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This project was centered around designing a processor model (using the C programming language) based on the Coldfire computer architecture that will run on third party software known as Open Virtual Platforms. The end goal is to have a fully functional processor that can run Coldfire instructions and utilize peripheral

This project was centered around designing a processor model (using the C programming language) based on the Coldfire computer architecture that will run on third party software known as Open Virtual Platforms. The end goal is to have a fully functional processor that can run Coldfire instructions and utilize peripheral devices in the same way as the hardware used in the embedded systems lab at ASU. This project would cut down the substantial amount of time students spend commuting to the lab. Having the processor directly at their disposal would also encourage them to spend more time outside of class learning the hardware and familiarizing themselves with development on an embedded micro-controller. The model will be accurate, fast and reliable. These aspects will be achieved through rigorous unit testing and use of the OVP platform which provides instruction accurate simulations at hundreds of MIPS (million instructions per second) for the specified model. The end product was able to accurately simulate a subset of the Coldfire instructions at very high rates.
ContributorsDunning, David Connor (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12
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Description
Alongside the many challenges of Covid-19, the pandemic also disrupted the normal structure of education for students. As classes were being transferred to online formats, computer science students started learning more through constructivism principles rather than the traditionally taught, in-person lectures. This quantitative assessment hopes to determine whether constructivist principles

Alongside the many challenges of Covid-19, the pandemic also disrupted the normal structure of education for students. As classes were being transferred to online formats, computer science students started learning more through constructivism principles rather than the traditionally taught, in-person lectures. This quantitative assessment hopes to determine whether constructivist principles or traditional/visual cognition principles are better for teaching computer science topics. Determinations will be made through a social behavioral experiment teaching pointers to participants. Participants were split into three groups: a control group, a constructivist group, and a visual cognition group. Each group took part in an assessment testing their knowledge retention about pointers after having a lecture based around each teaching method. The assessment evaluated retries per assessment, time per correct answer, time per question, and the average time taken in total. The results of the experiment led to a conclusion that, according to the resulting data, constructivism teaching principles benefited participant scores, and visual cognition teaching principles worsened participant scores. However, a definitive answer of which teaching method is better for computer science could not be made due to insufficient sample size. When reflecting on the first iteration of this experiment, it is clear that future iterations of this experiment would benefit from a higher sample size, an easier assignment for the constructivist group, a feedback survey, and a longer period to experiment.
ContributorsTiruchinapalli, Sai Santosh (Author) / Burger, Kevin (Thesis director) / Hartwell, Leland (Committee member) / Barrett, The Honors College (Contributor)
Created2023-05