Advertisement

Educational Technology Research and Development

, Volume 59, Issue 5, pp 593–618 | Cite as

A review of online course dropout research: implications for practice and future research

  • Youngju Lee
  • Jaeho Choi
Research Article

Abstract

Although online learning is expanding in availability and popularity, the high dropout rates remain a challenging problem. In this paper, we reviewed the existing empirical studies on online course dropouts in post-secondary education that were published during the last 10 years. We identified 69 factors that influence students’ decisions to dropout and classified them into three main categories: (a) Student factors, (b) Course/Program factors, and (c) Environmental factors. We then examined the strategies proposed to overcome these dropout factors: (a) understanding each student’s challenges and potential, (b) providing quality course activities and well-structured supports, and (c) handling environmental issues and emotional challenges. Finally, we discussed issues regarding dropout factors and strategies for addressing these factors and offered recommendations for future research.

Keywords

Dropout factors Online course Strategies Higher education Future research 

References

*These references make up the 35 past empirical studies that we reviewed

  1. Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States. Needham, MA: Sloan Consortium.Google Scholar
  2. *Bocchi, J., Eastman, J. K., & Swift, C. O. (2004). Retaining the online learner: Profile of students in an online MBA program and implications for teaching them. Journal of Education for Business, 79(4), 245–253.Google Scholar
  3. *Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46, 39–42.Google Scholar
  4. *Castles, J. (2004). Persistence and the adult learner: Factors affecting persistence in Open University students. Active Learning in Higher Education, 5(2), 166–179.Google Scholar
  5. *Cheung, L. L. W., & Kan, A. C. N. (2002). Evaluation of factors related to student performance in a distance-learning business communication course. Journal of Education for Business, 77(5), 257.Google Scholar
  6. *Chyung, S. Y. (2001). Systematic and systemic approaches to reducing attrition rates in online higher education. American Journal of Distance Education, 15(3), 36–49.Google Scholar
  7. *Clay, M. N., Rowland, S., & Packard, A. (2009). Improving undergraduate online retention through gated advisement and redundant communication. Journal of College Student Retention: Research, Theory and Practice, 10(1), 93–102.Google Scholar
  8. Creswell, J. W. (2008). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Upper Saddle River, New Jersey: Pearson Prentice Hall.Google Scholar
  9. Diaz, D. P. (2002). Online drop rate revisited. The technology source, May/June. Retrieved from http://technologysource.org/issue/2002-05/.
  10. *Drouin, M. A. (2008). The relationship between students’ perceived sense of community and satisfaction, achievement, and retention in an online course. Quarterly Review of Distance Education, 9(3), 267–284.Google Scholar
  11. *Dupin-Bryant, P. (2004). Pre-entry variables related to retention in online distance education. American Journal of Distance Education, 18(4), 199–206.Google Scholar
  12. Finnegan, C., Morris, L. V., & Lee, K. (2009). Differences by course discipline on student behavior, persistence, and achievement in online courses of undergraduate general education. Journal of College Student Retention: Research, Theory and Practice, 10(1), 39–54.CrossRefGoogle Scholar
  13. *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
  14. Harasim, L. (2000). Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education, 3(1–2), 41–61.CrossRefGoogle Scholar
  15. *Holder, B. (2007). An investigation of hope, academics, environment, and motivation as predictors of persistence in higher education online programs. Internet and Higher Education, 10(4), 245–260.Google Scholar
  16. Greene, J. C., & Caracelli, V. J. (1997). Defining and describing the paradigm issue in mixed-method evaluation. In J. C. Greene & V. J. Caracelli (Eds.), Advances in mixed-method evaluation: The challenges and benefits of integrating diverse paradigms (pp. 5–17). San Francisco, CA: Jossey-Bass.Google Scholar
  17. *Ivankova, N. V., & Stick, S. L. (2007). Students’ persistence in a distributed doctoral program in educational leadership in higher education: A mixed methods study. Research in Higher Education, 48(1), 93–135.Google Scholar
  18. Kember, D. (1995). Open learning courses for adults: A model of student progress. Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar
  19. *Kemp, W. C. (2002). Persistence of adult learners in distance education. American Journal of Distance Education, 16(2), 65.Google Scholar
