Using Academic & Non-Academic Factors to Predict Student Course Success in a Lecture Capture Quantitative Business Course

Concurrent Session 6

Session Materials

Brief Abstract

The purpose of this quantitative study is to develop a methodology utilizing secondary data to measure student course performance in a lecture capture quantitative business course and to identify academic and/or non-academic factors that predict student course success in a lecture capture quantitative business course.

Presenters

onathan Sweet is a Ph.D. candidate in the educational leadership program at Florida Atlantic University in Boca Raton, Florida. Jonathan has his bachelor’s degree in business administration with a minor in business law. He also has a master’s degree in business administration with a concentration in operations management as well as a master’s degree in educational leadership. While working his way through school, Jonathan worked as a supplemental instructor and e-tutor for a variety of undergraduate business courses. He also was a teaching assistant for a large lecture capture video streaming business course for several semesters. After finishing his MBA, Jonathan began to teach as an adjunct professor at Florida Atlantic University and Lynn University before starting his Ph.D. in 2014. Jonathan currently is a visiting instructor at Florida Atlantic University in the department of information technology and operations management and teaches courses both in traditional and fully-online formats. His current research interests are in the area of distance learning formats and ways to improve student success in quantitative distance learning courses.

Tags

Analytics

Extended Abstract

The U.S. Department of Education reports that, as of Fall 2014, approximately 28% of all students enrolled at a degree-granting postsecondary institution were enrolled in at least one distance education course; up two percent from Fall 2013 (United States Department of Education, 2016). However, while enrollment may be increasing, one growing concern for these distance education courses is high course attrition rates (Xu & Jaggars, 2011; Lynch, 2001; Carr, 2000). Quantitative courses are often to blame for these attrition rates, as most students, particularly business students, historically struggle with these courses due to complex nature of the course material (Yousef, 2011; Brookshire & Palocsay, 2005). At one particular state university in Florida, approximately 30% of the students enrolled in a quantitative business course either failed or withdrew from the course by the end of the semester (Davis, 2009). In addition, Stevens and Zhu (2015) found that business students enrolled in an online quantitative course on average scored a half letter grade lower than students in the traditional section. Therefore, the purpose of this study is two-fold: 1) to develop a methodology utilizing secondary data to measure student course performance in a lecture capture quantitative business course; and 2) to identify academic and/or non-academic factors that predict student course success in a lecture capture quantitative business course. Specifically, the research questions guiding this study include the following: What is the frequency and variation of scores on student course success for students in a lecture capture quantitative business course? To what extent does student engagement differ between students who achieve course success and students who do not achieve course success? What academic or non-academic factor(s) best predict student course success? Using a quantitative non-experimental ex post facto design, the target population includes undergraduate business students attending a public research university in Florida. The sample is delimited to students enrolled in the lecture capture section of the Quantitative Methods in Administration Course during the Fall 2016 semester. The data sources for this study will include administrative data from the course’s Blackboard (course management system) page, demographic and academic data from the University registrar data, and data collected from the Student Course Engagement Questionnaire (SCEQ). The data for this study will be analyzed using descriptive and multi-variate statistics. The dependent variable is student course success. The independent variables include background characteristics (e.g., gender, race/ethnicity), academic factors (e.g., GPA, course exam/quiz scores), and non-academic factors (e.g., attendance, discussion board posts, supplemental instruction). The study is significant because it will advance a methodological approach to inform faculty and researchers on how to assess lecture capture courses using secondary data. Additionally, the results will inform faculty, administrators, and academic advisors with insight on key course success factors that can be used to help improve course and program retention as well as graduation rates.