Exploration of Factors Related to Persistence of Students in Online Undergraduate Programs at a Four-year Public Institution

Concurrent Session 4

Brief Abstract

While online education is on the rise, attrition of these students has remained a significant issue. This study addresses what factors can predict persistence in online undergraduate programs at a four-year institution. The factors that were explored included pre-college academic characteristics, demographic characteristics, academic performance, and learner behavior of online students.

Presenters

Jean E. Starobin is the Associate Director of Student Success & Engagement for UF Online, University of Florida. Before joining UF Online as their Associate Director, she was the Director of Enrollment Management and Online Programs for the Heavener School of Business at the University of Florida. Jean completed her doctorate in higher education administration and policy in 2014.

Additional Authors

Extended Abstract

This presentation focuses on theme Research: Designs, Methods, and Findings. The intent of this session is to explore the critical factors that predict retention and graduation at a large, public university for online undergraduate students using existing data sources. In the 2015 Online Report Card (Allen et al., 2016), the growth of distance education has been shown to have risen substantially.  Additionally, attrition levels for online students remain a significant challenge. The presentation will take place in two parts: literature review of existing models for student persistence and data analysis. More specifically, the literature review will include discussions on the landscape of online and distance education, a historical review of persistence models with a focus on how they relate to distance students (Bean & Metzner, 1985; Pascarella & Terenzini, 1991; Rovia, 2003; Tinto,1975), the definition of a nontraditional student (Bean & Metzner, 1985), and factors that relate to distance student success (Dupin-Bryant, 2004; Hart, 2012).  The data analysis component will methodically walk the participants through the steps employed from data collection, analysis, logistic regression modelling, and final recommendations. This study examines the factors related to student success by creating portraits of non-retained students versus graduated and retained students. Logistic regression models will illustrate the key factors that were significant predictors for retention or graduation. Recommendations will follow and are intended to guide and help academic advisors and staff who work with and support online and distance students.

The outcomes of this session are for participants to both understand how data analysis paired with an understanding of their population of students can help institutions make informed recommendations to positively impact their students’ success. The goals of this presentation are the following: (1) to reflect on Persistence and Student Success Models as they pertain to online and distance students, (2) to demonstrate a method or a blueprint in which an institution can harness their data to understand the learner behaviors of their students, (3) to discuss the landscape of online learning, and (4) to engage participants in a discussion concerning critical factors related to online student success and how they have addressed them at their own institution.

 

References

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Allen, E.I., Seaman, J. Poulin, R., & Straut, T. T. (2016). 2015 Online Repord Card – Tracking Online Education in the United States. Retrieved from on August 11, 2016 http://onlinelearningconsortium.org/read/online-report-card-tracking-online-education-united-states-2015/

Ashar, H., & Skenes, R. (1993). Can Tinto’s Student Departure Model be Applied to Nontraditional Students? Adult Education Quarterly, 43, 90-100.

Bean, J., & Metzner, B. (1985). A Conceptual Model of Nontraditional Undergraduate Student Attrition, Review of Educational Research, 55, 485-650.

Carr, S. (2000). As Distance Education Comes of Age, the Challenge is Keeping the Students.  Chronicle of Higher Education, A39 – A41.

Carlson-Sabelli, L.L., Giddens, J. Fogg, L., & Fiedler, R. A. (2011). Challenges and Benefits of Using a Virtual Community to Explore Nursing Concepts Among Baccalaureate Nursing Students. International Journal of Nursing Education Scholarship, 8(1), 1-14.

DiRamio, D., & Wolverton, M. (2006) Integrating Learning Communities and Distance Education: Possibility or Pipedream, Innovative Higher Education, 31(2), 99-113.

Dupin-Bryant, P.A., (2004). Pre-entry Variables Related to Retention in Online Distance Education. American Journal of Distance Education, 18(4), 199-206.

Hart, C., (2012). Factors Associated with Student Persistence in an Online Program of Study: A Review of the Literature. Journal of Interactive Online Learning, 11(1), 19-42.

Pascarella, E.T., & Terenzini, P.T., (1991) How College affects students: findings and insights from twenty years of research. San Francisco: Jossey – Bass.

Rovai, A. P. (2003). In Search of Higher Persistence Rates in Distance Education Online Programs. Internet and Higher Education, 6, 1-16.

Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45, 89-125.

Tinto, V. (1982). Limits of Theory and Practice in Student Attrition. Journal of Higher Education, 53(6), 687-700.

Tinto, V. (1997). Colleges as communities: exploring the education character of student persistence. Journal of Higher Education, 68(6), 599-623.

Tinto, V. (1998). Colleges as communities: taking research on student persistence seriously. Review of Higher Education, 21(2), 167-177.

Tinto, V. (2005). Epilogue: moving from theory to action. In A. Seidman (Ed.), College student retention: Formula for student success. Westport, CT: ACE/Praeger.

Xenos, M. (2004). Prediction and Assessment of Student Behavior in Open and Distance Education in Computers Using Bayesian Networks.  Computers & Education, 43(4), 345-359.