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.
The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and, therefore, design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective learning analytics systems. Because of the complexity of heterogeneous student data, models designed to analyze it frequently neglect temporal correlations in the interest of convenience. Moreover, the performance descriptions gained from the student data model or prediction results from the analytical models do not always help explain the "why" and "how" behind it. Furthermore, domain specialists are unable to participate in the knowledge discovery process since it necessitates significant data science abilities, and an analytical model is a black box to them.
This research aims to develop analytical models that enable domain experts to study their students' performance behavior and explore trustworthy sources of information with the help of explanations on the analytics. Our work demonstrates various approaches to using the temporal aspect of heterogeneous student data to build analytical models: weighted network analysis, unsupervised cluster analysis, and recurrent neural network analytics. The description, implementation process, and findings of each method are presented as technical contributions to the temporal analysis of student data. We experiment with all these analytical models that highlight the complexity of heterogeneous-temporal data, model building, decision-making tasks, and the need for a more in-depth focus on visual information of analytics with state-of-art explainable AI tools and techniques.
Our work underscores a need for developing a robust way to integrate the possibilities inherent within each approach. To achieve this goal, we present a comprehensive yet flexible and empirical framework to support the design and development of analytical models to extract meaningful insights about students' academic performance and identify early actionable interventions to improve the learning experience. We illustrate our framework on three applications (e.g., student network model, unsupervised clustering model, and recurrent neural network analytics) to demonstrate the value of this framework in addressing the challenges of using student data for learning analytics. These applications present vast opportunities to benefit students' learning experience by implementing flexible educational data representations, fitting different predictive models, and extracting insights for designing prescriptive analytics and building strategies to overcome perceived limitations.
An academic institution's culture drives its ability to accept, leverage, and deploy predictive and prescriptive analytics to enhance the workflow of maximizing pedagogical outcomes. We believe that our work will aid in the future development or refinement of a set of design standards for learning analytics systems.
In this thesis, a combined approach based on the Finite Element (FE) and Smoothed Particle Hydrodynamics (SPH) methods is proposed to model turning operations. The approach exploits the advantages of each method and leads to high-fidelity coupled FE-SPH machining models that are significantly more numerically efficient and are on par with the models based on each of the two methods alone. Both two-dimensional and three-dimensional models are developed and validated by comparing predicted forces and chip morphologies with experimental results. Parametric studies are carried out to fine-tune the model-based parameters in order to avoid numerical stability issues. The three-dimensional models are extended to include modulated tool path (MTP) machining which is a technique for breaking chips during machining by modulating the motion of the tool. The MTP model predictions are shown to agree with the results from an existing analytical model. With this model, various tool paths can be simulated to choose an optimal path that decreases tool-wear without sacrificing productivity. Preliminary results from a three-dimensional turning model incorporating machining dynamics through a spring-damper system are also presented. This model has the potential to be used for studying machining stability for a given set of machining conditions.
In addition to the above, another significant contribution of this thesis is the determination of Johnson-Cook material model parameters for a given material using an inverse method and experimental values of cutting forces and workpiece temperatures. The methodology described in the present work identifies the non-uniqueness of the solution to the inverse problem and proposes an approach that eliminates the non-uniqueness.
Charlotte Bilingual Preschool’s Family Program aims to equip parents to support their children’s education at home and at school by increasing parents’ educational engagement, promoting parenting best practices, developing families’ social capital, and supporting families’ mental health and well-being. This study aimed to evaluate 1) how the Family Program promotes growth in these areas for the families at the preschool, 2) the interconnections among parents’ attitudes, behaviors, and supports, and 3) how parents’ attitudes, behaviors, and supports relate to their children’s functioning in preschool.
The analysis of survey data collected at the beginning and end of the 2019-20 school year uncovered little evidence that attendance at Family Program events (i.e., Family Cafes and Workshops) led to improvements in family or child outcomes, other than increased parent friendships and more connections in the preschool family network. The disruption of programming caused by the COVID-19 pandemic and the shift to remote instruction in March 2020, likely relate to the lack of findings in this area. Nonetheless, correlational and regression analyses did identify relationships among mothers’ attitudes, perceptions of social support and social capital, and educational involvement behaviors. For instance, findings suggest that common good social capital (i.e., a positive, collaborative community atmosphere) may promote positive interactions with teachers and other parents, which could in turn, promote more positive educational involvement behaviors, including home-based involvement, ethnic identity parenting, and more positive behavior management practices. Additionally, analyses indicated that the positive relationship between maternal stress and negative behavior management practices was attenuated when mothers perceive strong social support and social capital. While these positive outcomes did not relate to parents’ attendance at Family Cafes and Workshops, they were associated with parents’ self-reported school involvement, suggesting that parents’ broader interactions with the Family Program (i.e., beyond attendance at Family Cafes and Workshops) may yield positive outcomes.
