-
Prospective Students
-
Faculty and Staff
-
Current Students
-
Alumni and Friends
Family engagement with schools has been shown to be a predictor of student success (Powell et at., 2010) and federal statute supports school/ family relationships through the Family Engagement in Education Act. For students with disabilities (SWD), family engagement may be even more critical. Unfortunately, data has suggested that family engagement may be limited due to barriers families of SWD may face (Van Haren & Fiedler, 2008). Teacher invitation, teacher beliefs about family involvement and quality of communication are factors related to family engagement. The purpose of this study was to investigate an in-service teacher's use of a step-by-step strategy during family/ teacher conferences to increase family engagement during the conference, improve quality teacher communication and positively impact teacher beliefs on family involvement. The step-by-step conferencing strategy was called PIQUE and was developed through a review of prior research and feedback from experts in the field. This case study used both quantitative and qualitative methods to determine the effectiveness of the PIQUE strategy. Within an AB single-case design, I noted an increase in the 5-second intervals of the family speaking during the conference from baseline to post-intervention phase. This increase was immediate and demonstrated an accelerating trend. The teacher and parent completed surveys and interviews, which were analyzed thematically alongside descriptive and inferential field notes recorded by the researcher. Through this analysis, two primary themes were identified as Misunderstanding Communication as Equal to Engagement and Bias as a Barrier to Engagement. A secondary theme of Lack of Confidence When Engaging with Families was also identified. Triangulation was achieved across quantitative and qualitative data sources. Conclusions point to an increase in equity of power during conferences and positive change in teacher beliefs about family involvement and engagement after the implementation of the intervention. A conclusion that PIQUE implementation led to these changes should be interpreted with caution due to the threats to internal and external validity of case studies. The study concluded with implications for practice, limitations and suggestions for future research.
Over the past two decades, the Internet of Things (IoT) has seen a significant expansion in both the sophistication and variety of its applications. These applications span several domains, including enhancing and automating services in healthcare, advancing smart manufacturing processes, and elevating home living standards through smart home technologies. These technologies empower individuals with greater control over their home appliances. Smart locks are smart home devices that were introduced as replacements for traditional locks. Smart locks, designed to go beyond the basic functionality of traditional locks by offering additional features, have seen a surge in market growth and competitiveness. According to the Statista Research Department, it is projected that the global market for smart locks will surpass four billion dollars by 2027.
A number of studies have examined end users' concerns, needs, and expectations regarding smart homes in general. However, little research has been conducted to examine these aspects of the smart lock in particular. To address this gap, we conducted a series of user studies that aim to elucidate how smart locks are integrated and interact within smart home environments, focusing on user interactions both with the locks themselves and when they are part of broader automation scenarios. This dissertation contributes to a deeper understanding of smart lock technology from a user-centric viewpoint. It offers insights into user motivations, concerns, and preferences regarding smart lock usage and automation. It also highlights the importance of balancing convenience and security, the pivotal role of trust, and the complexities of integrating smart locks into broader smart home systems.
Social studies education has garnered significant national attention as state governments throughout the country have waged an intentional, political attack against the teaching of Critical Race Theory (CRT) and “divisive concepts” in K-12 public schools. Even though CRT is often conflated with diversity, equity, and inclusion (DEI) initiatives and not actually taught at the elementary or secondary level, since January 2021, over one hundred anti-CRT (or divisive concepts) bills have been introduced in more than thirty different state legislatures throughout the country that would prohibit educators from teaching about concepts rooted in race. For Black women teachers, these legislative restrictions create a teaching context that pressures them to divert from the historical work of their predecessors and go against the grain of Black female identity. As such, this phenomenological study explored how Black female social studies teachers teach about race, racism, and oppression given today’s hostile sociopolitical climate.
