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Diet-related diseases like obesity and type-2 diabetes are on the rise. Precision nu- trition, a way to tailor dietary requirements for each individual, is heralded as a solution to these problems. However, nutritional research is held within sparse, siloed resources that rarely connect, which leads to significant barriers hindering the progress of precision nutrition. Three knowledgebases were produced as a re- sult of this work. The ABCkb 1.0 overcomes these barriers by linking 11 separate resources in the path from plants to disease through molecular mechanisms. This resource is built in Neo4j and provides a web-based interface available for browsing (https://abckb.charlotte.edu). A second knowledgebase, ABCkb 2.0 connects micro- biota information to diet and human health through the incorporation of text-mined associations from full text articles. The final knowledgebase produced links long-covid to dietary components through possible molecular mechanisms. These three knowl- edgebases promote progress in precision nutrition to tackle the rise in diet-related disease.
Previous research has investigated the school context using conceptualizations of two constructs, school culture and school climate, that appear to overlap and contain measurement flaws, limiting their utility in applied research settings. To improve learning conditions and promote more equitable academic opportunities and outcomes for students in grades 3-8, the Charlotte, NC, community would benefit from a standard system of measurement that captures the essential elements of school climate and culture that local stakeholders believe matter most for students to succeed in Charlotte-Mecklenburg Schools (CMS). CMS does not currently administer a comprehensive school culture or climate survey. The present study aimed to address that need. Through a multiphase, participatory community research project, a coherent, parsimonious, and clear conceptualization of school environment emerged, setting the stage for the development and initial validation of the School Environment Survey.
This collaborative effort involved the exchange of knowledge, expertise, and resources via a partnership involving the Community Psychology Research Lab at the University of North Carolina at Charlotte and two community partners: CMS and a nonprofit organization, Communities In Schools of Charlotte-Mecklenburg. During the first phase of this project, essential elements of school climate and culture were reviewed, analyzed, and discussed during interviews and focus groups with 126 local stakeholders until the broader construct of school environment had been defined as a category of concepts that reflect the surroundings or conditions in which people operate in school. With this broad definition of school environment as the underlying, multidimensional construct, five applicable concepts (i.e., domains; see Kohl et al., 2013; Wang & Degol, 2016) were hypothesized to make up school environment: academics, safety, shared vision, community, and physical environment. Multiple participatory steps led to the development of 131 items hypothesized and designed to reflect 16 identified dimensions of school environment, organized into these five domains.
The resulting measure was piloted online with 186 teacher participants during the 2020-2021 school year. Exploratory factor analysis results suggest that within the boundary conditions of this effort (i.e., a focus on two CMS learning communities, the inclusion of teachers from grades 3-8, data collected during school year 2020-2021), a 25-item School Environment Survey that captures three domains (academics, safety, and shared vision) may be a useful indicator of teachers’ perceptions of school environment. That model explained 55% of the total variance and, notably, items that performed well on the resulting version of the measure cover nearly the entire hypothesized breadth of the concept as it was defined and operationalized by stakeholders; reliability estimates met or exceeded acceptable thresholds; and school environment results were found to positively relate to student learning outcomes (specifically, standardized tests in reading and math for students in grades 3-8).
However, this study had a relatively small sample size that prevented researchers from conducting a confirmatory factor analysis, and COVID-19 presented additional challenges and limitations. Therefore, in addition to an overview of specific advantages and the empirical and theoretical support for the current version of the School Environment Survey, recommendations for ongoing validation are provided as well as considerations of the implications for local practice.
Passion can drive entrepreneurs to new heights of success and fulfilment, but the dark side can also lead to conflict with relationships and activities. Work addiction goes a step further as it demands the entrepreneur’s time and energy even when they are off the clock. This study explores the relationship between entrepreneurial passion and work addiction, leaning on the Dualistic Model of Passion. It also investigates the possibility that personality traits influence this connection. Empirical evidence suggests that obsessively passionate entrepreneurs are more likely to encounter work addiction than their less passionate counterparts. The data also supports a weak link between harmonious entrepreneurial passion and work addiction, with harmoniously passionate entrepreneurs being less susceptible to work addiction. Finally, there was minimal support for the idea that the Dark Triad traits (narcissism, Machiavellianism, psychopathy) play a role in this exchange. There is a secondary nuance to this study regarding passion and addiction. It provides evidence that passion with one target can relate to addiction with another. This encourages the examination of other cross-target passion and addiction relationships.