This study’s findings support the approach of Charlotte Bilingual Preschool’s Family Program, by connecting caregivers’ attitudes, sense of support, and social capital (which are intermediate goals of the Family Program) to their educational involvement behaviors (the Family Program’s primary goal). Theoretically, promoting positive family involvement should yield more positive developmental outcomes for children in the short- and long-term as well. This study provided some support for this hypothesis, by connecting parents’ bonding and bridging social capital and their efforts to promote children’s appreciation of their ethnic and cultural identities to children’s social-emotional functioning and language skills. Furthermore, results suggested that when parents reported greater increases or improvements in several family-level variables, their children tended to show larger improvements in social-emotional protective factors and behavior. These findings indicate that the Family Program can have an important impact on children and families, especially by connecting socially isolated families with greater social support and social capital.
The COVID-19 pandemic and the preschool’s shift to remote programming in March 2020 was a major limitation that disrupted programming and reduced this study’s capacity to draw strong conclusions. However, the pandemic also provided an opportunity to examine the links between various forms of remote engagement and outcomes for children and families. Despite the pandemic, this study’s findings have important implications for Charlotte Bilingual Preschool, as well as other stakeholders seeking to enhance two-generation approaches to early childhood education; especially those supporting Latino immigrant families and English language learners. Limitations, implications, and future directions are discussed.
Athletes are aware that with involvement in sport they are exposed to the risk of getting injured. Suffering an injury can be one of the most stressful experiences in a student-athlete’s athletic career and can cause a series of psychological, emotional, and social responses, as well as impact one’s sense of identity. The very sparse literature in the counseling field regarding student-athletes and lack of research in general, exploring women student-athletes and women track and field student-athletes in particular, contributes to the need for this study. The purpose of this study was to explore lived experiences of former women track and field college student-athletes who trained and competed through pain and injury. This study utilized a phenomenological approach and implemented semi-structured interviews. Over the course of a six-week period, a total of 10 participants completed a demographic questionnaire and were interviewed via Zoom to facilitate in-depth descriptions of their experiences. Moustakas (1994) methods consistent with qualitative phenomenological research design were used to facilitate the data analysis. A total of five major themes emerged from the data, including: identity, perception of pain and injury, student-athlete - coach relationship, support system, and psychological impact. This research found that the themes are interconnected and impact each other. The findings indicate that women track and field student-athletes who chose to train and compete through pain and injuries face identity challenges, which are further facilitated by student-athlete – coach relationship, one’s support system, and acceptance of the “push through the pain” mindset. This mindset was found to be further facilitated by the underlying belief that student-athlete role is a job for which participants have been compensated. Participants were also found to minimize and justify their pain as a coping mechanism to help them in continuing to train and compete despite being in pain and injured. The relationship between participants and their coaches was found to contribute to negative psychological experiences. All themes were closely connected with cognitive and emotional functioning of the participants. Implications for counselors and counselor educators as well as future research recommendations are discussed. However, the emphasis for counselors is to approach working with student-athletes from a holistic standpoint, disclose personal experiences with athletics early on in the therapeutic relationship, and provide substantial psychoeducation regarding intercorrelation between mental health and athletic performance.
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.
The prediction of RNA secondary structure is complex. The biomolecule can adopt or sample numerous stable conformations, can change structure in response to a stimulus such as a binding event, and does not simply obey thermodynamically favorable folding rules. Due to this, estimation of structure based on the primary sequence is unreliable and misleading. Probing RNA structure dramatically improves computational prediction. The examination of both, ex vivo and in cell RNA can provide important information regarding structural stability, the RNA interactome, and refolding effects.
Current structural probes for RNA selective 2′-hydroxy acylation analyzed by primer extension (SHAPE) rely on a reaction with the 2′-OH on the ribose sugar of residues that are not base paired. These flexible residues can then be determined using gel electrophoresis or quantified using next generation sequencing (NGS) and mutational profiling (MaP) to prepare a library of probing data. The advent of SHAPE technologies led to a rapid increase in the accessibility of RNA structural data. Several successful SHAPE probes have been previously demonstrated, but arguments regarding reactivity and cell permeability remain. In this work, the design and application of novel, variably reactive SHAPE probes is shown ex vivo and a novel in cell probe is demonstrated.
Romantic relationships have an impact on both partners health and well-being, however, much remains unknown regarding how health behaviors are shaped in the early stages of dating. The developmental time of emerging adulthood targets an age group where romantic relationships and alcohol use commonly intersect and may contribute to lifelong patterns of use. This study utilized novel research methodology of combining new couple’s text messages during the early stages of dating (<6 months) with survey data as well as advanced statistical methods (i.e., Actor Partner Interdependence Model) to examine how emerging adult couples text messaging about alcohol early in their romantic relationships impacted each partner intra- and interpersonally in terms of alcohol use and attitudes. Additionally, using the moderated Actor-Partner Interdependence Model this study examined how relationship factors (i.e., relationship power and relationship satisfaction) linked to health outcomes and engagement in risk behavior moderated these relationships. Results indicated that text messages between partners about alcohol were significantly positively correlated with alcohol use, but not attitudes, and in a dyadic context text messages predicted one’s own frequency of alcohol use but not their partners. Moderation analyses were underpowered due to a small sample size. These findings indicate that communication about alcohol via text messages may play an important role in frequency of alcohol use among early dating couples, particularly on one’s own behavior. Research and clinical implications of this work are also discussed.
