<p dir="ltr">Fearlessness is a construct often discussed in clinical and personality psychology. However, at the self-report trait level, there is little work focusing on its empirical structure and how that applies to measurement. The present study examined the IPIP-Fearlessness scale using experience sampling methodology to examine how scores on the measure predict behaviors in daily life. Using a pre-registered analytical approach, participants completed a baseline survey and brief daily surveys six times daily for one full week. The final sample consisted of 241 participants. Criterion variables measuring boldness, general personality, sensation seeking, and sensitivity to reward and punishment were also correlated with the IPIP-Fearlessness measure’s subscales, and a confirmatory factor analysis confirmed the measure’s structure.</p><p dir="ltr">Results showed that two of the IPIP-Fearless subscales (Low Anxiety and Sociability) did predict daily behaviors, whereas there were not significant predictions found for the other behaviors. Affect (negative, positive, anxious, bored) was also found to be significant predictor for several of the behavioral outcome variables, as well as interpersonal status. Additional exploratory analyses were also conducted. The findings from this study continue to elucidate how we can use empirically derived self-report trait fearlessness, and its relationship to additional constructs and behaviors.</p>

<p dir="ltr">A major challenge to promoting effective early science and engineering education is the lack of reliable and validated assessments that align with current educational guidelines for science and engineering. Existing early science and engineering assessments either cover a narrow range of concepts and practices and/or are not designed in a way to evaluate and provide information within theorized dimensions of science and engineering knowledge and skills. The goals of this study were to develop a preschool science and engineering assessment and to examine the factor structure of children’s science and engineering knowledge and skills using the newly developed assessment. A 120-item assessment was developed and administered to 186 children (50.28% female) ages 3-to-5 years (<i>M </i>= 4.62 years, <i>SD</i> = 0.61 years). The overall best fitting structure of the assessment was found to be a three-dimensional model: disciplinary core ideas, science and engineering practices, and crosscutting concepts. Items that had low correlations with the overall test, loaded poorly onto their respective factors, or were found to provide overlapping information with other items (i.e., exhibited similar difficulties for the same content areas) were removed, resulting in a final and brief (48-item) version of the assessment. This study has important implications in that the newly developed science and engineering assessment can be used in both the research (e.g., evaluate curricula, interventions) and classroom (e.g., assess learning) settings to provide information at the dimension-level, and has the potential to transform how we view and instruct science and engineering during the early childhood years.</p>

<p dir="ltr">It has been shown that formative assessments can lead to improvements in the learning process. Cognitive Diagnostic Models (CDMs) are a powerful formative assessment tool that can be used to provide individuals with valuable information regarding skill mastery in educational settings. These models provide each student with a ``skill mastery profile'' that shows the level of mastery they have obtained with regard to a specific set of skills. These profiles can be used to help both students and educators make more informed decisions regarding the educational process, which can in turn accelerate learning for students. However, despite their utility, these models are rarely used with small sample sizes. One reason for this is that these models are often complex, containing many parameters that can be difficult to estimate accurately when working with a small number of observations. This work aims to contribute to and expand upon previous work to make CDMs more accessible for a wider range of educators and students.</p><p dir="ltr">There are three main small sample statistical problems that we address in this work: 1) accurate estimation of the population distribution of skill mastery profiles, 2) accurate estimation of additional model parameters for CDMs as well as improved classification of individual skill mastery profiles, and 3) improved selection of an appropriate CDM for each item on the assessment. Each of these problems deals with a different aspect of educational measurement and the solutions provided to these problems can ultimately lead to improvements in the educational process for both students and teachers. By finding solutions to these problems that work well when using small sample sizes, we make it possible to improve learning in everyday classroom settings and not just in large scale assessment settings.</p><p dir="ltr">In the first part of this work, we propose novel algorithms for estimating the population distribution of skill mastery profiles for a popular CDM, the Deterministic Inputs Noisy ``and'' Gate (DINA) model. These algorithms borrow inspiration from the concepts behind popular machine learning algorithms. However, in contrast to these methods, which are often used solely for prediction, we illustrate how the ideas behind these methods can be adapted to obtain estimates of specific model parameters. Through studies involving simulated and real-life data, we illustrate how the proposed algorithms can be used to gain a better picture of the distribution of skill mastery profiles for an entire population students, but can do so by only using a small sample of students from that population. </p><p dir="ltr">In the second part of this work, we introduce a new method for regularizing high-dimensional CDMs using a class of Bayesian shrinkage priors known as catalytic priors. We show how a simpler model can first be fit to the observed data and then be used to generate additional pseudo-observations that, when combined with the original observations, make it easier to more accurately estimate the parameters in a complex model of interest. We propose an alternative, simpler model that can be used instead of the DINA model and show how the information from this model can be used to formulate an intuitive shrinkage prior that effectively regularizes model parameters. This makes it possible to improve the accuracy of parameter estimates for the more complex model, which in turn leads to better classification of skill mastery. We demonstrate the utility of this method in studies involving simulated and real-life data and show how the proposed approach is superior to other common approaches for small sample estimation of CDMs.</p><p dir="ltr">Finally, we discuss the important problem of selecting the most appropriate model for each item on assessment. Often, it is not uncommon in practice to use the same CDM for each item on an assessment. However, this can lead to suboptimal results in terms of parameter estimation and overall model fit. Current methods for item-level model selection rely on large sample asymptotic theory and are thus inappropriate when the sample size is small. We propose a Bayesian approach for performing item-level model selection using Reversible Jump Markov chain Monte Carlo. This approach allows for the simultaneous estimation of posterior probabilities and model parameters for each candidate model and does not require a large sample size to be valid. We again demonstrate through studies involving simulated and real-life data that the proposed approach leads to a much higher chance of selecting the best model for each item. This in turn leads to better estimates of item and other model parameters, which ultimately leads to more accurate information regarding skill mastery. </p>

