The purpose of this study was to evaluate the effectiveness of UDL-based enhancements of an online module on functional behavior assessment. The UDL framework is an instructional design framework designed to enhance access, engagement, and learning using three overarching principles: multiple means of engagement, multiple means of representation, and multiple means of action and expression. The researcher utilized a two-group randomized control trial (RCT) with pre- and posttest measures. The control group completed a publicly available module on functional behavior assessment while the intervention group completed an enhanced version of the same module. Results demonstrate that both the intervention and control group demonstrated similar levels of knowledge gains, indicating that UDL instructional design enhancements are equally as effective as current instructional design practices in online, asynchronous modules. Given the legislative mandates for the use of UDL and ethical considerations regarding student accessibility, UDL is recommended for continued use in higher education and other professional learning for educators.

Major education and training challenges are plaguing the United States in preparing the next generation of the future workforce to meet the demands of the 21st Century. Several calls have been released to improve education programs to ensure learners are acquiring 21st century knowledge, skills, and abilities (KSAs). As we embark on the digital and automation ages of the 21st century, it is essential that we move away from traditional education programs that define and measure KSAs as static constructs (e.g., standardized assessments) with little consideration of the actual real-time deployment of these processes, missing critical information on the degree to which learners are acquiring and applying 21st century KSAs. The objective of this dissertation is to use 1 book chapter and 2 journal articles to illustrate the value in leveraging emerging technologies and multimodal trace data to define and measure scientific thinking, reflection, and self-regulated learning--core 21st century skills, across contexts, domains, tasks, and populations (e.g., medical versus undergraduates versus middle-school students). Chapters 2-4 of this dissertation provide evidence of ways to leverage multimodal trace data guided by theoretical perspectives in cognitive and learning sciences, with a special focus in self-regulated learning, to assess the extent to which learners engaged in scientific thinking, reflection, and self-regulated learning during learning activities with emerging technologies. Overall, results from these chapters illustrate that it is necessary to utilize methods that capture learning processes as they unfold during learning activities that are guided by theoretical perspectives in self-regulated learning. Findings from this research hold significant broader impacts for addressing the education and training challenges in the United States by collecting multimodal trace data over the course of learning to not only detect and identify how learners are developing KSAs such as scientific thinking, reflection, and self-regulated learning, but where these data could be fed into an intelligent and adaptive system to repurpose it back to trainers, teachers, instructors, and learners for just-in-time interventions and individualized feedback. The intellectual merit of this dissertation focuses predominantly on the importance of utilizing rich streams of multimodal trace data that are mapped onto different theoretical perspectives on how humans self-regulate across tasks like clinical reasoning, scientific thinking, and reflection with emerging technologies such as a game-based learning environment called Crystal Island. Discussion is incorporated around ways to leverage multimodal trace data on undergraduate, middle-school, and medical student populations across a range of tasks including learning about microbiology to problem solving with a game-based learning environment called Crystal Island and clinically reasoning about diagnoses across emerging technologies.

The aim of causal effect estimation is to find the true impact of a treatment or exposure. Observational data is employed in social sciences to estimate causal effect but is susceptible to self-selection and unobserved confounding biases. Covariates included in analysis should strive to address these biases. This research focuses on investigating covariate selection approaches––common cause criterion (CC), Disjunctive Cause Criterion (DCC), Modified Disjunctive Cause Criterion (MDCC), and modified cause criterion (MCC)––in linear regression (LR) and propensity score methods (PSM) causal effect estimation in the presence of unmeasured confounding. Realistic social science scenarios such as––inclusion of proxy variables with varying degrees of strength, misidentification of the unmeasured covariate as a confounder, small sample sizes, and measurement error in proxy covariates—were investigated. For LR and PSM, five causal effect estimation models were built using different covariate selection approaches and compared on three performance metrics––bias, coverage, and empirical SE. Results showed that in the presence of an unmeasured confounder, the causal effect estimate is biased. Study 1 results indicate that MDCC approach resulted in more consistent and efficient causal effect estimates in the presence of unmeasured confounders. Studies 2a and 2b indicate that the MDCC approach is robust to the unobserved variable being a confounder and can be employed even if the unmeasured covariate is not a confounder without adversely impacting the performance measures. Studies 3 and 4 showed including a proxy of the unmeasured confounder, even a weak proxy (r ~ 0.20) or one with measurement error, results in an improvement in the consistency of the causal effect estimate and in the efficiency of the causal effect estimator. As the correlation between the proxy covariate and the unmeasured confounder gets smaller the causal effect estimator becomes less efficient and the causal effect estimate becomes less consistent.

