Strengths-Based Analysis of Student Success in Online Courses

Carol S Gering, Dani' K Sheppard, Barbara L Adams, Susan L Renes, Allan A Morotti


Online courses today give a broad, diverse population access to higher education. Despite postsecondary institutions embracing this opportunity, scholarly literature reveals persistent concern over low retention rates in online courses. In response to this concern, an explanatory sequential, mixed methods study was conducted in three phases at a public research university to simultaneously explore personal, circumstantial, and course variables associated with student success from a strengths-based perspective. Existing data on student enrollments across four years were analyzed. A subset of Phase One students from a single semester were invited in the second phase to complete an assessment of non-cognitive attributes and personal perceptions, followed in the third phase by interviews among a stratified sample of successful students from the previous phase to elaborate on factors impacting their success. Quantitative analyses identified seven individual variables with statistical and practical significance for online student success. Interestingly, the combination of factors classified as predictive of success changed with student academic standing. The impact of differential success factors across academic experience may explain mixed results in previous studies. The themes that emerged from the interviews with students were congruent with quantitative findings. A unique perspective was shared when students discussed “teaching themselves,” providing additional insight into perceptions of teaching presence not formerly understood. The combination of a more contextual research approach, a strengths-based perspective, and insights from student perceptions yielded implications for educational practice.


online courses; online learning; student success; postsecondary education; higher education

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Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Newburyport, MA: Sloan Consortium. Retrieved from

Allen, I. E., & Seaman, J. (2017). Digital learning compass: Distance education enrollment report 2017. Babson Park, MA: Babson Survey Research Group. Retrieved from

Allen, I. E., Seaman, J., Poulin, R., & Straut, T. T. (2016). Online report card: Tracking online education in the United States. Babson Park, MA: Babson Survey Research Group and Quahog Research Group, LLC.

Aragon, S. R., & Johnson, E. S. (2008). Factors influencing completion and noncompletion of community college online courses. American Journal of Distance Education, 22(3), 146-158.

Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248-287.

Baturay, M. H., & Yukselturk, E. (2015). The role of online education preferences on student’s achievement. Turkish Online Journal of Distance Education, 16(3), 3-12.

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

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.

Boston, W. E., Ice, P., & Gibson, A. M. (2011). Comprehensive assessment of student retention in online learning environments. Online Journal of Distance Learning Administration, 4(1).

Carnevale, A. P., Strohl, J., & Smith, N. (2009). Help wanted: Postsecondary education and training required. New Directions for Community Colleges, 146, 21-31.

Clark, M. (2013). Student success and retention: Critical factors for success in the online environment (Doctoral dissertation). Retrieved from UNF Theses and Dissertations. Paper 444.

Clinefelter, D. L., & Aslanian, C. B. (2016). Online college students 2016: Comprehensive data on demands and preferences. Louisville, KY: The Learning House, Inc.

Cochran, J. D., Campbell, S. M., Baker, H. M., & Leeds, E. M. (2014). The role of student characteristics in predicting retention in online courses. Research in Higher Education, 55(1), 27-48.

Creswell, J. W. (2011). Controversies in mixed methods research. In N. K. Denzin and Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (4th ed.). (269-283). Los Angeles, CA: Sage.

Dweck, C. S. (2013). Self-theories: Their role in motivation, personality, and development. Hoboken, NJ: Taylor and Francis.

Ekstrand, B. (2013). Prerequisites for persistence in distance education. Online Journal of Distance Learning Administration, 16(3).

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2), 87-105.

Gibson, A., Kupczynski, L., & Ice, P. (2010). Student success in top 20 courses of an online institution: Demographic differences in a multi-semester cross-curricular study. i-Manager's Journal of Educational Technology, 7(2), 18-26.

Glazier, R. A. (2016). Building rapport to improve retention and success in online classes. Journal of Political Science Education, 12(4), 437-456.

Guidry, K. (2013). Predictors of student success in online courses: Quantitative versus qualitative subject matter. Journal of Instructional Pedagogies, 10, 1-13.

Hachey, A. C., Wladis, C. W., & Conway, K. M. (2014). Do prior online course outcomes provide more information than GPA alone in predicting subsequent online course grades and retention? An observational study at an urban community college. Computers & Education, 72, 59-67.

Harrell, I. L., & Bower, B. L. (2011). Student characteristics that predict persistence in community college online courses. American Journal of Distance Education, 25(3), 178-191.

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.

Hegeman, J. (2015). Using instructor-generated video lectures in online mathematics courses improves student learning. Online Learning, 19(3), 70-87.

