Research Article
Year 2020,
Volume: 21 Issue: 2, 141 - 152, 01.04.2020
Abstract
References
- Ali, F., Zhou, Y., Hussain, K., Nair, P. K., & Ragavan, N. A. (2016). Does higher education service quality effect student satisfaction, image and loyalty? Quality Assurance in Education, 24(1), 70–94. Carr, S.(2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46 (23), A39-A41. Carreira, P., & Lopes, A. S. (2018). Pathways of adult student-workers in higher education: explaining the risks of early dropout, late dropout and graduation. XXVII Meeting of the Economics of Education Association. Retrieved February 01, 2019 from https://iconline.ipleiria.pt/handle/10400.8/3340 De La Varre, C., Irvin, M. J., Jordan, A. W., Hannum, W. H., & Farmer, T. W. (2014). Reasons for student dropout in an online course in a rural K-12 setting. Distance Education, 35(3), 324–344. Fredericksen, E., Swan, K., Pelz, W., Pickett, A.M., & Shea, P. (2000). Student satisfaction and perceived learning with online courses: principles and examples from the SUNY Learning Network. Journal of Asynchronous Learning Network, 4 (2), 7-41. 152 Kostopoulos, G., Kotsiantis, S., & Pintelas, P. (2015). Estimating student dropouts in distance higher education using semi-supervised techniques. The 19th Panhellenic Conference on Informatics, 2015 (pp. 38-43). New York: ACM. Moore, M.G., & Kearsley, G. (1996). Distance education: a systems view of online learning. California, USA: Wadswoth Publishing Company. Mulyana, A., & Yuni, D. (2014). Pengaruh faktor-faktor pembentuk loyalitas mahasiswa Universitas Terbuka (Effect of factors forming student loyalty in Universitas Terbuka). Derivatif, 8 (2), 41-49. Muslim, D., Raza, S. M. M., & Touseef, S. A. (2017). Major dropouts reasons of students in e-learning institutions of Pakistan. The Online Journal of Communication and Media, 3(4), 30–35. Ojokheta, K.O. (2010). A path-analytic study of some correlates predicting persistence and student’s success in distance education in nigeria. Turkish Online Journal of Distance Education, 11(1), 181-192. Pannen, P. (2016). Panduan pelaksanaan pendidikan jarak jauh 2016 (The guidance of implementation of distance education 2016). Jakarta: Ministry of Research, Technology and Higher Education. 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. Peebles, D. (2014). Gender analysis of open and distance learning in the Caribbean region. Retrieved February 06, 2019 from http://www.ckln.org/home/sites/default/ files/ Caribbean%2520Regional%2520Policy%2520Framework%2520for%252 Ratnaningsih, D.J. (2011). Pemodelan daya tahan belajar mahasiswa pendidikan tinggi jarak jauh dengan pendekatan regresi logistik biner (Modelling of learning resilience of distance learning students using binary logistic regression). Jurnal Matematika, Sains, dan Teknologi, 12 (2), 57-67. Rockinson-Szapkiw, A.J., Spaulding, L.S., & Spaulding, M.T. (2016). Identifying significant integration and institutional factors that predict online doctoral persistance. The Internet and Higher Education, 31, 101-112. Rodrigues de Oliveira, P., Aparecida Oesterreich, S., & Luci de Almeida, V. (2018). School dropout in graduate distance education: evidence from a study in the interior of Brazil. Educacao Pesquisa, 44(e165786), 1–20. Rovai, A. (2002). Building sense of community at a distance. International Review of Research in Open and Distance Learning, 3(1), 1-16. Saefuddin, A., & Ratnaningsih, D.J. (2008). Pemodelan daya tahan mahasiswa putus kuliah pada pendidikan tinggi jarak jauh dengan regresi Cox (Modeling of resilience of students dropout in distance learning using Cox regression ). Statistika, 8 (1), 1-12. Sembiring, M.G. (2014). Modeling the determinants of student retention in distance education institutions. International Journal of Continuing Education and Lifelong Learning, 6 (2), 15-28. Sembiring, M.G. (2015). Validating student satisfaction related to persistence, academic performance, retention and career advancement within ODL perspectives. Open Praxis, 7 (4), 325–337. Stoessel, K., Ihme, T. A., Barbarino, M., Fisseler, B., & Sturmer, S. (2015). Sociodemographic diversity and distance education: Who drops out from academic programs and why? Research in Higher Education, 56(3), 228-246. Thistoll, T., & Yates, A. (2016). Improving course completions in distance education: an institutional case study. Distance Education, 37(2), 180–195. Universitas Terbuka (2015). Laporan kerja tahunan Rektor Universitas Terbuka 2015 (Annual report of Rector of Universitas Terbuka 2015). Tangerang Selatan: Universitas Terbuka. Yukselturk, E., Ozekes, S., & Turel, Y.K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and E-Learning, 17 (1), 118–133.
Year 2020,
Volume: 21 Issue: 2, 141 - 152, 01.04.2020
Abstract
The high rate of drop out is still a problem in the distance learning system, including at the Universitas Terbuka (UT). At UT, the term dropout is better known as the status of non-active students. The study aims was to determine the median time and determinant of non-active student in distance learning in Indonesia. This study used a cohort analysis in student of biology department who first registered in 2012 to 2014. The median time of non-active students was identified by the Kaplan-Meier analysis and the determinant of non-active student was analyzed by Cox proportional hazard model. The percentage of non-active students in this study reached 42%, with half of the students becoming non-active in the first two semesters. Students who have a greater risk of becoming non-active are those who are >45 years old, women, employed, recent education is not relevant to the field of biology, knowledge of the concept of distance learning and laboratory practice is lacking, has never participated in online tutorials and face-to-face tutorials, and is not satisfied existing academic services. The optimization of the provision and quality of preferred learning services at the beginning of the semester will be able to avoid higher-risk of non-active students.
