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Formative assessment’s evolution over the last 50 plus years has led to the ubiquitous use of the term and ostensibly its practice, yet very little research has specifically addressed teachers’ experiences of formative assessment, particularly in the realm of secondary English. This study’s goal, therefore, was to gain insight into how teachers experience engaging in formative assessment. By exploring their experiences, this descriptive phenomenological study sought to discover what meaning selected teachers ascribe to formative assessment and to thereby elevate teachers’ voices in the formative assessment conversation. This research question guided the study: What are secondary English teachers’ lived experiences of engaging in formative assessment? To answer this question, I generated data from 12 secondary English teachers by conducting in-depth, semi-structured interviews and collecting lived experience descriptions. Collectively, their experiences revealed that they practice formative assessment as a multi-step process, undertaken to determine where students are in their learning and to inform their instruction. They experience formative assessment as integral to their instruction and value informal formative interactions and conversations that are embedded in daily instruction. They consider positive class culture essential for undertaking formative assessment and have concerns that grading, district-mandated formative assessments, and the term itself may be impediments to effectively undertaking formative assessment. Ultimately, these findings offer strong support for the study’s conceptual framework; fill a gap in the formative assessment research; and offer policy makers, instructional leaders, and educational researchers insight into how these teachers understand and practice formative assessment.

Early reading skills are strongly associated with long term academic and life achievement. Despite the recognized importance of literacy, indicators point to a literacy crisis in the United States. Research and policies have highlighted the necessity of selecting reading programs with documented effectiveness and implementing them with fidelity. This mixed methods program evaluation investigated the extent to which the Fundations reading program is being implemented with fidelity at a private urban elementary school and if there has been a change in student reading performance since introducing the program. This study also explored teachers’ perceptions regarding program strengths and challenges along with their own competency and need for support. Both quantitative and qualitative data were collected in the form of implementation checklists, student running records scores, and a teacher survey. Findings indicated that the program is not scheduled for the prescribed frequency or length of lessons. This limited program exposure is significantly impacting overall fidelity, although ratings of adherence to the program and student participation are high. Despite program exposure issues, ANCOVA results demonstrated significant differences between student cohorts before and after Fundations was introduced. Post hoc analysis indicated that adjusted mean reading scores following Fundations implementation had increased by almost one full reading level compared to two out of the three years prior to the program. In addition, teachers identified professional development and implementation support as areas of need. Recommendations include allocating the minimum instructional time prescribed for Fundations, incorporating other measures of reading and approaches to analyzing reading data, increasing fidelity checks, and providing additional professional development.

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.