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In the United States, Black women often face a number of disparities due to historical systems of oppression, social determinants of health and intersecting aspects related to gender and race (Lewis et al., 2016; Thomas et al., 2011; Spates et al., 2020). These factors may affect aspects of physical, mental and spiritual health, thus impacting overall quality of life and wellness outcomes. Wellness is defined as an integrated multidimensional construct (Myers & Sweeney, 2000). Tenets of the theory of intersectionality also apply an integrated framework addressing the unique contributions of intersected identities in the lives of Black women (Crenshaw, 1999). Many bodies of work outline the detrimental effects of systematic oppression and institutional racism on specific aspects of mental health, health and well-being of minoritized populations. However, there is little research focusing on the intersectional experiences of Black women in relation to gendered racism, race-related stress socioeconomic status (SES) and its impacts on total wellness factors. In this study, a non-experimental correlational research design was used with a standard multiple regression to explore relationships between gendered racism, race-related stress, SES and wellness scores amongst Black women. A total of 471women across the U.S. completed an online survey consisting of a demographic questionnaire and three measurements: The Gendered Racial Microaggression Scale for Black Women, Index of Race-Related Stress-Brief and the Five Factor Wellness Inventory. A standard multiple regression analysis indicated that more gendered racial microaggression on certain domains (Assumption of Beauty and Sexual Objectification, Silenced and Marginalized, Angry Black Woman) were associated with higher wellness scores, but other domains (Strong Black Woman) were not. Additionally, higher scores on race-related stress and the lowest SES status group were associated with lower overall wellness scores. Findings from this study highlight the need and importance of examining the intersections of race and gender and their impacts on the lived experiences, health and wellbeing of Black women. Recommendations for future research are provided along with implications for counseling practice and instruction.
This dissertation presents my research on algorithm/architecture co-design of deep spatial and temporal separable convolutional neural networks and their applications. I will introduce DeepDive as a framework for enabling and power-efficient execution of spatial deep learning models on embedded FPGA. For emerging Deep Separable Convolutional Neural Networks (DSCNNs), DeepDive is a fully-functional, vertical co-design framework for power-efficient implementation of DSCNNs on edge FPGAs. Agile Temporal Convolutional Network (ATCN) is also proposed for high-accurate fast classification and time series prediction in resource-constrained embedded systems. ATCN is primarily designed for mobile embedded systems with performance and memory constraints, such as wearable biomedical devices and real-time reliability monitoring systems. It uses the separable depth-wise convolution to reduce the computational complexity of the model and residual connections as time attention machines, to increase the network depth and accuracy. The result of this configurability makes the ATCN a family of compact networks with formalized hyper-parameters that allow the model architecture to be configurable and adjusted based on the application requirements. I also will present DeepTrack and DeepRACE, which are two other aspects of the application of DNN in vehicle trajectory prediction in highways and real-time reliability monitoring of transistors.
In this thesis, we hypothesize that data visualization users are subject to systematic errors, or cognitive biases, in decision-making under uncertainty. Based on research from psychology, behavioral economics, and cognitive science, we design five experiments to measure the role of anchoring bias, confirmation bias, and myopic loss aversion under different uncertain decision tasks like social media event detection, misinformation identification, and financial portfolio allocation. This thesis makes three major contributions. First, we find evidence of cognitive biases in data visualization through multiple behavioral trace data including user decisions, interactions logs (hovers, clicks), qualitative feedback, and belief elicitation techniques. Second, we design five digital experiments with interactive data visualization systems across different design complexities (coordinated multiple views to single plot) and data types (social network, linguistic, geospatial, temporal, statistical) and evaluate them on user populations that range from novice to expert (crowdsourced, undergraduate, data scientist, domain expert). Third, we evaluate the experiments using statistical, probabilistic, and machine learning techniques to measure the effects of cognitive biases with mixed effects modeling, hierarchical clustering, natural language processing, and Bayesian cognitive modeling. These experiments show the promising role data visualizations and human-computer techniques could remediate such biases and lead to better decision-making under uncertainty.
High performance, light weight alloys are need-of-the particularly in the defense, automotive and aerospace sectors. Magnesium is a prime candidate since it is the lightest structural metal having a mass density of 1.74g/cm3 and is the sixth most abundant element in the Earth’s crust. But, as a metal with a Hexagonal close packed (HCP) crystal structure, it exhibits poor formability, yield asymmetry, edge cracking in rolling, and low ductility at room temperature. Activation of non-basal slip planes, suppression of twinning, and promotion of recrystallization mechanisms leading to texture randomization have been a few key strategies incorporated during alloy design to achieve better properties.
