References
- Astin, R. (1991). A study of employment and distance education students at a community college. Community College Research, 12, 41–49.
- Barefoot, B. O. (2004). Higher education’s revolving door: Confronting the problem of student drop out in US colleges and universities. Open Learning: The Journal of Open and Distance Learning, 19, 9–18.
- Belawati, T. (1998). Increasing student persistence in Indonesian post-secondary distance education. Distance Education, 19, 81–109.
- Berge, Z., & Huang, Y. (2004). A model for sustainable student retention: A holistic perspective on the student dropout problem with special attention to e-learning. Deosnews, 13(5). Retrieved from http://www.ed.psu.edu/acsde/deos/deosnews/deosnews13_5.pdf
- 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, 245–253.
- Boyles, L. W. (2000). Exploration of a retention model for community college student. (Unpublished doctoral dissertation). The University of North Carolina, Greensboro, USA.
- Brindley, J. E. (1988). A model of attrition for distance education. In D. Sewart, & J. Daniel (Eds.), Developing distance education (pp. 131–137). Oslo, Norway: International Council for Distance Education.
- Campbell, P. B., & Jennifer, S. (1996). Reducing the distance: Equity issues in distance learning in public education. Journal of Science Education and Technology, 5, 285–295 . December.
- Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 46, 39–41. Retrieved from http://chronicle.com/free/v46/i23/23a00101.htm
- Chitnis, S., & Philip, A. G. (1993). Higher education reform in India: Experience and perspectives. New Delhi: Sage Publication.
- Chyung, S. Y. (1999). A case study: Implementation of ARCS Model, the organizational elements model, and Kirkpatrick’s evaluation model in distance education. Paper presented at the 80th annual meeting of the American Research Association (AERA), Montreal, Canada.
- Chyung, S. (2001). Systematic and systemic approaches to reducing attrition rates in online higher education. American Journal of Distance Education, 15, 36–49.
- Cooper, E. (1990). An analysis of student retention at Snead State Junior College. ERIC, ED 329298.
- De’ath, G., & Fabricius, K. E. (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–3192.
- Diaz, D. (2002). Online drop rates revisited. Technology Source. May–June. Retrieved from http://technologysource.org/article/online_drop_rates_revisited.
- Dille, B., & Mezack, M. (1991). Identifying predictors of high risk among community college telecourse students. American Journal of Distance Education, 5, 24–35.
- Doherty, W. (2006). An analysis of multiple factors affecting retention in Web-based community college courses. The Internet and Higher Education, 9, 245–255.
- Frankola, K. (2001). Why online learners drop out. Workforce. 80, 53–63. Retrieved from http://www.kfrankola.com/Documents/Why%20online%20learners%20drop%20out_Workforce.pdf
- Garland, M. R. (1993). Ethnography penetrates the “I didn’t have time” rationale to elucidate higher order reasons for distance education withdrawal. Research in Distance Education, 5, 6–10.
- Goel, A., & Goel, S. L. (2009). Distance education: Principles, potentialities and perspectives. New Delhi: Deep and Deep Publications.
- Jegede, O. J., Fraser, B., & Curtin, D. F. (1995). The development and validation of a distance and open learning environment scale. Educational Technology Research and Development, 43, 89–94.
- Kember, D. (1989). A longitudinal process model of drop-out from distance education. The Journal of Higher Education, 60, 278–301.
- Kember, D. (1995). Open learning courses for adults: A model of student progress. Englewood Cliffs, NJ: Educational Technology Publications.
- Kennedy, D., & Powell, R. (1976). Student progress and withdrawal in the Open University. Teaching at a Distance, 7, 61–75.
- Kumar, A., & Nagadevera, V. (2006). Conference proceedings: Development of hybrid classification methodology for mining skewed datasets: A case study of Indian customs data. 4th ACS/IEEE International Conference on Computer Systems and Applications, Sharjah, UAE.
- Lau, L. K. (2003). Institutional factors affecting student retention. Education, 124, 126–136.
- Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers and Education, 53, 950–965.
- Martin, L. (1990). Dropout, persistence and completion in adult second and pre-vocational education programs. Adult Literacy and Basic Education, 14, 159–174.
- Nagadevara, V., & Nayanatara, S. (2004). Improving the effectiveness of post literacy programme through data mining techniques. In M. P. Gupta (Ed.), Towards e-government, management and challenges. New Delhi: Tata McGraw Hill Publishing Company.
- Newell, C. (2007). Learner characteristics as predictors of online course completion among nontraditional technical college students. D. Dissertation, University of Georgia. Retrieved from http://archive.coe.uga.edu/leap/adminpolicy/dissertations_pdf/2007/newell_2007_edd.pdf
- Osborn, V. (2001). Identifying at-risk students in videoconferencing and web-based distance education. The American Journal of Distance Education, 15, 41–54.
- Park, J. H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out in online learning. Educational Technology & Society, 12, 207–217.
- Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1, 1–10.
- Parker, A. (2003). Identifying predictors of academic persistence in distance education. USDLA Journal, 17. Retrieved from http://www.usdla.org/html/journal/JAN03_Issue/article06.html
- Powell, R. J., & Keen, C. (2006). The axiomatic trap: Stultifying myths in distance education. Higher Education, 52, 283–301.
- Rekkedal, T. (1972). Correspondence studies-recruitment, achievement, and discontinuation. Epistolodidaktika, 2, 3–38.
- Smith, K. T. (2006). Early attrition among first time eLearners: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking eLearning programmes. Journal of Online Learning and Teaching. Retrieved from http://jolt.merlot.org/Vol2_No2_TylerSmith.htm
- Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89–125.
- Uba, L. (1997). Educating for success: A strategy to motivate independent learners. College Quarterly, 4. Retrieved from http://www.senecac.on.ca/quarterly/1997-vol04-num04-summer/uba.html
- Verduin, J. R., & Clark, T. A. (1991). Distance education: The foundations of effective practice. San Francisco, CA: Jossey-Bass.
- Woodley, A., & Parlett, M. (1983). Student drop-out. Teaching at a Distance, 24, 2–23.
- Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers and Education, 43, 345–359.
- 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. Computer and Education, 39, 361–377.