Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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History in Action: Performing History as a Method of Teaching

Description
The purpose of this research was to create a theoretical lesson plan to teach the French Revolution, and specifically the March on Versailles, to secondary-level (middle and high school) students. This lesson plan incorporates a simulation of the March on Versailles for students to participate in as a supplement to…

The purpose of this research was to create a theoretical lesson plan to teach the French Revolution, and specifically the March on Versailles, to secondary-level (middle and high school) students. This lesson plan incorporates a simulation of the March on Versailles for students to participate in as a supplement to their usual lesson, and as a different and engaging method of learning. For the purposes of this honors thesis, the research and information gathered was split into four individual sections: a pedagogy, a historiography, a series of short biographies, and a script which is accompanied by a short film of the dialogue. These four parts would work together in order for an instructor to easily build either a simple, short, one-class lesson or a multi-lesson project for their students. The parts combine research into educational studies and research on French Revolutionary history in order to encompass all aspects of a lesson. The goal of such research into a potential lesson plan would be to create a history lesson which is more interesting to all students, especially those who struggle to find enjoyment in history. Moving forward, this theoretical lesson would be put into practice with middle or high school students in order to gauge their interest and engagement with the subject before and after a simulation in their class.
ContributorsNun, Taylor Jaylene (Author) / Thompson, Victoria (Thesis director) / Harris, Lauren (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Film, Dance and Theatre (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Characterizing the Performance of Machine Learning Algorithms: A Study and Novel Techniques

Description

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is…

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.
ContributorsLattus, Robert (Author) / Dasarathy, Gautam (Thesis director) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2021-12