This research sought to determine if a significant relationship existed between various levels of dogmatism of supervising teachers and student teachers and the degree of change in attitude of the student teachers during student teaching.

The problem of this study concerned the difference in relationships between major teaching behaving styles and dominant personality interests expressed in values.

The problem of this study was to determine the effects of student teaching upon student-centeredness and openmindedness.

Thesis advisor: Henry Braun / Informed by Astin's Input-Environment-Outcome (I-E-O) model and Pascarella's general model, this study explored the nature of student-athletes' engagement in educationally purposeful activities, described their engagement patterns, and revealed the relationships between student engagement factors and college outcomes by class and gender for 2596 student-athletes from 30 Division-I institutions. This research demonstrated that the NSEE Five Benchmarks constructed for the general population did not fit student-athletes. Therefore, engagement factors for student-athletes were constructed based on a subset of component items from the Five Benchmarks. Hierarchical Linear Models (HLM) were then applied to National Survey for Student Engagement (NSSE) 2006 and the aggregated school level data from the NCAA. The research results reveal that the association patterns between engagement factors and college outcome variables Satisfaction (SA), General Education and Personal Competence (GEPC), and Personal and Social Development (PSD) across all class and gender subgroups are very similar, but differ from those for GPA. This research concludes that engagement in educationally purposeful activities is the best predictor for student-athletes' college outcomes (except GPA). The analyses also reveal that what students do on campus contributes more to their college outcomes than who they were at matriculation and which school they attend. In particular, for all outcomes, the fraction of the total variance due to between-school differences was very small and the relationships between the coefficients of school-level equations and school-level characteristics were inconsistent. The results of this study, along with other related studies, can help colleges devise strategies to better fulfill their primary obligation to create genuine educational opportunities for their student-athletes through fostering their holistic development. / Thesis (PhD) — Boston College, 2013. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 164-173).

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2007. / Includes bibliographical references.

Thesis (Ph.D.)--Bowling Green State University, 2009. / Document formatted into pages; contains xvii, 163 p. Includes bibliographical references.

Thesis (Ph. D.)--Michigan State University. Dept. of Music Education, 2008. / Title from PDF t.p. (viewed on Mar. 30, 2009). Includes bibliographical references (p. 240-247). Also issued in print.

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2008. / Includes bibliographical references.