The engine of modern society is fueled by information, and the desire to obtain, process and relay it ever more quickly is motivation for scientists to dig deeper into pathways that enable this endgame. The implementation of ever-quicker computer processors, optical fiber-based communications, and Light Radar (LiDar) for climate studies are a small subset that illustrate how ubiquitous the applications of optics are. In this context, the study of 2D materials (2DMs) is important due to the fascinating properties they exhibit that could lead to a plethora of future opto-electronic applications that extend beyond what silicon alone can provide. The story began with graphene due to its high conductivity and tensile strength, but due to the difficulty of switching its conductivity, applications in transistors is limited, and other materials such as the transition metal dichalcogenides (TMDs) MoS2 and WS2, which exhibit a bandgap transition from indirect to direct when going from bulk to monolayer, are being explored. The wide bandgap semiconductor hexagonal boron nitride (hBN) has also been piquing interest. The presence of room-temperature stable excitons detected via various spectroscopies suggests applicability in mainstream field-effect transistors, and current industry direction towards so-called ‘nanosheet’ and ‘nano-wire’ channel transistors serve as prime examples of the relevant applicability of such 2D materials. Quantum computing and valley-tronic applications have also been reported [5], making this class of material exciting to study.
When material dimensions are reduced to the single atomic layer (‘monolayer’) limit, fast carrier dynamics become important that can only be investigated by even faster phenomena i.e., femtosecond ‘ultrafast’ laser pulses. When exposed to intense electric fields, several processes can occur; multiphoton absorption (MPA) which utilizes multiple photons to promote a single charge carrier to the conduction band (CB), tunneling ionization (TI) in which the laser field modifies the inter-atomic potential and allows CB access via tunneling, and avalanche ionization (AI) where inter-carrier impact causes ionization. Together, these strong-field ionization (SFI) processes are subject to significant research effort. If SFI-induced excited carrier populations exceed a threshold, damage occurs via a non-thermal ‘ablation’ process typically used for cutting and patterning.
The objective of this work was to explore the ultrafast optical dielectric breakdown (ODB) behavior of 2DMs such as MoS2, WS2, and hBN. The work involves an investigation of the etalon interference effect that causes differences in the ablation threshold fluence for the same material when placed on different substrates, differences in threshold fluence between different 2DMs, as well as an exploration of laser-induced defects added when multiple ultrafast pulses are incident on the material. ODB for the wide bandgap insulator hBN is also demonstrated and characterized using various imaging modalities and spectroscopies for the first time. Through the findings presented in this work, we begin to unravel some aspects of the nature of ablation, particularly the dominance of avalanche ionization as the key carrier generation mechanism in the ODB process in 2D materials. We also establish femtosecond laser direct writing as a useful tool for the nanopatterning of such 2DMs.
Fifty percent of African American men with learning disabilities will not persist past their first year of college (Newman et al., 2011). A bachelor’s degree for an African American man means he is five times less likely to be incarcerated than his peers with a high school diploma and will make approximately $32,000 more per year on average than his counterparts without a bachelor’s degree (Trostel, 2015). Frequently neglected and inadequately represented in the existing literature on learning disabilities are the experiences of African American men with learning disabilities in higher education. The purpose of this phenomenological multi-case study was to examine the postsecondary educational experiences of African American men with learning disabilities by exploring the perspectives of both parents and students.
Ten semi-structured interviews were conducted; Six parent interviews and four student interviews. The study answered the following research questions (1) What are the psychosocial experiences of parents of African American young men with learning disabilities at the postsecondary level? (2) What are the primary roles of parents of African American young men with learning disabilities at the postsecondary level? (3) What do parents perceive about the intersecting identities of disability, race, and gender on the social and academic experiences of their African American young man with learning disabilities at the postsecondary level? (4) What are the psychosocial experiences of African American men with learning disabilities attending a Postsecondary Institution? (5) What are the experiences of African American men with learning disabilities attending a Postsecondary Institution regarding social and academic supports?
