The emergence of machine intelligence, which is superior to the best human talent in some problem-solving tasks, has rendered conventional educational goals obsolete, especially in terms of enhancing human capacity in specific skills and knowledge domains. Hence, artificial intelligence (AI) has become a buzzword, espousing both crisis rhetoric and ambition to enact policy reforms in the educational policy arena. However, these policy measures are mostly based on an assumption of binary human-machine relations, focusing on exploitation, resistance, negation, or competition between humans and AI due to the limited knowledge and imagination about human-machine relationality. Setting new relations with AI and negotiating human agency with the advanced intelligent machines is a non-trivial issue; it is urgent and necessary for human survival and co-existence in the machine era. This is a new educational mandate. In this context, this research examined how the notion of human and machine intelligence has been defined in relation to one another in the intellectual history of educational psychology and AI studies, representing human and machine intelligence studies respectively. This study explored a common paradigmatic space, so-called ‘cyborg space,’ connecting the two disciplines through cross-referencing in the citation network and cross-modeling in the metaphorical semantic space. The citation network analysis confirmed the existence of cross-referencing between human and machine intelligence studies, and interdisciplinary journals conceiving human-machine interchangeability. The metaphor analysis found that the notion of human and machine intelligence has been seamlessly interwoven to be part of a theoretical continuum in the most commonly cited references. This research concluded that the educational research and policy paradigm needs to be reframed based on the fact that the underlying knowledge of human and machine intelligence is not strictly differentiated, and human intelligence is relatively provincialized within the human-machine integrated system.

This research is an anthology of a series of papers intended to describe the health state, healthcare experiences, healthcare preventive practice, healthcare barriers, and cardiovascular disease (CVD) risk factors of Asian Americans (AA) residing in Arizona (AZ). Asian Americans are known to be vulnerable populations and there is paucity of data on interventions to reduce CVD risk factors. An extensive literature review showed no available disaggregated health data of AA in AZ. The Neuman Systems Model guided this study. Chapter 1 elucidates the importance of conducting the research. It provides an overview of the literature, theory, and methodology of the study. Chapters 2 and 3 describe the results of a cross-sectional descriptive secondary analysis using the 2013, 2015, and 2017 Behavior Risk Factor Surveillance System (BRFSS) datasets. The outcomes demonstrate the disaggregated epidemiological phenomenon of AA. There were variations in their social determinants of health, healthcare barriers, healthcare preventive practice, CVD risk factors, and healthcare experiences based on perceived racism. It highlighted modifiable and non-modifiable predictors of hypertension (HTN) and diabetes. Chapter 4 is an integrative review of interventions implemented to reduce CVD risks tailored for Filipino Americans. Chapter 5 summarizes the research findings. The results may provide the community of practicing nurses, researchers, and clinicians the evidence to plan, prioritize, and implement comprehensive, theoretically guided, and culturally tailored community-led primary and secondary prevention programs to improve their health outcomes. The data may serve as a tool for stakeholders and policy makers to advocate for public health policies that will elevate population health of AA or communities of color in AZ to be in line with non-Hispanic White counterparts.

Longitudinal studies contain correlated data due to the repeated measurements on the same subject. The changing values of the time-dependent covariates and their association with the outcomes presents another source of correlation. Most methods used to analyze longitudinal data average the effects of time-dependent covariates on outcomes over time and provide a single regression coefficient per time-dependent covariate. This denies researchers the opportunity to follow the changing impact of time-dependent covariates on the outcomes. This dissertation addresses such issue through the use of partitioned regression coefficients in three different papers.

In the first paper, an alternative approach to the partitioned Generalized Method of Moments logistic regression model for longitudinal binary outcomes is presented. This method relies on Bayes estimators and is utilized when the partitioned Generalized Method of Moments model provides numerically unstable estimates of the regression coefficients. It is used to model obesity status in the Add Health study and cognitive impairment diagnosis in the National Alzheimer’s Coordination Center database.

The second paper develops a model that allows the joint modeling of two or more binary outcomes that provide an overall measure of a subject’s trait over time. The simultaneous modelling of all outcomes provides a complete picture of the overall measure of interest. This approach accounts for the correlation among and between the outcomes across time and the changing effects of time-dependent covariates on the outcomes. The model is used to analyze four outcomes measuring overall the quality of life in the Chinese Longitudinal Healthy Longevity Study.

The third paper presents an approach that allows for estimation of cross-sectional and lagged effects of the covariates on the outcome as well as the feedback of the response on future covariates. This is done in two-parts, in part-1, the effects of time-dependent covariates on the outcomes are estimated, then, in part-2, the outcome influences on future values of the covariates are measured. These model parameters are obtained through a Generalized Method of Moments procedure that uses valid moment conditions between the outcome and the covariates. Child morbidity in the Philippines and obesity status in the Add Health data are analyzed.

