For the first time ever, the Education section contains three papers. All three are true examples of applied mathematics---discussing modeling and computational techniques to solve three very different applied problems in the areas of robotics, automobile manufacturing, and signal processing, respectively. "The Computational Complexity of Motion Planning," by Jeffrey Hartline and Ran Libeskind-Hadas, makes a union that it not so frequently seen---that between real applications and theoretical computer science. It illustrates the class of PSPACE-complete problems, a set that is believed to be even harder than the well-known NP-complete problems, by an example in motion planning in robotics. The particular example is an interesting grid-based game. The paper is nicely self-contained, beginning with a brief review of key concepts from computational complexity including Turing machines and NP-completeness. It is suitable for either graduate students or advanced undergraduate students who have taken an introductory course in the theory of computation, and should provide welcome motivation for the students and a nice, self-contained module for the instructors. The second paper takes us into the very real world of clamps and welds in sheet metal assembly for automobiles and into the mathematical world of constrained optimization and statistical simulation. In "A Simple Model of Sheet Metal Assembly," Kathleen Hoffman and Fadil Santosa present a problem that serves as a nice case study of mathematical modeling in industry. It turns out that understanding the effect of the variations in both parts and assembly sequences on the variation in the final product is an important issue in manufacturing. The paper derives a sequence of fairly simple models for the assembly process and uses optimization techniques to help analyze these models. It then presents Monte Carlo simulations that allow one to determine the sequence of manufacturing steps that lead to the least total variation in the final product. This paper is excellently suited to mathematical modeling courses at the upper undergraduate level. Finally, the paper "Spread Spectrum from Two Perspectives," by Shlomo Engelberg, explores a powerful signal processing technique known as spread spectrum transmission. This technique is used to scramble messages in a way in which they can be unscrambled later. (The author mentions that the first patent on spread spectrum was taken out by the actress Hedy Lamarr and the composer George Antheil for a device that would allow a torpedo to be controlled by radio!) This technique is widely used today in wireless and cellular telephone communications---what could be more important to our modern lives? The paper presents two seemingly unrelated approaches to understanding the spread spectrum method. First, probabilistic ideas are used to develop some of the basic properties of spread spectrum transmission. Then recurrence relations are used to generate the pseudorandom sequences required by the method. The paper will form a nice module for courses in digital signal processing and related topics. Now if someone would like to design a group of robots that work on automotive assembly lines and talk on cell phones, they could blend together all three papers!