Date of Award
2018
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
DeDonno, Michael
Abstract
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.
Extent
263 p.
Recommended Citation
Sweet, Jonathan A., "Predicting Undergraduate Student Course Success in a Lecture Capture Quantitative Methods Course" (2018). Electronic Theses and Dissertations 2010 - 2019. 1993.
https://digitalcommons.fau.edu/etd_general_6/1993
Identifier
FA00005988
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2018.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.