  20. *Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers and Education, 48(2), 185–204.Google Scholar
  21. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, GA: Sage Publications.Google Scholar
  22. *Liu, S. Y., Gomez, J., & Cherng-Jyh, Y. (2009). Community college online course retention and final grade: Predictability of social presence. Journal of Interactive Online Learning, 8(2), 165–182.Google Scholar
  23. *Moore, K., Bartkovich, J., Fetzner, M., & Ison, S. (2003). Success in cyberspace: Student retention in online courses. Journal of Applied Research in the Community College, 10(2), 12.Google Scholar
  24. Moore, M., & Kearsley, G. (1996). Distance education: A system view. Belmont, CA: Wadsworth.Google Scholar
  25. *Morgan, C. K., & Tam, M. (1999). Unravelling the complexities of distance education student attrition. Distance Education, 20(1), 96–108.Google Scholar
  26. *Morris, L. V., Finnegan, C., & Wu, S. (2005a). Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education, 8(3), 221–231.Google Scholar
  27. *Morris, L. V., Wu, S., & Finnegan, C. L. (2005b). Predicting retention in online general education courses. American Journal of Distance Education, 19(1), 23–36.Google Scholar
  28. *Muilenburg, L. Y., & Berge, Z. L. (2001). Barriers to distance education: A factor analytic study. The American Journal of Distance Education, 11(2), 39–54.Google Scholar
  29. *Müller, T. (2008). Persistence of women in online degree-completion programs. International Review of Research in Open and Distance Learning, 9(2), 1–18.Google Scholar
  30. *Osborn, V. (2001). Identifying at-risk students in videoconferencing and web-based distance education. American Journal of Distance Education, 15(1), 41–54.Google Scholar
  31. *Packham, G., Jones, P., Miller, C., & Thomas, B. (2004). E-learning and retention: Key factors influencing student withdrawal. Education Training, 46(6/7), 335–342.Google Scholar
  32. *Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2), 1–10.Google Scholar
  33. *Parker, A. (2003). Identifying predictors of academic persistence in distance education. United States Distance Learning Association Journal, 17(1), 55–61.Google Scholar
  34. *Perry, B., Boman, J., Care, W. D., Edwards, M., & Park, C. (2008). Why do students withdraw from online graduate nursing and health studies education? Journal of Educators Online, 5(1), 1–17.Google Scholar
  35. *Pierrakeas, C., Xenos, M., Panagiotakopoulos, C., & Vergidis, D. (2004). A comparative study of dropout rates and causes for two different distance education courses. International Review of Research in Open and Distance Learning, 5(2), 1–13.Google Scholar
  36. *Pigliapoco, E., & Bogliolo, A. (2008). The effects of psychological sense of community in online and face-to-face academic courses. International Journal of Emerging Technologies in Learning, 3 (4), 60–69.Google Scholar
  37. *Poellhuber, B., Chomienne, M., & Karsenti, T. (2008). The effect of peer collaboration and collaborative learning on self-efficacy and persistence in a learner-paced continuous intake model. Journal of Distance Education, 22(3), 41–62.Google Scholar
  38. *Rolfe, C. J. (2007). Getting the bugs out of the distance learning experience. College Quarterly, 10(3), 1–35.Google Scholar
  39. Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Washington, DC: American Psychological Association.Google Scholar
  40. *Rovai, A. P., & Wighting, M. J. (2005). Feelings of alienation and community among higher education students in a virtual classroom. The Internet and Higher Education, 8(2), 97–110.Google Scholar
  41. *Shin, N., & Kim, J. (1999). An exploration of learner progress and drop-out in Korea National Open University. Distance Education, 20(1), 81–95.Google Scholar
  42. *Tello, S. F. (2007). An analysis of student persistence in online education. International Journal of Information and Communication Technology Education, 3(3), 47–62.Google Scholar
  43. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.Google Scholar
  44. *Willging, P. A., & Johnson, S. D. (2004). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 8(4), 105–118.Google Scholar
  45. *Woodley, A., De Lange, P., & Tanewski, G. (2001). Student progress in distance education: Kember’s model re-visited. Open Learning, 16(2), 113–131.Google Scholar
  46. *Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers and Education, 39(4), 361.Google Scholar

Copyright information

© Association for Educational Communications and Technology 2010

Authors and Affiliations

  1. 1.University of Virginia, Instructional Technology ProgramCharlottesvilleUSA
  2. 2.CharlottesvilleUSA
  3. 3.CharlottesvilleUSA

Personalised recommendations