Date of Graduation
College of Physical Activity and Sport Sciences
Physical Education Teacher Education
Emily J Jones
Sean M Bulger
Background/Purpose: Scholars have posited that the demand for online learning is not going away, and the question is no longer if online physical education (OLPE) is practical but rather, what are the most effective ways of administering OLPE to accommodate students (Daum & Buschner, 2012). Currently, limited data are available on student retention rates and attrition factors in OLPE courses. Several early OLPE studies (Brewer, 2001; Mosier, 2010; Ransdell et al., 2008) as well as the 2007 NASPE Initial Guidelines for Online Physical Education have suggested that certain prescreening efforts be in place prior to student enrollment in OLPE, however, at present no such empirically sound and theoretically based screening instruments exist. Screening and pre-screening systems can help identify students who are at risk of failing and/or not completing online coursework. The purpose of the study is to identify online student cognitive characteristics and environmental factors associated with success and/or failure within college online health-related fitness (HRF) courses. Methods/Analysis: Students (N=821) enrolled in Auburn University's 16-week online HRF course---Active Auburn--- during the Fall 2017 participated in the study. At the beginning of the course, participants responded to two previously validated research instruments, the Educational Success Prediction Instrument Version-2 (ESPRI-V2; Roblyer, et al., 2008) and the Distance Learning Survey (DLS; Osborn, 2001). A Pearson's Chi Square analysis was used for student demographic and environmental categorical data. Next, a one-way between subjects analysis of variance (ANOVA) was employed to compare completers and non-completers mean scores for each ESPRI-V2 and DLS cognitive factor (i.e. study environment). Lastly, a direct binary logistic regression was performed to assess the impact of significant factors from the previous analysis on the likelihood that student would complete or not complete an online HRF course. Results: The model contained 6 independent variables (GPA, class standing, hours worked outside of school, achievement, organization and study environment). The full model containing all predictors was statistically significant (&khgr; 2 (6, N=821) = 94.296, p<.001), indicating that the model was able to distinguish between students who completed and did not complete the online HRF course. Four of the independent variables made a unique statistically significant contribution to the model: (1) GPA, (2) Class Standing, (3) Hours Worked Outside of School and (4) Organization. The strongest predictor of a course completion were student who reported entering the course with a GPA of 2.6- 4.0, recording an odds ratio of 3.96. This indicated that students who entered the course with a GPA above a 2.6 were almost 4 times more likely to complete an online HRF course than those who entered with a lower GPA, controlling for all other factors in the model. Conclusion: Upon course entry, students who did not complete the course generally reported a combination of the following factors: GPA below 2.6, worked more than 20 hours outside of school, underclassman class standing, and reported weak organizational beliefs. This analysis provides an initial understanding of the unique student characteristics affecting online HRF course completion.
Goad, Tyler, "Predicting Student Success in Online Physical Education" (2018). Graduate Theses, Dissertations, and Problem Reports. 5684.