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The Role of Student Characteristics in Predicting Retention in Online Courses

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Abstract

Given the continued issue of student retention for online classes, past research has suggested several “retention strategies” focused on engaging students as a way to reduce their withdrawal rate from these classes. However, a recent study testing the effects of these strategies on retention in online undergraduate business courses (Leeds et al., Int J Manage Educ 7(1/2), 2013) did not show empirical support for the effectiveness of such strategies. Taking an alternative approach that focuses on individual characteristics of students, this study takes a broader view and examines previous research literature on traditional face-to-face classes to determine how individual characteristics of students may be associated with the likelihood of withdrawal from online classes. Using a sample of undergraduate students (n = 2,314) from a large state university, results from this study identified prior performance in college classes (cumulative GPA) and class standing (senior vs. non-senior) as significant student characteristics related to student retention in online classes for all students. Other factors significantly related to retention rates for students with certain characteristics or within certain majors include previous withdrawal from online courses, gender, and receipt of academic loans.

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References

  • Allen, I., & Seaman, J. (2010). Staying the course: Online education in the United States. Needham, MA: Sloan Consortium.

    Google Scholar 

  • Altonji, J. G. (1993). The demand for and return to education when education outcomes are uncertain. Journal of Labor Economics, (1), 48.

  • Angelino, L., Williams, F., & Natvig, D. (2007). Strategies to engage online students and reduce attrition rates. The Journal of Educators Online, 4(2), 1–14.

    Google Scholar 

  • Arbaugh, J. B., & Benbunan-Fich, R. (2006). An investigation of epistemological and social dimensions of teaching in online learning environments. Academy of Management Learning & Education, 5(4), 435–447.

    Article  Google Scholar 

  • Bean, J. (1980). Dropouts and turnover: The synthesis and a test of a causal model of student attrition. Research in Higher Education, 12, 155–187.

    Article  Google Scholar 

  • Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540.

    Article  Google Scholar 

  • Bergman, J. Z., Westerman, J. W., & Daly, J. P. (2010). Narcissism in management education. Academy of Management Learning & Education, 9(1), 119–131.

    Article  Google Scholar 

  • Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 46(23), A39–A41.

    Google Scholar 

  • DesJardins, S., Ahlburg, D., & McCall, B. (2002). A temporal investigation of factors related to timely degree completion. The Journal of Higher Education, 73, 555–582.

    Article  Google Scholar 

  • Dobbs, R. R., Waid, C. A., & del Carmen, A. (2009). Students’ perceptions of online courses: The effect of online course experience. Quarterly Review of Distance Education, 10(1), 9–26.

    Google Scholar 

  • Frydenberg, J. (2007). Persistence in University Continuing Education Online Classes. International Review of Research in Open and Distance Learning, 8(3), 1–15.

    Google Scholar 

  • Horn, L. (1998). Stopouts or stayouts? Undergraduates who leave college their first year. Washington, DC: U.S. Department of Education.

    Google Scholar 

  • Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression. New York, NY: Wiley.

    Book  Google Scholar 

  • Ishitani, T. T., & DesJardins, S. L. (2002–2003). A longitudinal investigation of dropout from college in the United States. Journal of College Student Retention, 4, 173–201.

    Google Scholar 

  • Leeds, E., Campbell, S., Baker, H., Ali, R., & Crisp, J. (2013). the impact of student retention strategies: An empirical study. International Journal of Management in Education, 7(1/2), 22–43.

    Google Scholar 

  • Light, A. (1996). Hazard model estimates of the decision to reenroll in school. Labor Economics, 2, 381–406.

    Article  Google Scholar 

  • Metzner, B. S., & Bean, J. P. (1987). The estimation of a conceptual model of nontraditional undergraduate student attrition. Research in Higher Education, 27(1), 15–38.

    Article  Google Scholar 

  • Murtaugh, P., Burns, L., & Schuster, J. (1999). Predicting the retention of university students. Research in Higher Education, 40, 355–371.

    Article  Google Scholar 

  • Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. Internet and Higher Education, 6(1), 1–16.

    Article  Google Scholar 

  • Singell, L. (2004). Come and stay a while: Does financial aid effect retention conditioned on enrollment at a large public university? Economics of Education Review, 23(5), 459–471.

    Article  Google Scholar 

  • Singell, L., & Waddell, G. (2010). Modeling retention at a large public university: Can at-risk students be identified early enough to treat? Research in Higher Education, 51, 546–572.

    Article  Google Scholar 

  • St. John, E., Hu, S., & Weber, J. (2001). State policy on the affordability of public higher education: The influence of state grants on persistence in Indiana. Research in Higher Education, 42, 401–428.

    Article  Google Scholar 

  • Stratton, L., O’Toole, D., & Wetzel, J. (2007). Are the factors affecting dropout behavior related to initial enrollment intensity for college undergraduates? Research in Higher Education, 48(4), 453–485.

    Article  Google Scholar 

  • Thomas, S. L., & Zhang, L. (2005). Post-baccalaureate wage growth within 4 years of graduation: The effects of college quality and college major. Research in Higher Education, 46(4), 437–459.

    Article  Google Scholar 

  • Tinto, V. (1975). Dropout from higher education. Review of Educational Research, 45, 89–125.

    Article  Google Scholar 

  • Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition. Chicago; London: University of Chicago Press.

    Google Scholar 

  • Tinto, V. (2006). Research and practice of student retention: What next? Journal of College Student Retention, 8(1), 1–20.

    Article  Google Scholar 

  • Willging, P., & Johnson, S. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127.

    Google Scholar 

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Correspondence to Justin D. Cochran.

Appendix

Appendix

See Table 9.

Table 9 Definitions of study variables

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Cochran, J.D., Campbell, S.M., Baker, H.M. et al. The Role of Student Characteristics in Predicting Retention in Online Courses. Res High Educ 55, 27–48 (2014). https://doi.org/10.1007/s11162-013-9305-8

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