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Computational thinking, the creative thought process behind algorithmic design and programming, is a crucial introductory skill for both computer scientists and the population in general. In this thesis I perform an investigation into introductory computer science education in the United States and find that computational thinking is not effectively taught

Computational thinking, the creative thought process behind algorithmic design and programming, is a crucial introductory skill for both computer scientists and the population in general. In this thesis I perform an investigation into introductory computer science education in the United States and find that computational thinking is not effectively taught at either the high school or the college level. To remedy this, I present a new educational system intended to teach computational thinking called Genost. Genost consists of a software tool and a curriculum based on teaching computational thinking through fundamental programming structures and algorithm design. Genost's software design is informed by a review of eight major computer science educational software systems. Genost's curriculum is informed by a review of major literature on computational thinking. In two educational tests of Genost utilizing both college and high school students, Genost was shown to significantly increase computational thinking ability with a large effect size.
ContributorsWalliman, Garret (Author) / Atkinson, Robert (Thesis advisor) / Chen, Yinong (Thesis advisor) / Lee, Yann-Hang (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Most embedded applications are constructed with multiple threads to handle concurrent events. For optimization and debugging of the programs, dynamic program analysis is widely used to collect execution information while the program is running. Unfortunately, the non-deterministic behavior of multithreaded embedded software makes the dynamic analysis difficult. In addition, instrumentation

Most embedded applications are constructed with multiple threads to handle concurrent events. For optimization and debugging of the programs, dynamic program analysis is widely used to collect execution information while the program is running. Unfortunately, the non-deterministic behavior of multithreaded embedded software makes the dynamic analysis difficult. In addition, instrumentation overhead for gathering execution information may change the execution of a program, and lead to distorted analysis results, i.e., probe effect. This thesis presents a framework that tackles the non-determinism and probe effect incurred in dynamic analysis of embedded software. The thesis largely consists of three parts. First of all, we discusses a deterministic replay framework to provide reproducible execution. Once a program execution is recorded, software instrumentation can be safely applied during replay without probe effect. Second, a discussion of probe effect is presented and a simulation-based analysis is proposed to detect execution changes of a program caused by instrumentation overhead. The simulation-based analysis examines if the recording instrumentation changes the original program execution. Lastly, the thesis discusses data race detection algorithms that help to remove data races for correctness of the replay and the simulation-based analysis. The focus is to make the detection efficient for C/C++ programs, and to increase scalability of the detection on multi-core machines.
ContributorsSong, Young Wn (Author) / Lee, Yann-Hang (Thesis advisor) / Shrivastava, Aviral (Committee member) / Fainekos, Georgios (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2015