In this study, the effect of thermo-mechanical processing i.e., confined rolling on three Mg-Al alloys namely AZ31B, Mg-6%Al and Mg-9%Al was analyzed. Extensive microstructural analysis using advanced electron microscopy techniques revealed the occurrence of either partial (in case of Mg-9%Al) or complete dynamic recrystallization which in turn refined the grain size, improved both strength and ductility under compression and randomized texture. Correlation of rolling temperature, strain rate, %Al content with texture, twinning, recrystallization kinetics, deformation mechanisms, precipitation kinetics and morphology have shed light on creating the best processing route for each of these alloys to achieve optimal microstructure and improved compressive mechanical behavior.
Nature walks have been demonstrated to increase cognitive and emotional well-being by restoring attention and increasing positive affect, both of which are linked to increases in reflective (“broadened") thinking. Broadened thinking is contrasted to the narrowing of thoughts associated with scarcity, the feeling of not having enough resources. This study proposed a model outlining the process by which broadened thinking occurs during nature walks while also incorporating scarcity. One hundred sixty-five college students reporting varying levels of scarcity took at 30-minute outdoor walk. Structural equation modeling demonstrated that the proposed model was a good fit for the data, supporting the hypothesized links between nature, restoration, positive affect, and broadened thinking. Although scarcity did not moderate relationships as expected, ANOVAs showed that participants experiencing the highest time scarcity saw the greatest increases in restoration and broadened thinking, providing some support for the hypothesis that those with more scarcity would derive greater benefit from nature walks. This study demonstrates the effectiveness of nature walks as an intervention, especially for students pressed for time, and highlights the importance of cultivating walk environments that are safe and accessible for all. Implications for future research and interventions at the individual and societal level are discussed.
The field of education relies heavily on instructional coaches to build teacher capacity in the implementation of evidence-based practices (EBPs) with fidelity. Although observation tools are used to measure the fidelity of implementation by teachers, less is reported about specific behaviors demonstrated by a coach. This two-part nonexperimental study used primary and secondary data. It sought to develop a valid and reliable Coaching Observation Tool, and used it to analyze 36 recorded real-time coaching sessions supporting the implementation of an EBP, Targeted Reading Intervention (TRI). The tool was developed using an iterative process of initial coach interview and systematic review of the literature, review of a sample of recorded coaching sessions with the initial draft of the tool, and focus group member checking interview with coaches. Next, the tool was used to analyze a sample of recorded TRI coaching sessions. The coaches in the present study provided coaching to teachers during year 2 of a TRI multi-site randomized controlled trial study. Although the tool was developed and used to identify the frequency with which discrete coaching behaviors were used, the current tool did not demonstrate validity and reliability. The findings suggest this tool could be helpful to identify coaching practices to support the implementation of EBP, such as TRI. Researchers using coaching to support the implementation of EBP alone, or as a component within PD, will find this tool provides them a clearer understanding of the instructional coach in building teacher capacity with the fidelity of implementation of the EBP.
As a result of various academic, behavioral, and social-emotional challenges that adolescents may experience during high school, an alarming rate of students are not acquiring their high school credentials. To address this concern, researchers have suggested dropout prevention efforts should focus on using a comprehensive, preventative, tiered framework such as Schoolwide Positive Behavior Interventions and Supports to target alterable classroom-level variables such as student behavior, student attendance, academic performance, and student engagement. One of the most efficient and effective methods for improving academic engagement and student behavior is through the implementation of evidence-based classroom management practices, such as increasing students’ opportunities to respond (OTRs) during teacher-directed instruction. Unfortunately, many teachers lack adequate amounts of training in these practices. This study investigated the effects of multilevel professional development (PD) and coaching support provided by a school-based coach on high school teachers’ use of a trained classroom management skill (i.e., OTRs) during teacher-directed instruction in a single-case, multiple baseline design across two teacher participants. Overall results showed teachers improved implementation fidelity but failed to achieve the required rates of OTRs. Additionally, when teachers improved implementation fidelity, students also demonstrated increases in active academic engagement. Social validity data indicated teachers and the school’s instructional coach rated the multilevel PD and coaching framework to be moderately effective in supporting teachers’ implementation of high rates of OTRs. Student participants reported observed increases in teachers’ use of a variety of OTRs, positive feelings associated with actively participating in class when presented with increased OTRs, and a better understanding or retention of course content when teachers used high rates of OTRs. Limitations of the study, implications for practice, and suggestions for future research are discussed.
