Sociodemographic Diversity and Distance Education: Who Drops Out from Academic Programs and Why?
Current higher education is characterized by a proliferation of distance education programs and by an increasing inclusion of nontraditional students. In this study we investigated whether and to what extent nontraditional students are particularly at risk for attrition (vs. graduating) from distance education programs. We conducted a secondary analysis of cross-sectional institutional surveys deployed in the context of a public German distance teaching university among university graduates and dropouts (N = 4,599). Using binary-logistic multiple regression analyses, we predicted the likelihood of program attrition by students’ membership in sociodemographic groups, their goal orientations, and the corresponding interactions. Results revealed higher risks to drop out from university for female, migrant, and fully-employed students, but lower risks for older and parent students. A higher importance of career development or personal development goals related to a lower risk for attrition. Moreover, data also provide evidence that among some student groups the likelihood to graduate (or to drop out) significantly depends on students’ goal orientations. Results were robust across different academic faculties and were complemented by an analysis of dropout reasons. The practical implications of our findings are discussed with regard to designing equitable distance learning environments that value human diversity and quality of opportunity.
KeywordsDiversity inclusion Higher education Attrition Sociodemographic groups Academic goals
This research was made possible by a grant from the Ministry of Innovation, Science, Research and Technology of North Rhine-Westphalia (Germany) to the FernUniversität in Hagen (Principal investigator: Stefan Stürmer). We are grateful to Heide Schmidtmann, Jana Darnstädt and Julia Kreimeyer from the Institutional Research and Quality Monitoring Office of the FernUniversität for their assistance in carrying out this research.
- Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States. Needham: Sloan Consortium.Google Scholar
- Berge, Z. L., & Huang, Y.-P. (2004). A model for sustainable student retention: A holistic perspective on the student dropout problem with special attention to e-learning. DEOSNEWS, 13(5), 1–26.Google Scholar
- Bothma, F., & Monteith, J. L. (2004). Self-regulated learning as a prerequisite for successful distance learning. South African Journal of Education, 24(2), 141–147.Google Scholar
- Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46(23), A39.Google Scholar
- Choy, S. (2002). Nontraditional undergraduates. Findings from the condition of education 2002 (Vol. NCES 2002-012). Washington, DC: US Department of Education, National Center for Education Statistics.Google Scholar
- European Commission. (2009). The Bologna process in higher education in Europe. Key indicators on the social dimension and mobility. Luxemburg: Office for Official Publications of the European Communities.Google Scholar
- Guri-Rosenblit, S. (1999). Distance and campus universities: Tensions and interactions. A comparative study of five countries. Issues in higher education series. Paris: International Association of Universities.Google Scholar
- 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.Google Scholar
- Hülsmann, T. (2004). Guest editorial: Low cost distance education strategies: The use of appropriate information and communication technologies. International Review of Research in Open & Distance Learning, 5(1), 1–14.Google Scholar
- Jung, I. (2003). Issues of cost-effectiveness in open and distance learning. Distance Education in China, 196, 26–32.Google Scholar
- National Center for Education Statistics. (2011). Learning at a distance. Undergraduate enrollment in distance education courses and degree programs.Google Scholar
- Ojokheta, K. O. (2010). A path-analytic study of some correlates predicting persistence and student’s success in distance education in Nigeria. Turkish Online Journal of Distance Education, 11(1), 181–192.Google Scholar
- Oyserman, D., & Destin, M. (2010). Identity-based motivation: Implications for intervention. Counseling Psychologist, 38(7), 1001–1043.Google Scholar
- Phipps, R., & Merisotis, J. (1999). What’s the difference? A review of contemporary research on the effectiveness of distance learning in higher education. Washington, DC: Institute for Higher Education Policy.Google Scholar
- Poellhuber, B., & Anderson, T. (2011). Distance students’ readiness for social media and collaboration. International Review of Research in Open and Distance Learning, 12(6), 102–125.Google Scholar
- Powell, R., Conway, C., & Ross, L. (1990). Effects of student predisposing characteristics on student success. The Journal of Distance Education, 5(1), 5–19.Google Scholar
- Stürmer, S., Fisseler, B., Stoessel, K., Ihme, T. A., & Barbarino, M.-L. (2014). Unpublished data from a longitudinal evaluation of virtual study-buddy program. Hagen: FernUniversität in Hagen.Google Scholar
- UNESCO. (2002). Open and distance learning. Trends, policy and strategy considerations.Google Scholar
- UNESCO. (2009). Trends in global higher education: Tracking an academic revolution.Google Scholar
- Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127.Google Scholar