The gap in supply (i.e., shortage) and demand of the STEM workforce have prompted extensive research on identifying factors that predict STEM outcomes and retention of students. Few studies, however, have examined the relationships between STEM outcomes and predictors in an integrated model, taking into account measurement errors in the predictors. Drawing upon the Expectancy-Value Model of Achievement Related Performance and Choice, I conducted a structural equation modeling (SEM) analysis to examine the relationships among academic support, academic engagement, mathematics readiness, and hours worked and first-year STEM students’ academic success and retention. The SEM allowed me to investigate the relationships between predictors and outcomes simultaneously while accounting for the measurement error. The sample consisted of first-year STEM majors who took the National Survey of Student Engagement during 2016, 2018, and 2020 academic years. Results indicated that academic support was a statistically significant predictor of first-year STEM students’ academic success and retention. Additionally, mathematics readiness was found to be a statistically significant predictor of first-year retention. Last but not the least, results suggested that female students on average were more likely than their male counterparts to engage in academic support and academic engagement activities even though females had longer on-campus work hours than males. These results have implications for policies and practices aimed at improving STEM retention. Areas of further research are also identified.
This dissertation explores the relationship between entrepreneurial passion and entrepreneurial persistence within small-size companies. Although prior literature has acknowledged that entrepreneurial passion enhances entrepreneurial persistence, a better understanding of the role of opportunity evaluation in this context is needed. Using data from 176 entrepreneurs, I suggest that gain estimation, loss estimation, and feasibility moderate the relationship between entrepreneurial passion and entrepreneurial persistence and that this relationship leads to the invention and development of new business opportunities. Implications for theory and practice as well as avenues for future research are discussed.
INDEX WORDS: Entrepreneurial passion, Entrepreneurial persistence, Opportunity evaluation
The underrepresentation of Black women in Science, Technology, Engineering, and Mathematics (STEM) is a long-standing issue. According to the National Science Foundation (NSF, 2019), Black women hold less than 10% of STEM degrees, while only 2% work in STEM fields. These disparities can be attributed to structural inequities related to the STEM pipeline. Therefore, to mitigate these disparities, informal STEM education programs can help increase participation in STEM and create more opportunities for Black women and girls. Thus, this collective case study addressed the following research questions: 1. How do adolescent Black girls engage in and respond to informal STEM education programs? 2. How can informal STEM education programs develop adolescent Black girls' STEM identity and increase participation in STEM? 3. What pedagogical practices effectively engage adolescent Black girls in STEM? Purposeful criterion sampling was used to recruit participants for this study. The research process included interviewing four adolescent Black girls who attended informal STEM education programs. To understand each program's process and climate, participants described their experiences through initial questionnaires, interviews, and written prompt responses. Furthermore, this research used critical race feminism and Black feminist thought to analyze five prominent themes from the data. The findings suggest that Black girls who participate in informal STEM education programs (a) benefited from an affirming environment, (b) experienced engaging instructional strategies, (c) utilized support systems, (d) recognized racial representation was significant, and (e) experienced racial microaggressions. In addition, the findings support equitable STEM access for Black girls through informal education programs. The implications of this study also suggest a need to attend to the psychological and emotional needs of Black girls in informal STEM spaces.
Students in the United States are not achieving in mathematics as indicated on the NAEP (2019) exams and other measurements of student achievement (OECD, 2019; O’Dwyer, Wang, & Shields, 2015; NCES, 2019). Mathematically gifted and promising students are especially impacted by this phenomenon, though it is not exactly known what factors contribute to successful teachers of these students. This phenomenological case study focused on the beliefs, instructional practices, and conceptual understanding of mathematics of five teachers in a public charter school for gifted students. Data sources collected included semi-structured interviews, classroom observations, and questionnaires based on Swan’s (2006) practices and beliefs research with effective mathematics teachers. Two theories of giftedness served as the theoretical lens for this study: Renzulli’s Three-Ring Model (1978) and Gagné’s Differentiated Model of Giftedness and Talent (1985) to better understand these phenomena. Using an interpretive phenomenological analysis several themes emerged in response to each research question. Findings for instructional practices indicated that teachers used both student-centered and teacher-centered practices and consistently utilized differentiated groupings. Additionally, teacher participants believe that gifted students possess both positive traits and challenges and specifically for math, believe that sense-making is key, and math is a subject students should enjoy. Teachers’ conceptual understanding of mathematics is guided by their ongoing practice, the curriculum, and math experiences prior to teaching. These findings indicate the importance of ongoing training and professional development in mathematics and gifted education, as well as the recruitment and retention of teachers who possess a strong conceptual understanding of mathematics, a passion for the subject, and a student-centered approach to teaching.
Keywords: mathematically gifted, instructional practices, beliefs, teachers’ conceptual understanding of mathematics
Despite evidence of the rapid growth of Latinx students throughout the country (Gándara, 2017; He & Yu, 2017), there has been inadequate critical examination of the educational policies and practices impacting this population and their self-identification. Teachers play a crucial role in ensuring that all students, regardless of cultural or linguistic background, receive a quality education that will equip them for post-school success in an increasingly diverse society. Studies suggest that teachers who share the racial, cultural, and/or ethnic identity of their students can play an important role in their success. Unfortunately, Latinx teachers remain starkly underrepresented within the field (Shapiro & Partelow, 2018), even though Latinx teacher representation plays a critical role in not only the academic achievement of Latinx students but is also prevalent in the solving of larger issues related to ethnic and racial identity development and consciousness (Goldhaber et al., 2019) because of the postcolonial structures in schools. Postcolonial structures are implicated by the structural inequality and racial and economic disparities that continue to plague racialized groups throughout society. In order to fulfill this role, Latinx teachers need to richly understand their Latinx identity development and consciousness.