Freeform optics have bridged the gap from theoretical to practical application and is propelled by ultra-precision multi-axis machining. Freeform optics have been used for infrared sensors, vision correction, and beam shaping. Manufacturing and application of dynamic freeform optics, where relative motion of freeform surfaces can enable improved or new functionality of an optical system, is a next step. The first part of this work concentrates on evaluating various manufacturing paths for glass transmissive dynamic freeform optics. Leveraging an iterative process design and metrology techniques, a method for the generation of high-quality optics for production is established. Metrology evaluations led to development of a six degree of freedom surface analysis that utilizes simulated annealing for optimization. Major results from the precision glass molding indicate high-volume production of transmissive glass freeform optics is possible. The second part of this work details research in the manufacturing of two separate dynamic freeform optics and optomechanics. For prototyping of visibly transmissive dynamic freeforms, a shift was machined into the optical surfaces. These dynamic systems allow for novel light management and improved depth of field in high-magnification systems. All of these aforementioned freeform processes clarify the methods for future manufacturing of freeform optics and associated optomechanics.
Learning Analytics (LA) has had a growing interest by academics, researchers, and administrators motivated by the use of data to identify and intervene with students at risk of underperformance or discontinuation. Typically, faculty leadership and advisors use data sources hosted on different institutional databases to advise their students for better performance in their academic life. Although academic advising has been critical for the learning process and the success of students, it is one of the most overlooked aspects of academic support systems. Most LA systems provide technical support to academic advisors with descriptive statistics and aggregate analytics about students' groups. Therefore, one of the demanding tasks in academic support systems is facilitating the advisors' awareness and sensemaking of students at the individual level. This enables them to make rational, informed decisions and advise their students. To facilitate the advisors' sensemaking of individual students, large volumes of student data need to be presented effectively and efficiently.
Effective presentation of data and analytic results for sensemaking and decision-making has been a major issue when dealing with large volumes of data in LA. Typically, the students' data is presented in dashboard interfaces using various kinds of visualizations like scientific charts and graphs. From a human-centered computing perspective, the user’s interpretation of such visualizations is a critical challenge to design for, with empirical evidence already showing that ‘usable’ visualizations are not necessarily effective and efficient from a learning perspective. Since an advisor's interpretation of the visualized data is fundamentally the construction of a narrative about student progress, this dissertation draws on the growing body of work in LA sensemaking, data storytelling, creative storytelling, and explainable artificial intelligence as the inspiration for the development of FIRST, Finding Interesting stoRies about STudents, that supports advisors in understanding the context of each student when making recommendations in an advising session. FIRST is an intelligible interactive interface built to promote the advisors' sensemaking of students' data at the individual level. It combines interactive storytelling and aggregate analytics of student data. It presents the student's data through natural language stories that are automatically generated and updated in coordination with the results of the aggregate analytics. In contrast to many LA systems designed to support student awareness of their performance or to support teachers in understanding the students' performance in their courses, FIRST is designed to support advisors and higher education leadership in making sense of students' success and risk in their degree programs. The approach to interactive sensemaking has five main stages: (i) Student temporal data Model, (ii) Domain experts’ questions and queries, (iii) Student data reasoning, (iv) Student storytelling model, and (v) Domain experts’ reflection. The student storytelling stage is the main component of the sensemaking model and it composes four tasks: (i) Data sources, (ii) Story synthesis, (iii) Story analysis, and (iv) User interaction.
The contributions of this study are: i) A novel student storytelling model to facilitate the sensemaking of complex, diverse, and heterogeneous student data, ii) An anomaly detection model to enrich student stories with interesting, yet, insightful information for the domain experts and iii) An explainable and interpretable interactive LA model to inspire advisors' trust and confidence with the student stories. This study reports on four ethnographic studies to show the potential of the proposed LA sensemaking model and how it affects the advisor's sensemaking of students at the individual level. The user studies considered for this dissertation were focus group discussions, in-depth interviews, and diary study- in-situ and snippet technique. These studies investigate if FIRST can improve and facilitate the advisor's sensemaking of students’ success or risk by presenting individual student's heterogeneous data as a complete and comprehensive story.
