Persistence of first-year students in computer science is a problem. Although there are many factors that determine whether a student can be successful in computer science, research has shown that some attrition is due to personal perception of their ability in computer science. We created a learning experience specifically designed to improve students' self-assessment of their abilities. "Providing robotic experiences through objectbased programming" (PREOP) is a GUI programming environment based on Alice that students use to program autonomous robot controllers. Results show that for students that rate themselves as average programmers, the modules designed around PREOP positively affect the students' intimidation levels.

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Implementing a robot controller that can effectively manage limited resources in a deterministic, real-time manner is challenging. Behavior-based architectures that decompose autonomy into levels of intelligence are popular due to their robustness but do not provide real-time features that enforce timing constraints or support determinism. We propose an architecture and approach for using the real-time features of the Real-Time Specification for Java (RTSJ) in a behavior-based mobile robot controller to show that timing constraints affect performance. This is accomplished by extending a real-time aware architecture that explicitly enumerates timing requirements for each behavior. It is not enough to reduce latency. The usefulness of this approach is demonstrated via an implementation on Solaris 10 and the Sun Java Real-Time System (Java RTS). Experimental results are obtained using a K-team Koala robot performing path following with four composite behaviors. Experiments were conducted using several task period sets in three cases: real-time threads with the real-time garbage collector, real-time threads with the non- real-time garbage collector, and non-real-time threads with the non-real-time garbage collector. Results show that even if latency and determinism are improved, the timing of each individual behavior significantly affects task performance.

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