The Role of Service Quality Management in Students’ Re-Enrollment
The students’ re-enrollment can reflect as one of the indications of an educational institution’s service quality and student loyalty. The re-enrollment is identified by the condition that students enrolled in one semester would re-enroll in the next immediate semester. The main concern of this article is that there are many interrelated variables affecting students’ re-enrollment. Those variables mainly included service quality management of higher distance education, student characteristics, student academic success, accessibility to student learning support services. The study applied ex-post-facto method with a sample of 3539 students and used a statistical technique of the binary logistic regression to identify factors related to student re- enrollment in three regional office centers of Universitas Terbuka, Indonesia, identified as low, medium, and high in service quality management. The results revealed that the students’ re-enrollment is affected by the level of service quality management modulated factors: (1) the student personal characteristics, (2) the level of success in previous semesters, and (3) the participation in the learning support services. The paper discusses the implications of managing the students’ re-enrollment based on the findings.
___
- Choi, H., Lee, Y., Jung, I., & Latchem, C. (2013). The extent of and reasons for non re-enrollment: A case of Korea
National Open University. The International Review of Research in Open and Distance Learning,
14(4). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1314.
Cohen, L., Manion, L. & Morison, K. (2000). Research Methods in Education. London: Routledge Falmer.
Daniel, Sir John. (2007). The expansion of higher education in the Developing World: What
can distance learning contribute? Chea International Commission Conference 2007, 1
February 2007 Washington, DC. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/
download?doi=10.1.1.627.9793&rep=rep1&type=pdf
Darojat, O. (2018). How are the results of quality assurance programs used to inform practices at a distance
higher education? Turkish Online Journal of Distance Education, 6(1). Retrieved from: http://
dergipark.gov.tr/tojde/issue/34638/382730
Dzakiria, H., Kasim, A., Mohamed. A.H., & Christopher, A.A. (2013). Effective learning interaction as a
prerequisite to successful open distance learning (ODL): A Case study of Learners in the Northern
State of Kedah and Perlis, Malaysia. Turkish Online Journal of Distance Education, 14(1). Retrieved
from http://files.eric.ed.gov/fulltext/EJ1006252.pdf.
Field, Andy. (2013). Discovering Statistics Using IBM SPSS Statistics (4th Edition). London: Sage Publications.
Gilbreath, Kim, Nichols. (2011). Person-Environment Fit and its Effects on University Students: A Response
Surface Methodology Study. Research in Higher Education, 51(1). DOI 10.1007/s11162-010-
9182-3.
Godfrey, K. E. & Matos-Elefonte, H. (2010). Key indicators of college success: Predicting college enrollment,
persistence, and graduation. New York, NY: College Board.
Hamid, F.S. & Yip, N. (2019). Comparing service quality in public vs private distance education institutions:
evidence based on Malaysia. Turkish Online Journal of Distance Education, 20(1), pp. 17-34.
Kerlinger, F.N., & Rint, N. (1986) Foundations of Behaviour Research. London: Winston Inc.
Le, H., Robbins, S. B., & Westrick, P. (2014). Predicting student enrollment and persistence in college
STEM fields using an expanded P-E fit framework: A large-scale multilevel study. Journal of
Applied Psychology, 99(5), 915-947.10.1037/a0035998
Li, Xiaobin. (2009). Review of distance education used in higher education in China. Asian Journal of
Distance Education, 7(2), pp. 28 – 34.
Nicholson, K. (2011). Quality assurance in higher education: A review of the literature. Retrieved March 17,
2012 from http://cll.mcmaster.ca/COU/pdf/Quality% 20Assurance%20Literature%20Review.
pdf?referer=http%3A%2F%2Fworks.bepr ess.com%2Fkaren_nicholson%2F19%2F.
Ozturk, O. (2019). A logistic regression analysis of factors affecting enrollment decisions of prospective
students of distance education programs in Anadolu University. Turkish Online Journal of Distance
Education, 20(1), pp. 145-160.
Park, Hyeoun-Ae. (2103). An introduction to logistic regression: From basic concepts to interpretation with
particular attention to nursing domain. Journal of Korean Academic Nursing, 43(2), pp. 154-164.
Retrieved from http://dx.doi.org/10.4040/jkan.2013.43.2.154.
Peng, C-Y., J., Lee, K.L., & Ingersoll, G.M. (2002). An introduction to logistic regression analysis and
reporting. The Journal of Educational Research, 96(1). Retrieved from http://www-psychology.
concordia.ca/fac/kline/734/peng.pdf.
Plimmer, G., Clarke-Okah, W., Donovan, C., and Russell, W. (2012). Lowering the cost and increasing
the effectiveness of quality assurance: COL RIM. In I. S. Jung & C. Latchem (Eds.), Quality
assurance and accreditation in distance education and elearning: Models, policies, and research,
(pp. 162-172). New York: Routledge.
56
Ryan, P. (2015). Quality assurance in higher education: A review of literature. Higher Learning Research
Communications, 5(4). http://dx.doi.org/10.18870/hlrc.v5i4.257.
Ryan, Y. & Brown, M. (2012). Quality assurance policies and guidelines for distance education in Australia
and New Zealand. In I. S. Jung & C. Latchem (Eds.), Quality assurance and accreditation in
distance education and e-learning: Models, policies, and research, (pp. 91-101). New York:
Sharma, Y. (2015). Open and distance learning – Access and success. University World News, Issue No: 382.
Retrieved from http://www.universityworldnews.com/article.php?story=20150917175108463
Simpson, O. (2016). Student Support Services for Success in Open and Distance Learning. Retrived from
http://www.cemca.org.in.
Smart, J.C., Feldman, K.A., & Ethington, C.A. (2006). Holland’s theory and patterns of college student success.
Retrieved from http://nces.ed.gov/npec/pdf/smart_team_report.pdf.
Stephan, J. L., Davis, E., Lindsay, J., & Miller, S. (2015). Who will succeed and who will struggle? Predicting
early college success with Indiana’s Student Information System. Washington, DC: U.S. Department
of Education, Institute of Education Sciences, National Center for Education Evaluation and
Regional Assistance, Regional Educational Laboratory Midwest. Retrieved from http://ies.ed.gov/
ncee/edlabs.
Sugilar. (2018). Predicting Student’s Re-enrollment in an Open and Distance Learning Environment. In:
Persichitte K., Suparman A., Spector M. (eds) Educational Technology to Improve Quality and
Access on a Global Scale. Educational Communications and Technology: Issues and Innovations.
Springer, Cham.
Suhlmann, M., Sassenberg, K., Nagengast, B., & Trautwein, U. (2018). Belonging Mediates Effects of
Student-University Fit on Well-Being, Motivation, and Dropout Intention. Social Psychology 49,
pp. 16-28. https://doi.org/10.1027/1864-9335/a000325.
Tezcan-Unal, B., Winston, K., & Qualter, A. (2019). Learning-oriented quality assurance in higher education
institutions. Journal Quality in Higher Education, 24(3), https://doi.org/10.1080/13538322.201
8.1558504.
University of Maryland University College. (2015). Predictive analytics for student success: Developing
data-driven predictive models of student success. Retrieved from https://www.umuc.edu/visitors/
about/ipra/upload/developing-data-driven-predictive-models-of-student-success-final.pdf.
Yousapronpaiboon, K. (2014). SERVQUAL: Measuring higher education service quality in Thailand. Paper
persented at the 5th World Conference on Educational Sciences - WCES 2013. Retrieved from: https://
doi.org/10.1016/j.sbspro.2014.01.350.