Although self-regulated learning has been identified as important for students in academic settings, the construct of teacher self-regulation is less well understood. The literature on teacher self-regulation is reviewed in this dissertation, identifying the weaknesses of studies to date and gaps in the literature. The largest gap is the existence of a valid measure of teacher self-regulation that can be used in the U.S. K-12 teacher population. Without such a measure, the possible relationships between teacher self-regulation and important outcomes like teacher learning, student self-regulation, and student achievement cannot be examined. By collecting evidence from various sources (i.e., expert review, teacher review, teacher responses, factor structure, etc.), this dissertation evaluated the reliability and validity of the English-version of the Teacher Self-regulation Scale (TSRS), which was originally developed and validated in Turkey and has since been validated in the Iranian English-as-Foreign-Language (EFL) teacher population. The TSRS, consisting of 40 items, is based on a theoretical model of self-regulation proposed by Zimmerman (2000) and captures nine factors. A series of confirmatory factor analyses (CFA) were conducted to test the factor structure using responses collected via an anonymous online survey from 923 U.S. K-12 teachers recruited from teacher professional organizations. In addition, the internal consistency of the nine subscales were assessed. In this sample, the nine-factor model did not fit the data well suggesting possible cross-cultural differences. Furthermore, unidimensionality was confirmed for only eight of the nine subscales: emotional control, goal setting, help seeking, intrinsic interest, mastery goal orientation, performance goal orientation, self-evaluation, and self-instruction. Theoretical relationships between teacher self-regulation subscales and another measure of teacher self-regulation, teacher sense of responsibility and teacher self-efficacy were also tested using a series of path analyses. A series of multiple regression analyses identified a number of demographic variables as significant predictors of teacher self-regulation subscales. Across the eight subscales, being a teacher of English/Language Arts and a female were significant predictors of higher TSRS responses, whereas being a native English speaker significantly predicted lower TSRS responses. There was also a significant positive relationship between years of teaching experience and TSRS responses for a number of subscales. Further research is needed to better represent the construct of teacher self-regulation. / A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2015. / June 19, 2015. / Includes bibliographical references. / Alysia D. Roehrig, Professor Directing Dissertation; Elizabeth Jakubowski, University Representative; Jeannine E. Turner, Committee Member; Insu Paek, Committee Member.

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Published Article / The epistemological and ontological orientations relevant for this research are positivism, interpretivism and pragmatism. These paradigms of inquiry are associated with quantitatively oriented research traditions, qualitatively oriented research traditions and mixed methods research respectively. Researchers who use mixed methods build on the strengths of quantitative and qualitative methods and minimize their weaknesses. Since educational research is primarily evidence-based, the aim of the study was to explore the extent to which mixed methods research was reflected in two eminent South African educational research journals during the 11 year period, 2000 to 2010. To this end 1392 articles were analysed. Of the research articles published in the two journals, 17.8% and 15.1% respectively reported on the use of mixed methods. Quantitative methods dominated between 2000 and 2002, followed by a paradigm war in 2003 to 2007, and mainly qualitative methods from 2008 onwards. Mixed methods research was mostly used in the educational domains of didactics (inclusive of curriculum studies), management and social studies. The most dominant themes investigated in these fields were related to curricula and the NQF/OBE, transformation, staff diversity, e-learning and other teaching methods. The need to develop mixed methods research in all branches of social research in South Africa is indicated.

The purpose of this study was to develop a methodological approach using secondary data that researchers, faculty, and staff can utilize to assess student course performance and to identify the input and course environment factors that best predict student course success in an undergraduate lecture capture quantitative methods course. Using Astin and antonio (2012)’s Input Environment and Outcome (IEO) Model as a framework, this quantitative study examined both input variables that students bring to a course as well as the course environment factors that students experience in the course. Three secondary data sources were utilized and analyzed using descriptive and multivariate statistics. The findings revealed that students with higher levels of student course engagement and academic self-concept were more likely to achieve student course success in this lecture capture quantitative methods course. In addition, prior University GPA along with live-class attendance, discussion board posts, and course quiz and exam scores were the strongest predictors of student course success. The largest implication from this study was the methodological approach developed to identify factors that predicted student course success. This approach can be used to help faculty identify course-embedded measures for assessment as well as develop Keys for Success to help future students succeed in difficult courses. While this study added significantly to the limited research on lecture capture courses, future research should further explore qualitative aspects of the course, such as motivation and student video-viewing behaviors, as well as additional impacts on physical attendance in lecture capture courses. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Reading comprehension emerges as an important skill set in the early elementary grades. It is supported by component skills including decoding, linguistic knowledge including vocabulary and syntactic knowledge, as well as more complex, higher-level components such as inference making and comprehension monitoring. Theoretical understanding of reading comprehension has historically included reader-focused models, as well as models that include reader-text interaction and text-task interaction. Together, the dimensions of reader, text, and task represent the conceptual space in which comprehension can occur. Using a crossed random-effects model, the probability of a correct item response can be modeled as a function of reader, text, and item characteristics. This approach has been used in several studies of reading comprehension, with informative results. However, to date this work has focused on older readers, or has used relatively small samples of readers. In this study, a crossed random-effects modeling approach was used to analyze a large data set consisting of item response data from a sample of 2,723 Grade 3 students. Student-level predictors of vocabulary knowledge, syntactic knowledge, and word recognition, as well as several categorizations of item type, and passage-level predictors of lexile and several indices of passage complexity were found to be significant predictors of reading comprehension. Cross-level interactions were investigated, and significant interactions were found between student and item predictors, and between student and passage predictors. Approximately 50% of variance in reading comprehension across students was explained by the student-level predictors, but only 18-22% of variance across items was explained by the passage-level and item-level predictors. Results from this study suggest that for Grade 3 readers, the strong predictive relations between student predictors of syntactic and vocabulary knowledge to reading comprehension may be moderated by some aspects of item and task demands. However, for this large-scale, multiple choice assessment of reading comprehension, variability in items and passages was largely unexplained. Results are discussed in the context of theoretical accounts of reading comprehension, from which the item and passage predictors are derived. / A Dissertation submitted to the School of Communication Science and Disorders in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2017. / July 26, 2017. / Florida Assessments for Instruction in Reading, grade three, item response modeling, multilevel modeling, reader-text interactions, reading comprehension / Includes bibliographical references. / Hugh W. Catts, Professor Directing Dissertation; Christopher Schatschneider, University Representative; Carla Wood, Committee Member; Donald Compton, Committee Member.

