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

The purpose of this research was to determine whether student academic achievement in college computer science programs in the Republic of China (ROC) could be predicted by factors reported to be effective in US studies. The relationship between these factors and course performance in computer science programs was examined. Gender differences were also interrogated. Sophomore, junior, and senior students enrolled in five universities offering computer science programs in the ROC constituted the population. A researcher-designed questionnaire was used to collect background information. Validity and reliability issues were addressed by the conduct of validity assessment, questionnaire pilot testing, and interviews with selected pilot test subjects. Scores from the College Entrance Examination (CEE) and college computer science courses were accessed through university registrar's offices. A total of 940 questionnaires were collected, representing more than 81% of the population. From data analysis, the predictive powers of CEE test scores in relation to subsequent college performance appeared to be limited. The CEE math component was negatively correlated to performance in college computer science programs. The positive relation of math ability to academic achievement in complete computer science programs was confirmed. High school overall achievement as well as math course averages were identified as effective performance predictors for college computer science programs. Prior computer experience showed no conclusive relationship to subsequent performance in college computer science courses. The close relationship between performance in beginning computer science courses and performance in complete computer science programs was validated. Significant linear prediction models with limited predictive powers (R2 ranged from 0.19 to 0.30) were generated for overall performance, but not for introductory computer science course performance. Model predictive powers were significantly improved (R2 range from 0.59 to 0.63) when performance in introductory computer science courses was included in the models. Significant gender differences were not found for CEE performance, prior computer experience, and prediction models. However, female subjects outperformed male counterparts in course performance at both the high school and college levels. / Graduation date: 1996

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (Ph. D.)--Florida State University, 2003. / Advisor: Dr. Barbara Licht, Florida State University, Dept. of Psychology. Title and description from dissertation home page (viewed Apr. 8, 2004). Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 238-251). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 144-153). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 174-188). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 126-134). Available also in a digital version from Dissertation Abstracts.

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