Based on the data analysis, three parent themes and two student themes emerged respectively: (1) Bubble Wrap Parenting, (2) The Changing of the Guard, and (3) In the Intersection of Black and Disabled; (1) Right in the Middle of the Dichotomy, and (2) The Juggling Act. The findings underscore that when Black men with learning disabilities receive services that segregate them from their peers, they face a forced choice between preserving their identity and accessing necessary support. One recommendation arising from these findings is to make support services universally available. This entails granting all students access to supports such as assistive technology and note-taking apps that have traditionally been exclusively available for the disabled population. By doing so, any stigma surrounding segregated support would be eliminated.
Understanding and characterizing cancer patient outcomes is challenging and involves multiple clinical measurements (e.g., imaging and genomics biomarkers). Enabling multimodal analytics promises to reveal novel predictive patterns that are not available from singular data input. In particular, exploring histopathological and genomics sequencing data allows us to provide a path for us to understand the insights of cancer biology. In this dissertation, we first present a graph-based neural network (GNN) framework that allows multi-region spatial connection of tiles to predict molecular profile status in colorectal cancer. We demonstrate the validity of spatial connections of tumor tiles built upon the geometric coordinates derived from the raw histopathological images. These findings capture the interaction between histopathological characteristics and a panel of molecular profiles of treatment relevance. Second, we propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colorectal cancer survival prediction. The proposed unsupervised pretraining captures the intrinsic interaction between tissue microenvironments in WSI and a wide range of genomics data (e.g., miRNA-sequence, copy number variant, and methylation). After the multimodal knowledge aggregation in pretraining, the task-specific model finetuning expands the scope of data utility applicable to both multi- and single-modal data. Finally, we introduce a contrastive pathology-and-genomics pretraining to enhance patient survival prediction by extracting the multimodal interaction for each patient while distinguishing the differences among various patients. Together, the above methods provide an array of solutions for addressing the challenges in multimodal disease data understanding, leading to improved overall performance of patient outcome prediction in colorectal cancer.
Researchers have identified that inequitable learning experiences for African American students have negatively impacted their educational outcomes in the United States, and culturally sustaining practices offer great promises in supporting African American students. This meta-analysis investigated the effectiveness of culturally sustaining practices on African American students’ academic and behavioral outcomes. This study built on prior attempts to synthesize multiple definitions of culturally sustaining practices with recommendations from the literature aimed directly at African American students. In this dissertation, I first used the existing synthesis to establish a theoretical framework with an operational definition of culturally sustaining practices for African American students (CSPAAS). I then conducted a systematic review to identify group design studies aligned with the components of the CSPAAS framework. Effect sizes were extracted from each individual study and a random effects model was employed to determine the overall effectiveness of CSPAAS interventions. Additionally, I evaluated the included studies for methodological rigor using the Council for Exceptional Children (CEC, 2014, 2023) quality indicators to determine the extent to which CSPAAS interventions could be identified as evidence-based practices. Results revealed CSPAAS academic interventions were highly effective (n = 17; g = 1.01) and CSPAAS behavioral interventions were moderately effective (n = 5; g = 0.5). The CSPAAS practices for both academic and behavioral interventions also met CEC (2014, 2023) criteria to be categorized as evidence-based practices. Implications for future research are discussed.
In recent years, the global energy sector has been undergoing a significant transformation, characterized by an increasing shift towards data-driven operations and the widespread adoption of renewable energy such as solar photovoltaics (PV). This transition is largely motivated by the urgent need to address climate change and the realization of the potential that large-scale data collection and analysis hold for enhancing energy efficiency and sustainability. As the energy landscape becomes more complex and interconnected, the role of sophisticated energy forecasting techniques has grown in importance. These techniques are crucial for managing the variability and uncertainty inherent in renewable energy sources, such as wind and solar power, which are subject to fluctuations in weather and environmental conditions. Moreover, the integration of big data analytics into energy systems facilitates more accurate and timely predictions, thereby enabling more effective planning, operation, and maintenance of energy infrastructure. This dissertation introduces a novel, data-driven methodologies to address key challenges in energy forecasting: predicting weather-induced power outages, net load forecasting, and accurately estimating solar PV penetration.
