EXPLORING THE EFFECTS OF THE RELATIONSHIP BETWEEN SCHOOL CHARACTERISTICS, TEACHER CHARACTERISTICS, ADMINISTRATIVE SUPPORT, AND SCHOOL CLIMATE ON VOLUNTARY INTRA-DISTRICT TEACHER MIGRATION

This quantitative research study utilized a binary logistic regression in a block design to investigate exogenous and endogenous factors influencing a teacher’s decision to make an intra-district move. The research focused on the following exogenous factors: classroom characteristics (size of class, percent minority, percent of students with an individualized education plan, and percent of students that are English language learners) and teacher characteristics (experience and gender). The following endogenous factors were examined: direct administrative influence (administrative support, rules enforced, school vision, teacher recognition, and job security) and indirect administrative influence (school climate, student misbehavior, parental support, materials, staff collaboration). The research was conducted by using information available from the National Center for Educational Statistics, the SASS from 2011-2012 and TFS from 2012-2013. The 2012-2013 Teacher Follow-up Survey identified 60 teachers who made a voluntary intra-district move. Results illustrate there is a statistically significant relationship between percentage of English Language Learners and overall job satisfaction and teachers choosing to make an intra-district move.

School-Based Referrals to the Juvenile Courts: Prevalence and Characteristics in Several States

Effective school discipline practices are essential to keeping schools safe and creating an optimal learning environment. However, the overreliance of exclusionary discipline often removes students from the school setting and deprives them of the opportunity to learn. Previous research has suggested that students are being introduced to the juvenile justice system through the use of school-based juvenile court referrals. In 2011, approximately 1.2 million delinquency cases were referred to the juvenile courts in the United States. Preliminary evidence suggests that an increasing number of these referrals have originated in the schools. This study investigated school-based referrals to the juvenile courts as an element of the School-to-Prison Pipeline (StPP). The likelihood of school-based juvenile court referrals and rate of dismissal of these referrals was examined in several states using data from the National Juvenile Court Data Archives. In addition, the study examined race and special education status as predictors of school-based juvenile court referrals. Descriptive statistics, logistic regression and odds ratio, were used to analyze the data, make conclusions based on the findings and recommend appropriate school discipline practices.

A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.

An Investigation of Child and Adolescent Dental Sealant Predictors, NHANES 2011-2012

Objective: To examine sociodemographic and dental factors for associations with dental sealant placement in children and adolescents aged 6-18 years old. Methods: Secondary data analysis of 2011-2012 NHANES data was conducted. Multiple logistic regression models were used to assess relationships between predictor variables and sealant presence. Results: More than a third (37.1%) of children and adolescents have at least one sealant present; 67.9% of children compared with 40.4% of adolescents. Racial/ethnic differences exist, with Non-Hispanic black youth having the lowest odds of having sealants. Sealant placement odds vary by presence of dental home; the magnitude of the odds varies by age group. Those with untreated decay have lower odds of having sealants than those who do not have untreated decay (child OR: 2.6, 95% CI: 1.83-3.72; adolescent OR: 3.9, 95% CI: 2.59-6.07). Conclusion: Disparities exist in odds of sealant prevalence across racial/ethnic groups, income levels, and dental disease and visit characteristics. Further research is necessary to understand the reasons for these differences and to inform future interventions.