Matching Items (29)
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- All Subjects: Education
- All Subjects: deep learning
- Creators: School of Mathematical and Statistical Sciences
- Member of: Theses and Dissertations
Description
Previous research discusses students' difficulties in grasping an operational understanding of covariational reasoning. In this study, I interviewed four undergraduate students in calculus and pre-calculus classes to determine their ways of thinking when working on an animated covariation problem. With previous studies in mind and with the use of technology, I devised an interview method, which I structured using multiple phases of pre-planned support. With these interviews, I gathered information about two main aspects about students' thinking: how students think when attempting to reason covariationally and which of the identified ways of thinking are most propitious for the development of an understanding of covariational reasoning. I will discuss how, based on interview data, one of the five identified ways of thinking about covariational reasoning is highly propitious, while the other four are somewhat less propitious.
ContributorsWhitmire, Benjamin James (Author) / Thompson, Patrick (Thesis director) / Musgrave, Stacy (Committee member) / Moore, Kevin C. (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2014-05
Description
Computer simulations are gaining recognition as educational tools, but in general there is still a line dividing a simulation from a game. Yet as many recent and successful video games heavily involve simulations (SimCity comes to mind), there is not only the growing question of whether games can be used for educational purposes, but also of how a game might qualify as educational. Endemic: The Agent is a project that tries to bridge the gap between educational simulations and educational games. This paper outlines the creation of the project and the characteristics that make it an educational tool, a simulation, and a game.
ContributorsFish, Derek Austin (Author) / Karr, Timothy (Thesis director) / Marcus, Andrew (Committee member) / Jones, Donald (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2013-05
DescriptionThis project examines the television industry today, especially the field of educational programs. It includes the detailed implementation of one such show, a 30-minute demonstration of life skills, split into 3 segments. The pilot episode is also included.
ContributorsKesting, Amanda Jean (Author) / Alvarez, Melanie (Thesis director) / Snyder, Brian (Committee member) / Glaser, Ann (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2013-05
Description
As we count down the years remaining before a global climate catastrophe, ever increases the importance of teaching environmental history and fostering environmental stewardship from a young age. In the age of globalization, nothing exists in a vacuum, yet our traditional education system often fails to reflect the abundant connections between content areas that are prevalent outside of schools. In fact, many of the flaws of the field of education have been exacerbated by the COVID-19 pandemic and a forced transition to online schooling, with many educators reverting to outdated practices in a desperate attempt to get students through the year. The aim of this project was to design a unit curriculum with these issues in mind. This month-long environmental history unit engages students through the use of hands-on activities and promotes interdisciplinary connections. The unit can be taught in a physical, online, or hybrid American history class, and will hopefully inspire and motivate students to become environmental stewards as they look toward their futures on this planet.
ContributorsColeman, Lauren Jean (Author) / Walters, Molina (Thesis director) / Anthony, Charles (Committee member) / School of International Letters and Cultures (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Division of Teacher Preparation (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This study estimates the effect of district wealth on Arizona Empowerment Scholarship Account program participation using data from the Arizona Department of Education. We find that students from poor districts are not more likely to participate as school performance decreases.Conversely, those from wealthy districts do increase participation as school performance decreases. We briefly try to explain the observed heterogeneity through survey results and commenting on the program design.
ContributorsAngel, Joseph Michael (Author) / Kostol, Andreas (Thesis director) / Kuminoff, Nicolai (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Accurate pose initialization and pose estimation are crucial requirements in on-orbit space assembly and various other autonomous on-orbit tasks. However, pose initialization and pose estimation are much more difficult to do accurately and consistently in space. This is primarily due to not only the variable lighting conditions present in space, but also the power requirements mandated by space-flyable hardware. This thesis investigates leveraging a deep learning approach for monocular one-shot pose initialization and pose estimation. A convolutional neural network was used to estimate the 6D pose of an assembly truss object. This network was trained by utilizing synthetic imagery generated from a simulation testbed. Furthermore, techniques to quantify model uncertainty of the deep learning model were investigated and applied in the task of in-space pose estimation and pose initialization. The feasibility of this approach on low-power computational platforms was also tested. The results demonstrate that accurate pose initialization and pose estimation can be conducted using a convolutional neural network. In addition, the results show that the model uncertainty can be obtained from the network. Lastly, the use of deep learning for pose initialization and pose estimation in addition with uncertainty quantification was demonstrated to be feasible on low-power compute platforms.
ContributorsKailas, Siva Maneparambil (Author) / Ben Amor, Heni (Thesis director) / Detry, Renaud (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In shotgun proteomics, liquid chromatography coupled to tandem mass spectrometry
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
ContributorsWhyte, Cameron Stafford (Author) / Suren, Jayasuriya (Thesis director) / Gil, Speyer (Committee member) / Patrick, Pirrotte (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Schools across the United States have been subject to a rise in violent incidents since 2013. Reading about school shootings, racist acts, and violent demonstrations in schools has unfortunately become commonplace, which is contributing to inequitable outcomes for some student populations. These equity gaps have triggered demands for more equitable solutions in schools, a responsibility that falls on the shoulders of stakeholders like school governing boards, principals, and parents.