The purpose of this autoethnographic study was to systematically examine and reflect on my lived experiences as an immigrant in the United States to better understand Latinx identity development and consciousness and the role of Latinx teachers in combating the exclusionary practices of postcolonial structures in schooling. Using Latinx Critical Theory (LatCrit) as the theoretical framework, this dissertation focuses on Latinx teachers and the impact of identity development on their role in the classroom. I examined my Latinx identity at different stages of my life and positioned this within the collective experiences of other Latinx teachers to better understand the factors that have contributed to my evaluation of self.
The data collected for this study consisted of a life timeline, transcribed journals I kept throughout my teaching career, cultural artifacts, and an individual autobiographical narrative. This highly reflective process was then analyzed thematically to identify themes and patterns that emerged from the data. The findings revealed three major themes: ongoing identity detachment, Latinx imposter syndrome, and unveiled hypocrisy in education. Ongoing identity detachment highlights my continued loss of identity. Latinx imposter syndrome depicts the feelings of doubt, fear, and fraud I had to overcome in my role as a bilingual teacher of emergent bilingual students. Lastly, unveiled hypocrisy in education captures my awareness and understanding of the marginalization and exclusion within the education system. This study has significant implications for the understanding of Latinx teacher identity development and the influence their self-perception has on their work within the classroom. Recommendations on how to further the discussion on identity development and consciousness and the role of Latinx teachers in combating the exclusionary practices of postcolonial structures in schooling are made to teacher preparation programs, school and district leaders, Latinx teachers, and for future research.
Keywords: postcolonial structures, raciolinguistic ideologies, internalized oppression, immigrant experience, Latinx teacher identity
Dirty work is socially constructed as tainted on one or more domains (physical: dangerous, dirty, or associated with death; moral: underhanded or in contradiction to prevailing norms; social: in association with stigmatized others or done in subservience), and it shapes dirty workers’ perceptions and experiences of their identities. The processes through which the perception of taint shapes identities and associated outcomes (e.g., identity ambivalence, isolation) and the effects of the magnitude of dirt are not fully understood. To understand these processes, the present study describes the development of a tool to measure the dirt of dirty work. First,the author developed a series of item to assess the content domain of dirty work based on a literature review supported by open-ended responses describing work perceptions from dirty workers. In the subsequent studies, the author reduced the item pool by a series of exploratory factor analyses (EFA). Then, the author tested the overall model fit across two separate samples via confirmatory factor analysis (CFA) and identified a three-factor model. Finally, the author gathered validity evidence through convergent and discriminatory validity analyses: the pattern of correlations generally provided convergent validity evidence with the respective covariates, and the data tentatively supported the measure’s ability to discriminate among forms of taint by occupation in a one-way MANOVA.
Safety issues of lithium-ion batteries (LIBs) are usually initiated from an internal short circuit (ISC) that can be triggered by external accidental abusive loadings. The generated heat and the increased temperature would lead to several complicated physio-chemical changes of the batteries, e.g., thermal runaway (TR). Thus, investigation of the multiphysics behaviors of lithium-ion batteries becomes a paramount task to understand the battery safety issues. Experimental characterization and numerical simulation are essential ways to understand the underlying nature of the multiphysics behavior of batteries. However, experimental observation may only provide insufficient data due to the limitation of experimental technology. Particularly, in-situ and operando experiment methodologies are limited. Multiphysics modeling is regarded as a critical and insightful tool to unravel the nonlinear and complicated behaviors. Machine learning (ML) model with data-driven methodology is another important tool to realize fast and accurate estimation and classification. Herein, an ML-based ISC risk evaluation model will be first developed based on the training dataset generated by the combination of experimental data and simulation data. A Representative Volume Element (RVE) based mechanical model, which can predict accurate mechanical behaviors at a much lower calculation time cost, will be established to assist the data generation. Next, an ML-based classifier will be developed to classify the cell’s safety levels under various work conditions. A multiphysics model will be developed to assist the generation of training data samples. Finally, two typical safety issues: defect and TR propagation are systematically studied. The safety risk of the defective batteries will be further evaluated. Electrochemical and mechanical characterization tests will be designed and conducted. The multiphysics model will be used to provide necessary auxiliary instructions of the related mechanisms. TR propagation behaviors of battery packs will be experimentally and numerically investigated. The battery pack TR model will be developed based on the single-cell multiphysics model.
This study comprehensively investigates the multiphysics behavior of LIB cells under mechanical abusive loadings, highlights the promise of combining the physical model with a data-driven model, and provides an innovative solution for the recognition of the battery safety risks for battery safety monitoring.
