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Dergiler / Turkish Online Journal of Distance Education / 2019 / Cilt: 20 - Sayı: 1

Survival Modeling on Non Active Students’ Study of Universitas Terbuka: A Case Study

Dewi Juliah RATNANINGSIH
Asep SAEFUDDIN
Anang KURNIA
Wayan MANGKU
Sayfa
177–190
Yayın Tarihi
2019-01-01
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Covariate that influenced the survival of study was course of study, age, marital status, employment status, educational background, number of credits taken, 1st semester’s result, Grade Point Average (GPA), and the courses taken per semester.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Survival analysis is a statistical method, a part of the Generalized Linear Models, used to study the survival time of an individual to an event.
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Temel Bulgular & Veri:The application of survival analysis is indispensable in open and distance education, like Universitas Terbuka (UT), Indonesia.
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Sonuç & Katkı:Covariate that influenced the survival of study was course of study, age, marital status, employment status, educational background, number of credits taken, 1st semester’s result, Grade Point Average (GPA), and the courses taken per semester.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Survival analysis · non active students · application and case studies

Abstract

Survival analysis is a statistical method, a part of the Generalized Linear Models, used to study the survival time of an individual to an event. The application of survival analysis is indispensable in open and distance education, like Universitas Terbuka (UT), Indonesia. This analysis can determine students’ durability of study opportunities within a certain time. The analysis showed that the appropriate survival model to describe the covariate that was supposed to influence the study durability of the UT students of the Non Basic Education Program was Log Logistics. Covariate that influenced the survival of study was course of study, age, marital status, employment status, educational background, number of credits taken, 1st semester’s result, Grade Point Average (GPA), and the courses taken per semester.

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Bu Makaleye Atıf Yap

RATNANINGSIH, D. J., SAEFUDDIN, A., KURNIA, A., MANGKU, W. (2019). Survival Modeling on Non Active Students’ Study of Universitas Terbuka: A Case Study. Turkish Online Journal of Distance Education, Cilt: 20 - Sayı: 1, 177–190. https://ajindex.com/articles/140061

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Kaynakça (11)

  1. Bean, J.P. (1982). Student attrition, intentions, and confidence: Interaction effect in the a path model. Research in Higher Education, 17, 291-320.
  2. Cain, K.C., Harlow, S.D., Little, R.J., Nan, B., Yosef, M., Taffe, J.R. & Elliot, M.R. (2011). Bias due to left trucation and left censoring in longitudinal studies of developmental and disease processes. American Journal of Epidemiology,173(9), 1078-1084.
  3. Collet, D. (1997). Modelling survival data in medical research. Second Edition. London: Chapman & Hall. Cox, D.R. and Oakes, D. (1984). Analysis of survival data. London: Chapman & Hall. Dobson, A.J. (2002). An introduction to generalized linear models. Second Edition. London: Chapman & Hall. Gijbels, I. (2010). Censored data. Wiley Interdisciplinary Reviews: Computational Statistics, 2(2), 178-188.
  4. Islam, S. (2010). Readiness for self learning of Universitas Terbuka and high school students in open and distance learning higher education system in Indonesia. Journal of Open and Distance Education, 11(1), 1-14.
  5. Jones, B.S. and Branton, R.P. (2005). Beyond logit and probit: Cox duration models of single, repeating, and competing events for state policy adoption. State Politics and Policy Quarterly, 5(2), 420-443.
  6. Kadarko, W. (2000). Understanding students' learning styles and strategies. Journal of Open Distance Education, 3(2), 1-15.
  7. Klapproth, F. and Schaltz, P. (2014). Who is retained in school, and when? Survival analysis of predictors of grade retention in Luxembourgish secondary school. European Journal of Psychology of Education A Journal of Education and Development, 29(3), 119-136.
  8. Law, A.M. and Kelton, W.D. (2000). Simulation modeling and analysis (3rd ed). New York: McGraw-Hill. Lee, E.T. (1992). Statistical methods for survival data analysis. New York: John Wiley & Sons Inc. Lee, M.C. (2014). Business bankruptcy prediction based on survival analysis approach. International Journal of Computer Science & Information Tecnology, 6(2), 103-119.
  9. McCullagh, P. and Nelder, J.A. (1983). Generalized linear models. Second Edition. London: Chapman & Hall. McCormick, N.J. and Lucas, M.S. (2014). Student retention and success: Faculty initiatives at Middle Tennessee State University. Journal of Student Success and Retension,1(1), 1-12.
  10. Okello, J.O. and Abdou, D.K. (2014). Parametric model and future event prediction base on right cencored data. American Journal of Mathematics and Statistics, 4(5), 205-213.
  11. Rahayu, D.P. (2009). An analysis of characteristics of O