The purpose of this study was to assess the effects of misspecification (i.e., under-specification, over-specification, and general misspecification) of the within-person level errors in longitudinal data under quadratic, exponential, and logistic latent growth curve models and to make a contribution to the literature in this area. A Monte Carlo simulation in R uncovered a common bias pattern throughout this study. For under-specification of autocorrelated processes and general misspecification of MA as AR, the mean intercept was more likely to be downward- biased, but the linear slope and nonlinear slope for an individual tend to be upward-biased. On average, the variation of the intercept is more likely to be underestimated for under-specification of autocorrelated errors in three types of nonlinear models, whereas the variation of linear slope variance is more often to be more overestimated, Opposite results were found for over-specification and general misspecification of AR data misspecified as MA. The covariance between intercept and linear slope was downward-biased by overspecified models but upward- biased by under-specified quadratic and exponential latent growth curve models. The covariance between intercept and logistic slope was downward-biased by over-specified models, but upward-biased by under-specified models. The reverse pattern was observed for the covariance between linear slope and logistic slope, which was underestimated by under-specified logistic latent growth curve models and overestimated by over-specified logistic LGCs. Longitudinal data studies are widely conducted, and nonlinear change over time is often the focus of such studies. Rarely, do applied researchers hypothesize, a priori, stochastic effects (in this case, AR, MA, and ARMA) because such processes are primarily nuisance conditions which are not the focus of longitudinal data studies. Nevertheless, stochastic effects are widely known to be present in such data, dating at least as far back as Box and Jenkins (1978). Applied researchers encounter stochastic effects when they are force to confront correlated errors over time. By modeling the impact of various stochastic effects on parameter estimates, applied researchers are introduced to alternative models to consider as rival hypotheses when conducting longitudinal data wherein nonlinear processes are anticipated, so as to filter stochastic processes should they occur, especially as evidenced by correlated errors so common in longitudinal data.

A regional area of a school district in central Florida used the implementation of a house system as a school improvement intervention to impact school climate and academic achievement. The purpose of this study was to determine if a house system is an effective school improvement intervention that has a positive effect on school climate and academic achievement of students in elementary schools. Four research questions were developed to investigate if there was a difference in elementary schools that implemented a house system and ones that did not during the 2018-2019 school year. All schools included in the study had 5th grade students who responded to Cognia© elementary student survey. Those responses were used to study school climate. Historical attendance and suspension data for school year 2018-2019 was also used. The scale scores from the Florida Standard Assessment (FSA) were used to measure student achievement in reading and math. Of the six schools included in this study it was found that the implementation of a house system did have an impact on suspensions and student achievement on standardized tests in reading and mathematics. There was no evidence to support the implementation of a house system having an impact on school climate and student attendance. This study adds to the literature of the impact of a house system on elementary schools. The findings of this research have implications for further research on the house system as an intervention to improve academic achievement in reading and math at elementary schools.

In conducting a cross-cultural study with a quantitative method, the researchers need to effectively address the cultural and linguistic influence on the operation of the instrument (or scale) across population groups. Measurement invariance (MI) provides valuable information to this concern and is the key to many psychological and developmental research studies. It is tested by evaluating how well the specified model fits the observed data. Researchers had developed effective fit indices to evaluate MI. Most scholars utilized the chi-square test with some alternative fit indices (such as root mean square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), comparative fit index (CFI), and others) to report MI. Researchers argue about the sensitivity of these fit indices, and whether these fit indices accurately reflect the MI level. The current study followed Khojasteh and Lo (2015)'s study to test the sensitivities of a series of fit indices, including ΔCFI, ΔRMSEA, ΔSRMR, ΔGamma, and Δχ2, under specified conditions with Monte Carlo simulation data. Experimental conditions included test length, number of factors, sample sizes, factor loadings, and the percentage of noninvariant items. Results showed that the ΔCFI and ΔGamma are most powerful in testing invariance and are less sensitive to the sample size and non-invariance (or lack of invariance, LOI) situations. There was inflation in Type I error in the 2 factors 8 variable models. ΔSRMR and ΔRMSEA are more powerful only when the sample size is 1,000. ΔSRMR is sensitive to sample size and level of LOI; hence, it is not recommended. The results are compared with previous simulation studies and provide significant implications to researchers who are applying measurement invariance procedures about what fit indices to adopt in their studies.

The problem I address in this study is the persistence of inequity and low-self-concept among Black students. This study examines the ways in which dark-skinned African American girls' educational experiences support or impair their identity development. It adds to the body of knowledge by exploring personal journeys toward self-awareness amid family, school, and societal realities. While research exists regarding the achievement gap between Black students and other groups and the efforts to close that gap, research that specifically addresses the needs of Black girls is lacking. This study is significant because it brings attention to an underrepresented group, Black girls. Further, this study recognizes and attends to the nuances among this group by focusing on dark-skinned African American girls. Self-concept does affect the achievement of students. However, this study investigates self-concept in a new way. It considers the internal processing of self-awareness and identity formation. The purpose of the study is to examine the ways in which African American girls' educational experiences support or impair their identity development. This qualitative study uses the transcendental phenomenological approach to describe the educational experiences of dark-skinned African American middle grades and high school girls as they develop and explore their identities. I interviewed African American girls who perceive themselves as dark-skinned about their educational experiences and how these experiences have or have not supported their understanding and perceptions of their own heritage and Black identity. Findings show that the participants perceived the school environment to be hostile and unsupportive of their identity development.

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