedicated to them, and their 75-year love story.
(1) I told you an anecdote of using IPython Notebooks in my aerodynamics class, and contrasted this to how an engineering student would traditionally encounter the piece of knowledge that my student interactively engaged with. Then I went on a bit of a historical tangent, to explain WHY it is that we teach engineering with such detachment from the “real world,” overemphasizing pure analytical content, and one-way “transmission of knowledge” from the professor to the student. This is a utilitarian view of knowledge that is insufficient —in fact, we cannot *produce* knowledge for students: it’s personal (cf. Stephen Downs)
And this is why OERs are not transformative, on their own. Open education *should* include the full share/reuse/remix combination and should include students sharing their own work.
(2) The reason why we care about Reproducibility in computational science is because computing creates scientific knowledege. We understand this in the context of research —using computing for scientific discovery.
But the implication from the point of view of teaching and learning is that computing has a pedagogical purpose within the science and engineering disciplines ... computing is a form of learning.
(3) This thinking is aligned with the modern Connectivist theory of learning. I took you a bit deeper than you probably wanted into philosophy, looking into the “pedagogy of connection” for working across disciplines, where boundary objects are the tools that faciliate such work ... in the rhetoric about “Computational Thinking,” the point is often made that computational thinking —where the key is an ability to abstract and solve problems algorithmically— is needed in every discipline: that is a suggestion to work across disciplines with computation.
Beyond “computational thinking,” I’m proposing computation as a learning tool. And here, I think, IPython Notebooks are the killer app.
“Knowledge is distributed”
— Pedagogical purpose of open sharing
“Computing creates knowledge”
— Pedagogical purpose of computing within disciplines
“Interactive computing is a tool of connection”
— IPython Notebooks are the killer app.
Follow the links to see the full video of the talk.