SavedAdd your journal

Journals / Turkish Online Journal of Distance Education / 2019 / Cilt: 20 - Sayı: 3

Should I Stay, or Should I Go? Revisiting Student Retention Models in Distance Education

Marko RADOVAN
Pages
29–40
Publication Date
2019-07-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our analysis suggests that the circumstances that affect the dropout rate should be considered in the planning and implementation of distance education programs.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Supporters of distance education highlight the many advantages of online learning as compared to face-to-face education, such as greater openness, diversity of teaching materials, adjustment to student learning styles, the speed of learning, and more.
📊
Key Findings & Data:Despite the advantages, the growing number of programs, and the increased acceptance of distance education in national strategies worldwide, distance learning providers are faced with the problem of dropouts.
💡
Conclusion & Impact:Our analysis suggests that the circumstances that affect the dropout rate should be considered in the planning and implementation of distance education programs.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Distance education · dropout · retention models

Abstract

Supporters of distance education highlight the many advantages of online learning as compared to face-to-face education, such as greater openness, diversity of teaching materials, adjustment to student learning styles, the speed of learning, and more. Despite the advantages, the growing number of programs, and the increased acceptance of distance education in national strategies worldwide, distance learning providers are faced with the problem of dropouts. Studies have shown that the dropout rate in distance educational programs is higher than the dropout rate in traditional programs. Understanding of the reasons and determinants behind dropping out of an educational program is a challenging task for educators, researchers, and policy-makers. In this paper, we describe the main characteristics of distance education, and analyze the models that try to describe and prevent students from dropping out of these programs. Our analysis suggests that the circumstances that affect the dropout rate should be considered in the planning and implementation of distance education programs.

🔖

Cite This Article

RADOVAN, M. (2019). Should I Stay, or Should I Go? Revisiting Student Retention Models in Distance Education. Turkish Online Journal of Distance Education, Cilt: 20 - Sayı: 3, 29–40. https://ajindex.com/articles/141004

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Bawa, P. (2016). Retention in Online Courses: Exploring Issues and Solutions—A Literature Review. SAGE Open, 6(1), 2158244015621777. https://doi.org/10.1177/2158244015621777
  2. Bean, J. P., & Metzner, B. S. (1985). A Conceptual Model of Nontraditional Undergraduate Student Attrition. Review of Educational Research, 55(4), 485–540.
  3. Benson, S. (2013, August 6). What is Personalized Learning? A Working Draft. Retrieved August 9, 2018, from https://scottebenson.wordpress.com/2013/08/06/what-is-personalized-learning/
  4. Brewer, S. A., & Yucedag-Ozcan, A. (2013). Educational Persistence: Self-Efficacy and Topics in a College Orientation Course. Journal of College Student Retention: Research, Theory & Practice, 14(4), 451–465. https://doi.org/10.2190/CS.14.4.B
  5. Carr, S. (2000). As Distance Education Comes of Age, the Challenge Is Keeping the Students. Chronicle of Higher Education, 46(23), 39–41.
  6. Carroll, D., Ng, E., & Birch, D. (2013). Strategies to Improve Retention of Postgraduate Business Students in Distance Education Courses: An Australian Case. Turkish Online Journal of Distance Education, 14(1), 140–153.
  7. Conrad, R.-M., & Donaldson, J. A. (2011). Engaging the Online Learner: Activities and Resources for Creative Instruction (Updated edition). San Francisco: Jossey-Bass. Diaz, D. P. (2002). Online drop rates revisited. The Technology Source, (1). Retrieved from http://technologysource.org/article/online_drop_rates_revisited/
  8. Docebo. (2018). eLearning Trends for 2018. London; Toronto; Athens: Docebo Inc. Retrieved from https:// www.docebo.com/resource/whitepaper-elearning-trends-2018/
  9. Eraut, M. (2009). Transfer of knowledge between education and workplace settings. In H. Daniels, H. Lauder, & J. Porter (Eds.), Knowledge, values, and educational policy: a critical perspective (pp. 65–84). London; New York, NY: Routledge. Flood, J. (2002). Read all about it: online learning facing 80% attrition rates. The Turkish Online Journal of Distance Education, 3(2). Retrieved from http://tojde.anadolu.edu.tr/makale_goster.php?id=48
  10. Gomez-Zermeno, M. G., & Aleman de La Garza, L. (2016). Research Analysis on MOOC Course Dropout and Retention Rates. Turkish Online Journal of Distance Education, 17(2), 3–14.
  11. Gregori, P., Martínez, V., & Moyano-Fernández, J. J. (2018). Basic actions to reduce dropout rates in distance learning. Evaluation and Program Planning, 66, 48–52. https://doi.org/10/gcr4b7
  12. Herbert, M. (2006). Staying the Course: A Study in Online Student Satisfaction and Ret