<p>I collected two types of data throughout Weeks 1-14, with the original purpose of enhancing teaching and learning in ENGL620. The data included weekly assignment recordings and weekly surveys.</p><p>The primary data were students' speech data, which were collected through 14 weekly timed speaking assessments conducted from Week 1 to Week 14. These assignments were made available on Monday at midnight and were required to be completed and submitted by Sunday at midnight). The assignments were delivered, and responses were collected using Extempore (<a href="http://www.extemporeapp.com/" target="_blank">www.extemporeapp.com</a>), a website specifically designed to support oral English assessment and practice.</p><p>To conduct more comprehensive assessments of students’ performances, I incorporated two OEPT item types into the weekly assignments, including PROS and CONS (referred to as “PC”) and LINE GRAPH (referred to as “LG”). See Appendix B for the assignment items. The PC item presented challenging scenarios ITAs may encounter and required the test-takers to make a decision and discuss the pros and cons associated with the decision. An example item is “<i>Imagine you have a student who likes to come to your office hours but often talks about something irrelevant to the course. What would you do in this situation? What are the pros and cons associated with the decision?</i>”. The LG item asked students to describe a line graph illustrating two or three lines and provide possible reasons behind those trends. It can be argued that the two tasks targeted slightly different language abilities and background knowledge. The two item types were selected because they represented two key skills that the OEPT tests. The PC task focused on stating one’s decision and presenting an argument within a personal context, while the LG item assessed students’ ability to describe visual information and engage in discussions about broader topics such as gender equality, employment, economic growth, college policy. The PC and LG items are the most difficult items in the test (Yan et al., 2019). Therefore, progress in the two tasks can be a good indicator of improvement in the speaking skills required in this context. All the items were either taken from retired OEPT items or developed by the researcher following the specifications for OEPT item development. In particular, the design of the items aimed to avoid assuming prior specific knowledge and to ensure that students could discuss them without excessive cognitive load.</p><p>For each task, the students were allocated 2 minutes for preparation and a maximum of 2 minutes to deliver their response to the assigned topic. The responses were monologic, resembling short classroom presentations. During the preparation time, the participants were permitted to take notes. Each item only allowed for one attempt, which aimed to capture students’ online production of speech and their utilization of language resources. Table 2 presents the descriptive statistics of the responses.</p><p>The PC prompt was deliberately kept consistent for Week 2 and Week 12 randomly selected as time points at the beginning and end of the semester. This deliberate choice of using the same prompt at these two distinct stages serves multiple purposes. Firstly, it provides a valuable perspective for analyzing growth over time. This approach adds depth to the study results and conclusions by providing additional evidence and triangulation. Second, this approach addresses one of the specific challenges identified by Ortega and Iberr-Shea (2005) in studies involving multiple data collection points, as maintaining consistency in the prompt can minimize potential variations in task difficulty or topic-related factors.</p><p>After completing each speaking assignment, the students were requested to rate the level of difficulty for each item on a scale of 1 (Very Easy) to 5 (Very difficult). Additionally, they were asked to fill out a weekly survey using Qualtrics. The Qualtrics survey contained six questions related to the frequency of their English language use outside of the classroom and their focus on language skills in the previous and upcoming week. These questions were considered interesting as potential contributing factors to changes in their performances throughout the semester. Refer to Appendix C for the survey questions.</p>

<p dir="ltr">This dissertation is a two-study dissertation divided into two chapters focused broadly on the help-seeking experiences of racial-ethnic minority caregivers of children with autism spectrum disorder (ASD). In chapter one, a systematic literature review was conducted to identify articles that have studied barriers in help-seeking for racial-ethnic minority caregivers of children with autism. A broad literature search across four databases was conducted (i.e., PubMed, PsycINFO, Education Resources Information Center, and Child Development and Adolescent Studies). The coding team identified 17 articles on help-seeking barriers for racial-ethnic minority caregivers of children with autism. A thematic analysis was then used to synthesize the help-seeking barriers identified across these 17 studies. Four themes emerged from our findings: logistical barriers, provider competence, ASD literacy, and cultural stigma. We also provided clinical recommendations for healthcare providers working with families with racial-ethnic minority caregivers of children with autism.</p><p dir="ltr">The second chapter was informed by the results found in chapter one. In chapter one, we found little research on Asian American caregiver perspectives on help-seeking barriers to autism services. Using caregiver perspectives, this research study sought to understand the help-seeking experiences of Asian American families. In this study, we conducted semi-structured qualitative interviews with 10 Asian American caregivers with a child aged 3-17 diagnosed with ASD. Interviews were conducted virtually, audio recorded, transcribed, and coded by three researchers. Data was analyzed using thematic analysis (Braun & Clarke, 2006). Our results indicated four themes in perceived barriers by Asian American caregivers of children with autism interviewees. Themes included: (1) logistical barriers, (2) provider level barriers competence, (3) ASD literacy, and (4) cultural stigma. We deliver clinical recommendations for providers to address the four barriers found in our study when working with Asian American families of children with ASD.</p>