Modeling human survivorship is a core area of research within the actuarial com

munity. With life insurance policies and annuity products as dominant financial

instruments which depend on future mortality rates, there is a risk that observed

human mortality experiences will differ from projected when they are sold. From an

insurer’s portfolio perspective, to curb this risk, it is imperative that models of hu

man survivorship are constantly being updated and equipped to accurately gauge and

forecast mortality rates. At present, the majority of actuarial research in mortality

modeling involves factor-based approaches which operate at a global scale, placing

little attention on the determinants and interpretable risk factors of mortality, specif

ically from a spatial perspective. With an abundance of research being performed

in the field of spatial statistics and greater accessibility to localized mortality data,

there is a clear opportunity to extend the existing body of mortality literature to

wards the spatial domain. It is the objective of this dissertation to introduce these

new statistical approaches to equip the field of actuarial science to include geographic

space into the mortality modeling context.

First, this dissertation evaluates the underlying spatial patterns of mortality across

the United States, and introduces a spatial filtering methodology to generate latent

spatial patterns which capture the essence of these mortality rates in space. Second,

local modeling techniques are illustrated, and a multiscale geographically weighted

regression (MGWR) model is generated to describe the variation of mortality rates

across space in an interpretable manner which allows for the investigation of the

presence of spatial variability in the determinants of mortality. Third, techniques for

updating traditional mortality models are introduced, culminating in the development

of a model which addresses the relationship between space, economic growth, and

mortality. It is through these applications that this dissertation demonstrates the

utility in updating actuarial mortality models from a spatial perspective.

Bivariate responses that comprise mixtures of binary and continuous variables are common in medical, engineering, and other scientific fields. There exist many works concerning the analysis of such mixed data. However, the research on optimal designs for this type of experiments is still scarce. The joint mixed responses model that is considered here involves a mixture of ordinary linear models for the continuous response and a generalized linear model for the binary response. Using the complete class approach, tighter upper bounds on the number of support points required for finding locally optimal designs are derived for the mixed responses models studied in this work.

In the first part of this dissertation, a theoretical result was developed to facilitate the search of locally symmetric optimal designs for mixed responses models with one continuous covariate. Then, the study was extended to mixed responses models that include group effects. Two types of mixed responses models with group effects were investigated. The first type includes models having no common parameters across subject group, and the second type of models allows some common parameters (e.g., a common slope) across groups. In addition to complete class results, an efficient algorithm (PSO-FM) was proposed to search for the A- and D-optimal designs. Finally, the first-order mixed responses model is extended to a type of a quadratic mixed responses model with a quadratic polynomial predictor placed in its linear model.

This dissertation reports three studies of the relationships between meanings teachers hold and meanings their students construct.

The first paper reports meanings held by U.S. and Korean secondary mathematics teachers for teaching function notation. This study focuses on what teachers in U.S. and Korean are revealing their thinking from their written responses to the MMTsm (Mathematical Meanings for Teaching secondary mathematics) items, with particular attention to how productive those meanings would be if conveyed to students in a classroom setting. This paper then discusses how the MMTsm serves as a diagnostic instrument by sharing a teacher’s story. The data indicates that many teachers name rules instead of constructing representations of functions through function notation.

The second paper reports the conveyance of meaning with eight Korean teachers who took the MMTsm. The data that I gathered was their responses to the MMTsm, what they said and did in the classroom lessons I observed, pre- and post-lesson interviews with them and their students. This paper focuses on the relationships between teachers’ mathematical meanings and their instructional actions as well as the relationships between teachers’ instructional actions and meanings that their students construct. The data suggests that holding productive meanings is a necessary condition to convey productive meanings to students, but not a sufficient condition.

The third paper investigates the conveyance of meaning with one U.S. teacher. This study explores how a teacher’s image of student thinking influenced her instructional decisions and meanings she conveyed to students. I observed 15 lessons taught by a calculus teacher and interviewed the teacher and her students at multiple points. The results suggest that teachers must think about how students might understand their instructional actions in order to better convey what they intend to their students.

The studies show a breakdown in the conveyance of meaning from teacher to student when the teacher has no image of how students might understand his or her statements and actions. This suggests that it is crucial to help teachers improve what they are capable of conveying to students and their images of what they hope to convey to future students.