This quantitative longitudinal study sought to highlight the difference between the proportion of men and women who planned to pursue a STEM major in the fields of mathematics, natural sciences, engineering, and computer and information sciences as freshmen, as well as to determine the proportion of men and women who changed their major choice by their senior year. In addition, the researcher sought to identify women students’ unique background characteristics and college experiences that have taken place over the course of their undergraduate college career that may have predicted their declared major choice (STEM versus non-STEM) as seniors. A review of the literature, along with Astin’s Involvement Theory, encouraged the hypothesis that college experiences influence women’s college major choice: STEM versus non-STEM. Secondary data obtained from the Cooperative Institutional Research Program at the higher Education Research Institute was used. The sample was delimitated to include only full-time undergraduate students who were graduating in 2012 or 2013. Five research questions were addressed in this study. Astin’s (1993) Input-Environment-Outcome Model was used as a conceptual framework. Descriptive (frequencies and percentages) and inferential (chi-square test and discriminant analysis) statistics were used to analyze the data. The results found a statistically significant difference between the proportion of men and women who planned to pursue a STEM major as freshmen as well as the proportion of men and women who changed their major choice from STEM to non-STEM. Discriminant analysis was used to predict group membership of STEM versus non-STEM major choice among women. It was found that many variables had an impact on predicting STEM group membership among women: satisfaction with college math and science courses, high school GPA, SAT score, high self-ratings of problem-solving skills and mathematical ability, and participating in undergraduate research. There were also variables that had a greater ability of predicting non-STEM group membership. The findings from this study will hopefully inform policy and practice. Implications for policy, practice, and future research are included. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

<p>There is a dire need to transform Mathematics education in the schooling system in South Africa as this is evident by the poor learner performance in the Mathematics examination results. There is a high failure rate in Mathematics at schools and the number of learners taking Mathematics up to the grade twelve level is on the decline. This study investigates the integration of computer technology in Mathematics education to improve learner performance.</p>

<p>The purpose of this study was to examine the level and extent of community participation in the Danga Ecological Sanitation Project carried out in the Zvishavane district of Zimbabwe. The people-centered approach was chosen as a theoretical background. Both quantitative and qualitative methods were used to gather relevant information. The results indicated that the community was not fully involved in the ecological sanitation project. As a result, the project had a poor performance record. In the course of this study, political interference in community projects carried out in Zimbabwean rural communities, resulting in the failure to reach the intended beneficiaries, was also noted. Full community participation in community projects may ensure that empowerment and ownership take place. Institutional arrangements, which in most cases impede development, need to be readdressed with clear demarcation of decision-making processes.</p>

This report presents a quantitative research and trading system (QUARTS) for US equities. After introduction of US stock market structure, it presents the quantitative model concept, specifically, its components and its interactions with different environments. Equipped with a software architecture design discipline that follows three steps -- define the problem; design the solution; and deploy to sites -- it designs the architecture of QUARTS. This is followed by a prototype implementation of research environment. Finally it gives two sample quantitative models to demonstrate the use of research environment. The report includes a detailed survey of Software Architecture and Design Methodologies to help readers to better understand the derivation of QUARTS architecture. / text

Many online retailers monitor visitor traffic as a measure of their storesâ success. However, summary measures such as the total number of visits per month provide little insight about individual-level shopping behavior. Additionally, behavior may evolve over time, especially in a changing environment like the Internet. Understanding the nature of this evolution provides valuable knowledge that can influence how a retail store is managed and marketed. This paper develops an individual-level model for store visiting behavior based on Internet clickstream data. We capture cross-sectional variation in store-visit behavior as well as changes over time as visitors gain experience with the store. That is, as someone makes more visits to a site, her latent rate of visit may increase, decrease, or remain unchanged as in the case of static, mature markets. So as the composition of the customer population changes (e.g., as customers mature or as large numbers of new and inexperienced Internet shoppers enter the market), the overall degree of visitor heterogeneity that each store faces may shift. We also examine the relationship between visiting frequency and purchasing propensity. Previous studies suggest that customers who shop frequently may be more likely to make a purchase on any given shopping occasion. As a result, frequent shoppers often comprise the preferred target segment. We find evidence supporting the fact that people who visit a store more frequently are more likely to buy. However, we also show that changes (i.e., evolution) in an individualâ s visit frequency over time provides further information regarding which customer segments are more likely to buy. Rather than simply targeting all frequent shoppers, our results suggest that a more refined segmentation approach that incorporates how much an individualâ s behavior is changing could more efficiently identify a profitable target segment.