Last year, I gave a keynote that launched from the idea of “computational thinking” and then put forward that computing generates new knowledge (which is why we care about reproducibility in computational science) and proposed that computing is hence a form of learning. I had this insight from seeing students work with IPython (now Jupyter) notebooks in my classroom and by reflecting on the point of view of connectivist knowledge. Connectivism posits that knowledge is created by interconnecting individuals in a community; it is distributed across a network of connections. Learning happens through interactions and conversations. Computing can thus be a way to interact, a form of conversation with the system under study, and so it enters in the learning process. Computational thinking, as popularized in the last decade, is an approach to problem solving inspired by computer science. In comparison to how the term was originally used by Seymour Papert in Mindstorms (1980), the contemporary definition seems shallow. Papert said, “My interest is in universal issues of how people think and how they learn to think.” He imagined that the learning environment could be reshaped by computing. Tools like Jupyter notebooks are a new opportunity to realize that dream. They are what I call computable content: educational content made powerfully interactive via compute engines in the learning platform.
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