Keywords
References
- Ali, F., Zhou, Y., Hussain, K., Nair, P. K., & Ragavan, N. A. (2016). Does higher education service quality effect student satisfaction, image and loyalty? Quality Assurance in Education, 24(1), 70–94. Carr, S.(2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46 (23), A39-A41. Carreira, P., & Lopes, A. S. (2018). Pathways of adult student-workers in higher education: explaining the risks of early dropout, late dropout and graduation. XXVII Meeting of the Economics of Education Association. Retrieved February 01, 2019 from https://iconline.ipleiria.pt/handle/10400.8/3340 De La Varre, C., Irvin, M. J., Jordan, A. W., Hannum, W. H., & Farmer, T. W. (2014). Reasons for student dropout in an online course in a rural K-12 setting. Distance Education, 35(3), 324–344. Fredericksen, E., Swan, K., Pelz, W., Pickett, A.M., & Shea, P. (2000). Student satisfaction and perceived learning with online courses: principles and examples from the SUNY Learning Network. Journal of Asynchronous Learning Network, 4 (2), 7-41. 152 Kostopoulos, G., Kotsiantis, S., & Pintelas, P. (2015). Estimating student dropouts in distance higher education using semi-supervised techniques. The 19th Panhellenic Conference on Informatics, 2015 (pp. 38-43). New York: ACM. Moore, M.G., & Kearsley, G. (1996). Distance education: a systems view of online learning. California, USA: Wadswoth Publishing Company. Mulyana, A., & Yuni, D. (2014). Pengaruh faktor-faktor pembentuk loyalitas mahasiswa Universitas Terbuka (Effect of factors forming student loyalty in Universitas Terbuka). Derivatif, 8 (2), 41-49. Muslim, D., Raza, S. M. M., & Touseef, S. A. (2017). Major dropouts reasons of students in e-learning institutions of Pakistan. The Online Journal of Communication and Media, 3(4), 30–35. Ojokheta, K.O. (2010). A path-analytic study of some correlates predicting persistence and student’s success in distance education in nigeria. Turkish Online Journal of Distance Education, 11(1), 181-192. Pannen, P. (2016). Panduan pelaksanaan pendidikan jarak jauh 2016 (The guidance of implementation of distance education 2016). Jakarta: Ministry of Research, Technology and Higher Education. 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. Peebles, D. (2014). Gender analysis of open and distance learning in the Caribbean region. Retrieved February 06, 2019 from http://www.ckln.org/home/sites/default/ files/ Caribbean%2520Regional%2520Policy%2520Framework%2520for%252 Ratnaningsih, D.J. (2011). Pemodelan daya tahan belajar mahasiswa pendidikan tinggi jarak jauh dengan pendekatan regresi logistik biner (Modelling of learning resilience of distance learning students using binary logistic regression). Jurnal Matematika, Sains, dan Teknologi, 12 (2), 57-67. Rockinson-Szapkiw, A.J., Spaulding, L.S., & Spaulding, M.T. (2016). Identifying significant integration and institutional factors that predict online doctoral persistance. The Internet and Higher Education, 31, 101-112. Rodrigues de Oliveira, P., Aparecida Oesterreich, S., & Luci de Almeida, V. (2018). School dropout in graduate distance education: evidence from a study in the interior of Brazil. Educacao Pesquisa, 44(e165786), 1–20. Rovai, A. (2002). Building sense of community at a distance. International Review of Research in Open and Distance Learning, 3(1), 1-16. Saefuddin, A., & Ratnaningsih, D.J. (2008). Pemodelan daya tahan mahasiswa putus kuliah pada pendidikan tinggi jarak jauh dengan regresi Cox (Modeling of resilience of students dropout in distance learning using Cox regression ). Statistika, 8 (1), 1-12. Sembiring, M.G. (2014). Modeling the determinants of student retention in distance education institutions. International Journal of Continuing Education and Lifelong Learning, 6 (2), 15-28. Sembiring, M.G. (2015). Validating student satisfaction related to persistence, academic performance, retention and career advancement within ODL perspectives. Open Praxis, 7 (4), 325–337. Stoessel, K., Ihme, T. A., Barbarino, M., Fisseler, B., & Sturmer, S. (2015). Sociodemographic diversity and distance education: Who drops out from academic programs and why? Research in Higher Education, 56(3), 228-246. Thistoll, T., & Yates, A. (2016). Improving course completions in distance education: an institutional case study. Distance Education, 37(2), 180–195. Universitas Terbuka (2015). Laporan kerja tahunan Rektor Universitas Terbuka 2015 (Annual report of Rector of Universitas Terbuka 2015). Tangerang Selatan: Universitas Terbuka. Yukselturk, E., Ozekes, S., & Turel, Y.K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and E-Learning, 17 (1), 118–133.
There are 1 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | April 1, 2020 |
| Submission Date | February 16, 2019 |
| Published in Issue | Year 2020 Volume: 21 Issue: 2 |