The purpose of this quantitative analytic study is to evaluate and test the theoretical underpinnings of the Kember (1995) student progress model that examines the direct or indirect effects of student persistence in e-learning by identifying the relationships between variables such as student perceptions, performance, cost-benefit analysis, and student persistence. Thomson (1999), Houle (2004), Harlow (2006), and PortaMerida (2009) verified the reliability and validity of the theory, yet their results are slightly dissimilar in the magnitude of influence on student persistence. Former studies indicate that it could be meaningful to reexamine the variables in more current studies. The online survey in this study explored the relationships among variables. The population of the sample of this study was 169 students at a public community college in Maryland that is offering online and hybrid degree programs. The logistic regression and multiple regression analysis were utilized to analyze the survey data. The findings of this study consistently indicated that negative external attribution was a significant factor for student persistence, degrading the student’s work. Simultaneously, individual student grade point average (GPA) and academic integration were highly correlated to student persistence. The findings of this study convey the current phenomena and knowledge of e-learning regarding student persistence. Social media has been seen as a potential problem, but it could also be a solution if it increases social interaction on focused scholarly topics. Decreasing external attribution and encouraging higher GPA by increasing the academic integration help students continue to pursue their educational goals.
Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008. The Sloan Consortium. http://www.sloan-c.org/publications/
Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Babson Park, MA: Babson Survey Research Group and Quahog Research Group. http://www.onlinelearningsurvey.com/reports/changingcourse.pdf
Barefoot, B. O. (2004). Higher education’s revolving door: Confronting the problem of student dropout in US colleges and universities. Open Learning, 19, 9-18. http://dx.doi.org/10.1080/0268051042000177818
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. http://psych.wisc.edu/henriques/mediator.html http://dx.doi.org/10.1037/0022-3514.51.6.1173
Bean, J., & Metzner, B. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55, 485-540. http://dx.doi.org/10.3102/00346543055004485
Blashak, T. (2010). Academic advising: A correlation analysis of online student retention and academic advisor emotional intelligence. Ed.D. Dissertation, Phoenix, AZ: University of Phoenix.
Bloom, B. S. (1956). Taxonomy of educational objectives, Handbook 1: Cognitive domain. New York: Longman.
Brown, C. (2007). An empirical test of the nontraditional undergraduate student attrition model using structural equation modeling. Ph.D. Dissertation, Athens, OH: Ohio University.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155159. http://dx.doi.org/10.1037/0033-2909.112.1.155
Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: SAGE Publications, Inc.
Crisp, G. (2010). The impact of mentoring on the success of community college students. Review of Higher Education, 34, 39-60. http://dx.doi.org/10.1353/rhe.2010.0003
Cronbach, L. (1951). Coefficient alpha and the internal. Psychometrika, 16, 297-334. http://dx.doi.org/10.1007/BF02310555
Davis, C. (2010). Noncognitive predictors of academic performance and persistence in horizontal and vertical transfer students by academic level. Ph.D. Dissertation, Norfolk, VA: Old Dominion University.
Dizik, A. (2010). Business education: Online programs push for more interaction. Wall Street Journal (Eastern Edition), B9. http://search.proquest.com/docview/577230086?accountid=28844
Farmer, L. (2009). Correlation of student expectations of online classes and course grades at a community college. D.M. Dissertation, Phoenix, AZ: University of Phoenix.
Field, K. (2009). President’s budget would end bank-based student lending and significantly expand Pell grants. The Chronicle of Higher Education. http://chronicle.com/article/Presidents-Budget-Would-End/1555
Filimban, G. Z. (2008). Factors that contribute to the effectiveness of online learning technology at Oregon State University. http://search.proquest.com/docview/304500250?accountid=28844
Fleming, N. D., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. To Improve the Academy, 11, 137.
French, B. F., Immekus J. C., & Oakes, W. (2003). A structural model of engineering students success and persistence. Proceedings of Frontiers in Education Annual Conference, Boulder, 5-8 November 2003, 19-24
Griffin, J. (2008). Distance learning technologies and writing center conferences: A comparative analysis of two methods for delivering consultations at a distance. Ph.D. Dissertation, Louisville, KY: University of Louisville.
Harlow, J. E. (2006). Social integration, motivational orientation, and self-regulated learning strategies of online versus face-to-face theological seminary biblical language students. Ph.D. Dissertation, Greensboro, NC: The University of North Carolina at Greensboro.
Hagedorn, L. (2006). How to define retention: A new look at an old problem. http://honolulu.hawaii.edu/intranet/committees/rrc/pdf/How_To_Define%20Retention.pdf
Harris, T. (2010). Enrollment management professionals in community colleges: An exploratory study of their influence on student recruitment and retention. Ph.D. Dissertation, Minneapolis, MN: Walden University.
Heyman, E. (2010). Overcoming student retention issues in higher education online programs: A Delphi study. Ed.D. Dissertation, Phoenix, AZ: University of Phoenix.
Houle, B. J. (2004). Adult student persistence in Web-based education. Ph.D. Dissertation, New York: New York University.
Jiang, M., & Ting, E. (2000). A study of factors influencing students’ perceived learning in a web-based course environment. International Journal of Educational Telecommunications, 6, 317-338.