In the first part of the study, a methodology to forecast weather-related power distribution outages one day ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modeling challenge for small and rural electric utilities. The weights for outage and non-outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather-related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.
The dissertation then explores net load forecasting in the context of increasing behind-the-meter (BTM) solar PV system adoption. This adoption introduces complexities to grid management, especially concerning net load-the difference between demand and PV generation. The intermittent nature of PV generation, influenced by weather and time, adds to net load volatility, posing challenges to grid reliability. This dissertation presents a review of state-of-the-art net load forecasting with a focus on forecasting approaches, techniques, explanatory variables, and the impact of PV penetration on net load forecasting. Additionally, the study conducts a critical analysis of existing literature to identify gaps in the field of net load forecasting and PV integration. To address some of these gaps, a benchmark net load forecasting model is proposed. The proposed model uses publicly available data from ISO New England. Through the case study, it is demonstrated that the proposed net load forecasting model outperforms the current benchmark load forecast model significantly in terms of forecasting accuracy, as measured by Mean Absolute Percentage Error. Moreover, the case study also demonstrates the effectiveness of the proposed model over a range of PV penetration, which is an important consideration as the use of solar energy continues to grow.
Furthermore, the dissertation addresses two critical questions regarding PV integration: (1) How much PV is there in the system?; (2) Which meters have BTM PV? To address the challenge of estimating PV penetration in systems, existing supervised and unsupervised methods are reviewed, which reveal common limitations, especially when PV installation information is limited or completely unavailable. To overcome these challenges, a regression-based approach is developed by leveraging the difference in performance in the benchmark load and net load forecasting models in forecasting net load. The proposed framework is deployed for real-world data from an ISO and a medium-sized in the United States. The results validate the effectiveness of the proposed method in accurately estimating PV penetration levels, even without explicit PV installation data, using only historical load data.
The final part of the study focuses on identifying meters with BTM PV installations. Again by, leveraging the performance disparities between load forecasting models and net load forecasting models, a methodology is devised to differentiate meters with and without PV installations. The effectiveness of the proposed frameworks is confirmed using an empirical case study at a medium-sized US utility with meter-level load data meters. The results illustrate that accurate identification of meters with PV installations was achieved while maintaining a low rate of false identifications. This methodology provides valuable insights for utilities, empowering them to comprehend the adoption and impact of distributed solar energy within their service territories.
Overall, this study contributes significantly to the field of energy system forecasting by developing data-driven models that enhance the understanding and management of weather-induced outages, net load variability, and solar PV integration. These advancements enable utilities to make informed decisions for grid planning, capacity management, and service customization, paving the way for more resilient and efficient energy systems.
Recurrent events are commonly encountered in medical and epidemiological studies. It is often of interest what and how risk factors influence the occurrence of events. While much research on recurrent events has addressed both time-independent and time-dependent effects, there is a possibility that these effects also vary with certain covariates.
In this dissertation, we develop novel estimation and inference procedures for two intensity models for recurrent event data—a class of semiparametric models and a nonparametric frailty model. Both models allow for the simultaneous measurement of time-varying and covariate-varying effects, with covariates potentially depend on event history. The proposed semiparametric models offer much flexibility through the choice of different link functions and parametric functions. Two hypothesis tests have been developed to assess the parametric functions of the covariate-varying effects. For the proposed nonparametric intensity model with gamma frailty, estimation procedure involves using an Expectation-Maximization (EM) algorithm and local linear estimation techniques. Variance estimators are obtained through a weighted bootstrap procedure. Both of the proposed models have been applied to a malaria vaccine efficacy trial (MAL-094) to assess the efficacy of the RTS,S/AS01 vaccine.
