Filed under: Uncategorized | Tags: distributed algorithms, education, game theory, theory
Suresh Venkatasubramanian at geomblog blogs recent about active learning modules for graduate algorithms. I’ve frequently used active learning in undergraduate classes but somehow have found it less effective for graduate classes. However, just recently, I ran across a nice paper by Sivilotti and Pike that describes kinesthetic learning activities for a distributed algorithms class. The basic idea is to have each student simulate a processor that is running a distributed algorithm. Examples they discuss are non-deterministic sorting, parallel garbage collection, a stabilizing token ring, and stabilizing leader election. I also think the ideas could be applied to more advanced/theoretical distributed algorithms. A great thing about distributed computing is that it lends itself nicely to this sort of classroom activity.
I have not yet used these ideas in a graduate class. However, I frequently using active learning in undergrad classes and the students seem to appreciate it. I’ll probably try out some of the ideas in the next academic year. I’m teaching a game theory class in the spring where I can use these types of activities – even perhaps for “hard” problems like e.g rational secret sharing, auctions, etc.
Leave a Comment so far
Leave a comment