This thesis explores the potential for software to act as an educational experience for engineers who are learning system dynamics and controls. The specific focus is a spring-mass-damper system. First, a brief introduction of the spring-mass-damper system is given, followed by a review of the background and prior work concerning this topic. Then, the methodology and main approaches of the system are explained, as well as a more technical overview of the program. Lastly, a conclusion and discussion of potential future work is covered. The project was found to be useful by several engineers who tested it. While there is still plenty of functionality to add, it is a promising first attempt at teaching engineers through software development.
The goal of this thesis project was to develop a digital, quantitative assessment of executive functioning skills and problem solving abilities. This assessment was intended to serve as a relative measure of executive functions and problem solving abilities rather than a diagnosis; the main purpose was to identify areas for improvement and provide individuals with an understanding of their current ability levels. To achieve this goal, we developed a web-based assessment through Unity that used gamelike modifications of Flanker, Antisaccade, Embedded Images, Raven’s Matrices, and Color / Order Memory tasks. Participants were invited to access the assessment at www.ExecutiveFunctionLevel.com to complete the assessment and their results were analyzed. The findings of this project indicate that these tasks accurately represent executive functioning skills, the Flanker Effect is present in the collected data, and there is a notable correlation between each of the REFLEX challenges. In conclusion, we successfully developed a short, gamelike, online assessment of executive functioning and problem solving abilities. Future developments of REFLEX could look into immediate scoring, developing a mobile application, and externally validating the results.
Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating environments, using photogrammetry, or other means. Geological formations and the surrounding terrain can be created using noise based algorithms such as Perlin noise. However, interpreting noise in this manner has a number of challenges due to the pseudo-random nature of noise. We will discuss how to generate noise, how to render noise, and the challenges in interpreting noise.