Abstract
The review of research on attrition commences with research on face-to-face teaching. Early research on entry characteristics and their relationship to attrition was discredited, as it was found that such variables predicted little of the variance in retention and success. Instead, research concentrated on developing models which took into account what occurred during the course of study. The most highly cited is the model of Tinto, which posited that retention was promoted through students becoming socially and academically integrated with the college community, through student–student and student–teacher interaction on-campus. It has been found difficult to translate this influential research into the context of online teaching, as it lacks the direct student–student and student–teacher contact which provides the integrative mechanism. The chapter concludes by reviewing research into virtual communities in online learning.
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References
Ahn, M. Y., & Davis, H. H. (2020). Four domains of students’ sense of belonging to university. Studies in Higher Education, 45(3), 622–634.
American Psychological Association. (2010). Publication manual of the American Psychological Association (6th edition). American Psychological Association.
Axelson, R. D., & Flick, A. (2010). Defining student engagement. Change: The magazine of higher learning, 43(1), 38–43.
Bawa, P. (2016). Retention in online courses: Exploring issues and solutions—A literature review. SAGE Open, 6(1), 1–11. https://doi.org/10.1177/2158244015621777
Bensimon. (2009). MISSING.
Bowles, T. V., & Brindle, K. A. (2017). Identifying facilitating factors and barriers to improving student retention rates in tertiary teaching courses: A systematic review. Higher Education Research & Development, 36(5), 903–919. https://doi.org/10.1080/07294360.2016.1264927
Calvert, C. (2014). Developing a model and applications for probabilities of student success: A case study of predictive analytics. Open Learning: THe Journal of Open, Distance and e-Learning, 29(2), 160–173. https://doi.org/10.1080/02680513.2014.931805
Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46(23), 39–42.
Carroll, D., Ng, E., & Birch, D. (2009). Retention and progression of postgraduate business students: An Australian perspective. Open Learning: The Journal of Open and Distance Learning, 24, 197–209.
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. https://doi.org/10.1007/s11162-013-9305-8
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29. https://doi.org/10.1109/TLT.2016.2616312
Delnoij, L. E. C., Dirkx, K. J. H., Janssen, J. P. W., & Martens, R. L. (2020). Predicting and resolving non-completion in higher (online) education—A literature review. Educational Research Review, 29, 100313.
Durkheim, E. (1951). In J. A. Spaulding & G. Simpson, (Trans.), Suicide. The Free Press.
Engstrom, C., & Tinto, V. (2008). Access without support is not opportunity. Change: The Magazine of Higher Learning, 40(1), 46–50. https://doi.org/10.3200/CHNG.40.1.46-50
Fan, S., Chen, L., Nair, M., Garg, S., Yeom, S., Kregor, G., & Wang, Y. (2021). Revealing impact factors on student engagement: Learning analytics adoption in online and blended courses in higher education. Education Sciences, 11(10), 608.
Fisher, S., Murray, K., & Frazer, N. A. (1985). Homesickness, health and efficiency in first year students. Journal of Environmental Psychology, 5(2), 181–195.
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(2–3), 87–105.
Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. The Internet and Higher Education, 13(1–2), 5–9.
HESP, (2017). Improving retention, completion and success in higher education, higher education standards panel discussion paper (Canberra: Australian Government).
Hess, D. J., Lanig, H., & Vaughan, W. (2007). Educating for equity and social justice: A conceptual model for cultural engagement. Multicultural Perspectives, 9(1), 32–39.
Hew, K. F., Cheung, W., & S. (2012). Student participation in online discussions: Challenges, solutions, and future research. Springer.
James, R., Krause, K.-L., & Jennings, C. (2010). The first year experience in Australian universities: Findings from 1994 to 2009. Centre for Studies in Higher Education, University of Melbourne.
Kember, D., Leung, D., & Prosser, M. (2019). Has the open door become a revolving door? The impact on attrition of moving from elite to mass higher education? Studies in Higher Education. https://doi.org/10.1080/03075079.2019.1629411
Kember, D. (1989). A longitudinal-process model of drop-out from distance education. The Journal of Higher Education, 60(3), 278–301.
Kember, D. (1995). Open learning courses for adults: A model of student progress. Educational Technology Publications.
Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., & Gonyea, R. M. (2008). Unmasking the effects of student engagement on first-year college grades and persistence. Journal of Higher Education, 79(5), 540–563.
Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59, 593–618.
Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers and Education, 48(2), 185–204.
Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29(4), 531–563. https://doi.org/10.1177/1052562904271199
Moore, M. G. (1973). Towards a theory of independent learning and teaching. Journal of Higher Education, 44(9), 661–679.
Moore, M. G. (1989). Three types of interaction. American Journal of Distance Education, 3(2), 1–7.
Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.
Muljana, P. S., & Luo, T (2019). Factors contributing to student retention in online learning and recommended strategies for improvement: A systematic literature review. Journal of Information Technology. digitalcommons.odu.edu.
Pascarella, E. T., & Terenzini, P. T. (1980). Predicting freshman persistence and voluntary dropout decisions from a theoretical model. The Journal of Higher Education, 51(1), 60–75.
Pascarella, E.T. (Ed.) (1982). Studying student attrition. Jossey-Bass.
Perry, W. G. (1970). Forms of intellectual and ethical development in the college years. Holt, Rinehart and Winston.
Perry, W.G. (1988). Different worlds in the same classroom. In P. Ramsden (Ed.), Improving learning: New perspectives. Kogan Page.
Qvortrup, A., & Lykkegaard, E. (2022). Study environment factors associated with retention in higher education. Higher Education Pedagogies, 7(1), 37–64.
Simpson, O. (2013). Supporting students for success in online and distance education. Routledge.
Spady, W. (1971). Dropouts from higher education: Toward an empirical model. Interchange, 2, 38–62.
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A Socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2), 177–193.
Tello, S. F. (2007). An analysis of student persistence in online education. International Journal of Information and Communication Technology Education, 3(3), 47–62.
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089
Tinto, V. (1987). Leaving college. University of Chicago Press.
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press.
Tinto, V. (2012). Enhancing student success: Taking the classroom success seriously. The International Journal of the First Year in Higher Education, 3(1), 1–8.
Tinto, V. (2015). Through the eyes of students. Journal of College Student Retention: Research, Theory & Practice. 1521025115621917.
Trowler, V. (2010). Student engagement literature review. (HEA Report 11) Retrieved from The Higher Education academy website: https://www.heacademy.ac.uk/system/files/StudentEngagementLiteratureReview_1.pdf
Van Gennep, A. (1960). In M. Vizedom & G. Caffee (Trans.), The rites of passage. Routledge & Kegan Paul.
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Kember, D., Fan, S. (2023). Review of Literature on Attrition. In: Kember, D., Ellis, R.A., Fan, S., Trimble, A. (eds) Adapting to Online and Blended Learning in Higher Education. Springer, Singapore. https://doi.org/10.1007/978-981-99-0898-1_9
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