Lambda the Ultimate

C.T. has been mentioned on LtU before. I do hope that it gets ever wider attention. I like this paper in that the approach is not mainly about making people be able to be programmers. It is about making people be able to understand things e.g. so they can (as I see it) understand items in the news, or talk about things over coffee. Even people who learn to program often miss the forest for the trees, I think.

How Computers Work: Computational Thinking for Everyone

What would you teach if you had only one course to help students grasp the essence of computation
and perhaps inspire a few of them to make computing a subject of further study? Assume they have
the standard college prep background. This would include basic algebra, but not necessarily more
advanced mathematics. They would have written a few term papers, but would not have written
computer programs. They could surf and twitter, but could not exclusive-or and nand. What about
computers would interest them or help them place their experience in context? This paper provides
one possible answer to this question by discussing a course that has completed its second iteration.
Grounded in classical logic, elucidated in digital circuits and computer software, it expands into
areas such as CPU components and massive databases. The course has succeeded in garnering the
enthusiastic attention of students with a broad range of interests, exercising their problem solving
skills, and introducing them to computational thinking.

"The course includes some computer programming, but does not dwell on it."

I wish more real-world day-job programmers (including refreshing my own self I can't be too hypocritical) really learned these things:

The big ideas in the honors course are
1. the correspondence between digital circuits and formulas in logic,
2. how abstractions facilitate combining solutions to small problems to form solutions to big ones,
3. how algebraic formulas can specify computations,
4. how models expressed in software capture the behavior of processes and devices,
5. how important, complex algorithms derive from simple, definitional properties,
6. how different definitional properties can produce the same results at vastly different computational
expense,
7. how computational expense makes some useful devices feasible and renders others infeasible,
8. and how all of these ideas bear on the ability of computers to deal with information on the massive
scale needed to provide services like search engines, internet storefronts, and social networks