Matching Items (99)
166198-Thumbnail Image.png
Description
People with disabilities are underrepresented in the Science, Technology, Engineering, and Math (STEM) workforce (NSF, 2016). One way to increase representation of people with disabilities in STEM fields is by supporting students with disabilities (SWDs) at the undergraduate level. In undergraduate education in the United States, SWDs represent approximately 19%

People with disabilities are underrepresented in the Science, Technology, Engineering, and Math (STEM) workforce (NSF, 2016). One way to increase representation of people with disabilities in STEM fields is by supporting students with disabilities (SWDs) at the undergraduate level. In undergraduate education in the United States, SWDs represent approximately 19% of the undergraduate community (U.S. Census Bureau, 2021). However, SWDs have lower graduation and retention rates. This is particularly true for STEM majors, where SWDs make up about 9% of the STEM community in higher education. The AAC&U has defined a list of High-Impact Practices (HIPs), which are active learning practices and experiences that encourage deep learning by promoting student engagement, and could ultimately support student retention (AAC&U). To date, student-centered disability research has not explored the extent to which SWDs participate in HIPs. We hypothesized that SWDs are less likely than students without disabilities to be involved in HIPs and that students who identify as having severe disabilities would participate in HIPs at lower rates. In this study, we conducted a national survey to examine involvement in HIPs for students with disabilities in STEM. We found that disability status significantly affects the probability of participation in undergraduate research, but is not a significant factor for participation in most other HIPs. We also found that self-reported severity of disability did not significantly impact participation in HIPs, though we observed trends that students reporting higher severity generally reported lower participation in HIPs. Our open-ended responses did indicate that SWDs still faced barriers to participation in HIPs.
ContributorsPais, Danielle (Author) / Brownell, Sara (Thesis director) / Cooper, Katelyn (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Life Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2022-05
168749-Thumbnail Image.png
Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid

Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
165711-Thumbnail Image.png
Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the

The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
165130-Thumbnail Image.png
Description

There is increasing interest in understanding how active learning affects students’ mental health as science courses transition from traditional lecture to active learning. Prior research has found that active learning can both alleviate and exacerbate undergraduate mental health problems. Existing studies have only examined the relationship between active learning and

There is increasing interest in understanding how active learning affects students’ mental health as science courses transition from traditional lecture to active learning. Prior research has found that active learning can both alleviate and exacerbate undergraduate mental health problems. Existing studies have only examined the relationship between active learning and anxiety. No studies have examined the relationship between active learning and undergraduate depression. To address this gap in the literature, we conducted hour-long exploratory interviews with 29 students with depression who had taken active learning science courses across six U.S. institutions. We probed what aspects of active learning practices exacerbate or alleviate depressive symptoms and how students’ depression affects their experiences in active learning. We found that aspects of active learning practices exacerbate and alleviate students’ depressive symptoms, and depression negatively impacts students’ experiences in active learning. The underlying aspects of active learning practices that impact students’ depression fall into four overarching categories: inherently social, inherently engaging, opportunities to compare selves to others, and opportunities to validate or invalidate intelligence. We hope that by better understanding the experiences of undergraduates with depression in active learning courses we can create more inclusive learning environments for these students.

ContributorsAraghi, Tala (Author) / Cooper, Katelyn (Thesis director) / Brownell, Sara (Committee member) / Busch, Carly (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2022-05
165842-Thumbnail Image.png
Description
Mounting evidence suggests that gender biases favoring men and racial biases favoring whites and Asians contribute to the underrepresentation of women and underrepresented minorities (URM) in science, technology, engineering, and mathematics (STEM). Systemic issues caused by gender and racial biases create barriers that prevent women and URM from entering STEM

Mounting evidence suggests that gender biases favoring men and racial biases favoring whites and Asians contribute to the underrepresentation of women and underrepresented minorities (URM) in science, technology, engineering, and mathematics (STEM). Systemic issues caused by gender and racial biases create barriers that prevent women and URM from entering STEM from the structure of education to admission or promotions to higher-level positions. One of these barriers is unconscious biases that impact the quality of letters of recommendation for women and URM and their success in application processes to higher education. Though letters of recommendation provide a qualitative aspect to an application and can reveal the typical performance of the applicant, research has found that the unstructured nature of the traditional recommendation letter allows for gender and racial bias to impact the quality of letters of recommendation. Standardized letters of recommendation have been implemented in various fields and have been found to reduce the presence of bias in recommendation letters. This paper reviews the trends seen across the literature regarding equity in the use of letters of recommendation for undergraduates.
ContributorsKolath, Nina (Author) / Brownell, Sara (Thesis director) / Goodwin, Emma (Committee member) / Barrett, The Honors College (Contributor) / School of Criminology and Criminal Justice (Contributor) / School of Life Sciences (Contributor)
Created2022-05
Description

Mental health conditions can impact college students’ social and academic achievements. As such, students may disclose mental illnesses on medical school applications. Yet, no study has investigated to what extent disclosure of a mental health condition impacts medical school acceptance. We designed an audit study to address this gap. We

Mental health conditions can impact college students’ social and academic achievements. As such, students may disclose mental illnesses on medical school applications. Yet, no study has investigated to what extent disclosure of a mental health condition impacts medical school acceptance. We designed an audit study to address this gap. We surveyed 99 potential admissions committee members from at least 43 unique M.D.-granting schools in the U.S. Participants rated a fictitious portion of a medical school application on acceptability, competence, and likeability. They were randomly assigned to a condition: an application that explained a low semester GPA due to a mental health condition, an application that explained a low semester GPA due to a physical health condition, or an application that had a low semester GPA but did not describe any health condition. Using ANOVAs, multinomial regression, and open-coding, we found that committee members do not rate applications lower when a mental health condition is revealed. When asked about their concerns regarding the application, 27.0% of participants who received an application that revealed a mental health condition mentioned it as a concern; 14.7% of participants who received an application that revealed a physical health condition mentioned it as a concern. Committee members were also asked about when revealing a mental health condition would be beneficial and when it would be detrimental. This work indicates that medical school admissions committee members do not exhibit a bias towards mental health conditions and provides recommendations on how to discuss mental illness on medical school applications.

ContributorsAbraham, Anna (Author) / Brownell, Sara (Thesis director) / Cooper, Katelyn (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2022-05
168541-Thumbnail Image.png
Description
The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project

The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project is ASU VIPLE (Visual IoT/Robotics Programming Language Environment). It is a visual programming environment for Computer Science education. VIPLE supports a number of devices and platforms, including a traffic simulator developed using Unity game engine. This thesis focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithm in VIPLE. The traffic data is generated from the recorded real traffic data published at Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation based on dynamic changing traffic data.
ContributorsZhang, Zhemin (Author) / Chen, Yinong (Thesis advisor) / Wang, Yalin (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2022
168788-Thumbnail Image.png
Description
Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults

Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance. For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
193542-Thumbnail Image.png
Description
As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a

As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
193593-Thumbnail Image.png
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics.

In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024