Herbert, M. (2006). Staying the course: A study in online student satisfaction and retention. Online Journal of Distance Learning Administration, 9(4), 300-317.

Jaggars, S., & Xu, D. (2010). Online learning in the Virginia Community College System. Community College Research Center, Columbia University. Retrieved from

Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 62, 149-158.

Jost, B., Rude-Parkins, C., & Githens, R. P. (2012). Academic performance, age, gender, and ethnicity in online courses delivered by two-year colleges. Community College Journal of Research and Practice, 36(9), 656-669.

Kelderman, E. (2013, January 10). Lumina Foundation adopts new tactics to reach college-completion goal. The Chronicle of Higher Education.

Layne, M., Boston, W. E., & Ice, P. (2013). A longitudinal study of online learners: Shoppers, swirlers, stoppers, and succeeders as a function of demographic characteristics. Online Journal of Distance Learning Administration, 16(2).

Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618.

Lee, Y., Choi, J., & Kim, T. (2013). Discriminating factors between completers of and dropouts from online learning courses. British Journal of Educational Technology, 44(2), 328-337.

Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48(2), 185-204.

Liu, S. Y., Gomez, J., & Yen, C. (2009). Community college online course retention and final grade: Predictability of social presence. Journal of Interactive Online Learning, 8(2), 165-182.

Lokken, F. (2017). Trends in elearning: Tracking the impact of elearning at community colleges. Washington, DC: Instructional Technology Council.

Lopez, S. J., & Louis, M. C. (2009). The principles of strengths-based education. Journal of College and Character, 10(4).

Maton, K. I., Dodgen, D. W., Leadbeater, B. J., Sandler, I. N., Schellenbach, C. J., & Solarz, A. L. (2004). Strengths-based research and policy: An introduction. In K. I. Maton, C. J. Schellenbach, B. J. Leadbeater, & A. L. Solarz (Eds.), Investing in children, youth, families, and communities: Strengths-based research and policy (pp. 3–12). Washington, DC: American Psychological Association.

National Adult Learner Coalition (2017, February). Strengthening America’s Economy by Expanding Educational Opportunities for Working Adults. Retrieved from

Olson, J. S., & McCracken, F. E. (2014). Is it worth the effort? The impact of incorporating synchronous lectures into an online course. Online Learning, 19(2).

Park, J. -H., & Choi, H. J. (2009). Factors influencing adult learners' decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207-217.

Perry, R. P., Hladkyj, S., Pekrun, R. H., & Pelletier, S. T. (2001). Academic control and action control in the achievement of college students: A longitudinal field study. Journal of Educational Psychology, 93(4), 776-789.

Rockinson-Szapkiw, A., Wendt, J., Wighting, M., & Nisbet, D. (2016). The predictive relationship among the Community of Inquiry framework, perceived learning and online, and graduate students’ course grades in online synchronous and asynchronous courses. The International Review of Research in Open and Distributed Learning, 17(3).

Rogers, P. R. (2015). Student locus of control and online course performance: An empirical examination of student success in online management courses. Academy of Educational Leadership Journal, 19(3), 261-270.

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

Saldaña, J. (2009). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage.

Schwarzer, R., & Jerusalem, M. (2009). The general self-efficacy scale (GSE). Anxiety, Stress, and Coping, 12, 329-345.

Shushok Jr, F., & Hulme, E. (2006). What's right with you: Helping students find and use their personal strengths. About Campus, 11(4), 2-8.

Simpson, O. (2006). Predicting student success in open and distance learning. Open Learning, 21(2), 125-138.

Soares, L. (2013, January). Post-traditional learners and the transformation of postsecondary education: A manifesto for college leaders. Retrieved from:

Stebleton, M. J., Soria, K. M., & Albecker, A. (2012). Integrating strength-based education into a first-year experience curriculum. Journal of College and Character, 13(2).

Stupnisky, R. H., Perry, R. P., Hall, N. C., & Guay, F. (2012). Examining perceived control level and instability as predictors of first-year college students’ academic achievement. Contemporary Educational Psychology, 37(2), 81-90.

Suphi, N., & Yaratan, H. (2012). Effects of learning approaches, locus of control, socio-economic status and self-efficacy on academic achievement: A Turkish perspective. Educational Studies, 38(4), 419-431.

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago, IL: University of Chicago Press.

Wadsworth, L. M., Husman, J., Duggan, M. A., & Pennington, M. N. (2007). Online mathematics achievement: Effects of learning strategies and self-efficacy. Journal of Developmental Education, 30(3), 6-14.

Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302-323.

Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology & Society, 10(2), 71-83.

Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30-41.