The goal of diagnostic assessment is to discriminate between groups. In many cases, a binary decision is made conditional on a cut score from a continuous scale. Psychometric methods can improve assessment by modeling a latent variable using item response theory (IRT), and IRT scores can subsequently be used to determine a cut score using receiver operating characteristic (ROC) curves. Psychometric methods provide reliable and interpretable scores, but the prediction of the diagnosis is not the primary product of the measurement process. In contrast, machine learning methods, such as regularization or binary recursive partitioning, can build a model from the assessment items to predict the probability of diagnosis. Machine learning predicts the diagnosis directly, but does not provide an inferential framework to explain why item responses are related to the diagnosis. It remains unclear whether psychometric and machine learning methods have comparable accuracy or if one method is preferable in some situations. In this study, Monte Carlo simulation methods were used to compare psychometric and machine learning methods on diagnostic classification accuracy. Results suggest that classification accuracy of psychometric models depends on the diagnostic-test correlation and prevalence of diagnosis. Also, machine learning methods that reduce prediction error have inflated specificity and very low sensitivity compared to the data-generating model, especially when prevalence is low. Finally, machine learning methods that use ROC curves to determine probability thresholds have comparable classification accuracy to the psychometric models as sample size, number of items, and number of item categories increase. Therefore, results suggest that machine learning models could provide a viable alternative for classification in diagnostic assessments. Strengths and limitations for each of the methods are discussed, and future directions are considered.

Guided by Tinto’s Theory of College Student Departure, I conducted a set of five studies to identify factors that influence students’ social integration in college science active learning classes. These studies were conducted in large-enrollment college science courses and some were specifically conducted in undergraduate active learning biology courses. Using qualitative and quantitative methodologies, I identified how students’ identities, such as their gender and LGBTQIA identity, and students’ perceptions of their own intelligence influence their experience in active learning science classes and consequently their social integration in college. I also determined factors of active learning classrooms and instructor behaviors that can affect whether students experience positive or negative social integration in the context of active learning. I found that students’ hidden identities, such as the LGBTQIA identity, are more relevant in active learning classes where students work together and that the increased relevance of one’s identity can have a positive and negative impact on their social integration. I also found that students’ identities can predict their academic self-concept, or their perception of their intelligence as it compares to others’ intelligence in biology, which in turn predicts their participation in small group-discussion. While many students express a fear of negative evaluation, or dread being evaluated negatively by others when speaking out in active learning classes, I identified that how instructors structure group work can cause students to feel more or less integrated into the college science classroom. Lastly, I identified tools that instructors can use, such as name tents and humor, which can positive affect students’ social integration into the college science classroom. In sum, I highlight inequities in students’ experiences in active learning science classrooms and the mechanisms that underlie some of these inequities. I hope this work can be used to create more inclusive undergraduate active learning science courses.

Generalized Linear Models (GLMs) are widely used for modeling responses with non-normal error distributions. When the values of the covariates in such models are controllable, finding an optimal (or at least efficient) design could greatly facilitate the work of collecting and analyzing data. In fact, many theoretical results are obtained on a case-by-case basis, while in other situations, researchers also rely heavily on computational tools for design selection.

Three topics are investigated in this dissertation with each one focusing on one type of GLMs. Topic I considers GLMs with factorial effects and one continuous covariate. Factors can have interactions among each other and there is no restriction on the possible values of the continuous covariate. The locally D-optimal design structures for such models are identified and results for obtaining smaller optimal designs using orthogonal arrays (OAs) are presented. Topic II considers GLMs with multiple covariates under the assumptions that all but one covariate are bounded within specified intervals and interaction effects among those bounded covariates may also exist. An explicit formula for D-optimal designs is derived and OA-based smaller D-optimal designs for models with one or two two-factor interactions are also constructed. Topic III considers multiple-covariate logistic models. All covariates are nonnegative and there is no interaction among them. Two types of D-optimal design structures are identified and their global D-optimality is proved using the celebrated equivalence theorem.

In the last decade, the population of honey bees across the globe has declined sharply leaving scientists and bee keepers to wonder why? Amongst all nations, the United States has seen some of the greatest declines in the last 10 plus years. Without a definite explanation, Colony Collapse Disorder (CCD) was coined to explain the sudden and sharp decline of the honey bee colonies that beekeepers were experiencing. Colony collapses have been rising higher compared to expected averages over the years, and during the winter season losses are even more severe than what is normally acceptable. There are some possible explanations pointing towards meteorological variables, diseases, and even pesticide usage. Despite the cause of CCD being unknown, thousands of beekeepers have reported their losses, and even numbers of infected colonies and colonies under certain stressors in the most recent years. Using the data that was reported to The United States Department of Agriculture (USDA), as well as weather data collected by The National Centers for Environmental Information (NOAA) and the National Centers for Environmental Information (NCEI), regression analysis was used to investigate honey bee colonies to find relationships between stressors in honey bee colonies and meteorological variables, and colony collapses during the winter months. The regression analysis focused on the winter season, or quarter 4 of the year, which includes the months of October, November, and December. In the model, the response variables was the percentage of colonies lost in quarter 4. Through the model, it was concluded that certain weather thresholds and the percentage increase of colonies under certain stressors were related to colony loss.