Chandler Unified School District (CUSD), a large school system in Arizona that serves 45,000 students from preschool through high school, has been unable to escape similar structural and frictional inequities within its schools. One instance of a racially charged student performance at Santan Middle School motivated CUSD to take a more immediate look at equity in the district. It is during this response that our team of New Venture Group consultants engaged with Matt Strom, Assistant Superintendent of CUSD, in analyzing the important question of “how CUSD can take steps towards closing equity gaps within the district?”
CUSD defines an equity gap as any difference in student opportunity, achievement, discipline, attendance, etc. contributable to a student’s ethnicity, gender, or socioeconomic status. Currently, certain student populations in CUSD perform vastly different academically and receive different opportunities within schools, but as was our problem statement, CUSD is aiming to reduce (and eventually close) these gaps.
Our team approached this problem in three phases: (1) diagnosis, (2) solution creation, and (3) prevention. In phase one, we created a dashboard to help principals easily and visually identify gaps by toggling parameters on the dashboard. Phase two focused on the generation of recommendations for closing gaps. To achieve this goal, a knowledge of successful gap-closing strategies will be paired with the dashboard. In our final phase, the team of consultants created a principal scorecard to ensure equity remains a priority for principals.
Chandler Unified School District (CUSD), a large school system in Arizona that serves 45,000 students from preschool through high school, has been unable to escape similar structural and frictional inequities within its schools. One instance of a racially charged student performance at Santan Middle School motivated CUSD to take a more immediate look at equity in the district. It is during this response that our team of New Venture Group consultants engaged with Matt Strom, Assistant Superintendent of CUSD, in analyzing the important question of “how CUSD can take steps towards closing equity gaps within the district?”
CUSD defines an equity gap as any difference in student opportunity, achievement, discipline, attendance, etc. contributable to a student’s ethnicity, gender, or socioeconomic status. Currently, certain student populations in CUSD perform vastly different academically and receive different opportunities within schools, but as was our problem statement, CUSD is aiming to reduce (and eventually close) these gaps.
Our team approached this problem in three phases: (1) diagnosis, (2) solution creation, and (3) prevention. In phase one, we created a dashboard to help principals easily and visually identify gaps by toggling parameters on the dashboard. Phase two focused on the generation of recommendations for closing gaps. To achieve this goal, a knowledge of successful gap-closing strategies will be paired with the dashboard. In our final phase, the team of consultants created a principal scorecard to ensure equity remains a priority for principals.
ContributorsFerrara, Justin Christopher (Co-author) / Lee, Cynthia (Co-author) / Weston, Joshua (Co-author) / Licon, Wendell (Thesis director) / Strom, Matthew (Committee member) / Department of Economics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Convolutional neural networks boast a myriad of applications in artificial intelligence, but one of the most common uses for such networks is image extraction. The ability of convolutional layers to extract and combine data features for the purpose of image analysis can be leveraged for pose estimation on an object - detecting the presence and attitude of corners and edges allows a convolutional neural network to identify how an object is positioned. This task can assist in working to grasp an object correctly in robotics applications, or to track an object more accurately in 3D space. However, the effectiveness of pose estimation may change based on properties of the object; the pose of a complex object, complexity being determined by internal occlusions, similar faces, etcetera, can be difficult to resolve.
This thesis is part of a collaboration between ASU’s Interactive Robotics Laboratory and NASA’s Jet Propulsion Laboratory. In this thesis, the training pipeline from Sharma’s paper “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks” was modified to perform pose estimation on a complex object - specifically, a segment of a hollow truss. After initial attempts to replicate the architecture used in the paper and train solely on synthetic images, a combination of synthetic dataset generation and transfer learning on an ImageNet-pretrained AlexNet model was implemented to mitigate the difficulty of gathering large amounts of real-world data. Experimentation with pose estimation accuracy and hyperparameters of the model resulted in gradual test accuracy improvement, and future work is suggested to improve pose estimation for complex objects with some form of rotational symmetry.
This thesis is part of a collaboration between ASU’s Interactive Robotics Laboratory and NASA’s Jet Propulsion Laboratory. In this thesis, the training pipeline from Sharma’s paper “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks” was modified to perform pose estimation on a complex object - specifically, a segment of a hollow truss. After initial attempts to replicate the architecture used in the paper and train solely on synthetic images, a combination of synthetic dataset generation and transfer learning on an ImageNet-pretrained AlexNet model was implemented to mitigate the difficulty of gathering large amounts of real-world data. Experimentation with pose estimation accuracy and hyperparameters of the model resulted in gradual test accuracy improvement, and future work is suggested to improve pose estimation for complex objects with some form of rotational symmetry.
ContributorsDsouza, Susanna Roshini (Author) / Ben Amor, Hani (Thesis director) / Maneparambil, Kailasnath (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
YouTube video bots have been constantly generating bot videos and posting them on the YouTube platform. While these bot-generated videos negatively influence the YouTube audience, they cost YouTube extra resources to host. The goal for this project is to build a classifier that identifies bot-generated channels based on a deep learning-based framework. We designed the framework to take text, audio, and video features into account. For the purpose of this thesis project, we will be focusing on text classification work.
ContributorsSai, Lun (Author) / Benjamin, Victor (Thesis director) / Lin, Elva S.Y. (Committee member) / Department of Information Systems (Contributor, Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05