Kay, S. (2009). Student graduation studies in online education. Ed.D. Dissertation, Philadelphia, PA: University of Pennsylvania.
Kember, D. (1989). A longitudinal-process model of drop-out from distance education. The Journal of Higher Education, 60, 278-301. http://dx.doi.org/10.2307/1982251
Kember, D. (1995). Open learning courses for adults: A model of student progress. Englewood Cliffs, NJ: Educational Technology Publications.
Kember, D., Murphy, D., Siaw, I., & Yuen, K. S. (1991). Towards a causal model of student progress in distance education. American Journal of Distance Education, 5, 3-15. http://dx.doi.org/10.1080/08923649109526742
Kember, D., Lai, T., Murphy, D., Siaw, I., & Yuen, K. S. (1992). Student progress in distance education: Identification of explanatory constructs. British Journal of Educational Psychology, 62, 285-298. http://dx.doi.org/10.1111/j.2044-8279.1992.tb01023.x
Kember, D., Lai, T., Mlurphy, D., Siaw, I., & Yuen, K. (1994). Student progress in distance education courses: A replication study. Adult Education Quarterly, 45, 286-301. http://dx.doi.org/10.1177/0741713694045001003
Kember, D., Lai, T., Mlurphy, D., Siaw, I., & Yuen, K. (1995). Student progress in distance education. A handbook for: The DESP inventory and the interview schedule. Unpublished Manuscript, Hong Kong: Hong Kong Polytechnic University.
Kord, J. (2008). Understanding the Facebook Generation: A study of the relationship between online social networking and academic and social integration and intentions to re-enroll. Ph.D. Dissertation, Lawrence, KS: University of Kansas.
Lei, S., & Gupta, R. (2010). College distance education courses: Evaluating benefits and costs from institutional, faculty and students’ perspectives. Education, 130, 616-631.
Lint, A. (2011). The impact of student progress factors on student persistence in E-learning. Dissertation 2011, Cypress, CA: TUI University, UMI/ProQuest.
Mandinach, E. B. (2005). The development of effective evaluation methods for E-learning: A concept paper and action plan. Teachers College Record, 107, 1814-1835. http://dx.doi.org/10.1111/j.1467-9620.2005.00543.x
National Center for Education Statistics website. Distance Education at Degree-Granting Postsecondary Institutions: 2006-2007. Postsecondary Education Quick Information System. http://nces.ed.gov/pubs2009/2009044.pdf
Nichols, M. (2010). Student perceptions of support services and the influence of targeted interventions on retention in distance education. Distance Education, 31, 93-113. http://dx.doi.org/10.1080/01587911003725048
Perantoni, E. (2010). Course design based on the Kolb learning style as it relates to student success in online classes. Ed.D. Dissertation, St. Charles, MO: Lindenwood University.
Riffell, S., & Sibley, D. (2005). Using web-based instruction to improve large undergraduate biology courses: An evaluation of a hybrid course format. Computer and Education, 44, 217-235. http://dx.doi.org/10.1016/j.compedu.2004.01.005
Savage, T. (2010). Examination of learner autonomy and student persistence in community college. Ed.D. Dissertation, Washington, DC: The George Washington University.
Senhouse, S. (2008). How to keep them hooked: A study of social integration and retention among distance learners. Ph.D. Dissertation, St. Minneapolis, MN: Capella University.
Smith, H. (2009). Asynchronous online instruction: Generation and validation of a program evaluation instrument. Ed.D. Dissertation, Malibu, CA: Pepperdine University.
Spady, W. (1971). Dropouts from higher education: Toward an empirical mode. Interchange, 2, 38-62. http://dx.doi.org/10.1007/BF02282469
Strevy, S. (2009). Persistence of students in RNBS completion online programs. Ph.D. Dissertation, Bloomington, IN: Indiana University.
Stuart, G. R. (2010). A benefit/cost analysis of three student enrollment behaviors at a community college: Dropout, transfer and completion of an associate’s degree/certificate. Ph.D. Dissertation, Cleveland, OH: Cleveland State University.
Thompson, E. (1999). Can the distance education student progress (DESP) inventory be used as a tool to predict attrition in distance education? Higher Education Research & Development, 18, 77-78. http://dx.doi.org/10.1080/0729436990180106
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89-125. http://dx.doi.org/10.3102/00346543045001089
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition. Chicago, IL: The University of Chicago Press.
Tinto, V. (1999). Taking retention seriously: Rethinking the first year of college. NACADA Journal, 19, 5-9. http://dx.doi.org/10.12930/0271-9517-19.2.5
Vuong, M., Brown-Welty, S., & Tracz, S. (2010). The effects of selfefficacy on academic success of first-generation college sophomore students. Journal of College Student Development, 51, 50-64. http://dx.doi.org/10.1353/csd.0.0109
Weidman, J. C. (1985). Retention of nontraditional students in postsecondary education. Chicago, IL: The Annual Meeting of the American Education Research Association,
Woosley, S., & Miller, A. (2009). Integration and institutional commitment as predictors of college student transition: Are third week indicators significant? College Student Journal, 43, 1260-1271.