Interdisciplinary Journal of Virtual Learning in Medical Sciences

Published by: Kowsar

The Correlation Between Students’ Attitudes and Persistence in E-Learning

Mahdi Mahmodi 1 , * and Maryam Jalali Moghadam 1
Authors Information
1 Department of Educational Sciences, Payame Noor University, Tehran, Iran
Article information
  • Interdisciplinary Journal of Virtual Learning in Medical Sciences: 10 (2); e89195
  • Published Online: September 11, 2019
  • Article Type: Research Article
  • Received: January 14, 2019
  • Revised: June 16, 2019
  • Accepted: June 16, 2019
  • DOI: 10.5812/ijvlms.89195

How to Cite: Mahmodi M, Jalali Moghadam M. The Correlation Between Students’ Attitudes and Persistence in E-Learning, Interdiscip J Virtual Learn Med Sci. Online ahead of Print ; 10(2):e89195. doi: 10.5812/ijvlms.89195.

Copyright © 2019, Interdisciplinary Journal of Virtual Learning in Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Methods
4. Results
5. Discussion
  • 1. Sherry L. Issues in distance education learning. International J Educ Telecommun. 1996;1(4):337-45.
  • 2. Ebrahimzadeh I. Pedagogy based on information technology: Conceptual query. J Paik Noor. 2008;4(4):4-13.
  • 3. Naghavi M. [Study of teachers and students attitude toward e-learning: Surveying in Iran's e-learning universities]. Q J Res Plann High Educ. 2007;13(1):157-76. Persian.
  • 4. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Manage Sci. 1989;35(8):982-1003. doi: 10.1287/mnsc.35.8.982.
  • 5. Liaw SS. Considerations for developing constructivist web-based learning. International Journal of Instructional Media Int J Instr Media. 2004;31:309-19.
  • 6. Tello SF. An analysis of the relationship between instructional interaction and student persistence in online education [dissertation]. Lowell: University of Massachusetts; 2002.
  • 7. Jung Y, Lee J. Learning engagement and persistence in massive open online courses (MOOCS). Comput Educ. 2018;122:9-22. doi: 10.1016/j.compedu.2018.02.013.
  • 8. Herbert M. Staying the course: A study in online student satisfaction and retention. Online J Distance Learn Adm. 2006;9(4).
  • 9. Smith B. E-learning technologies: A comparative study of adult learners enrolled on blended and online campuses engaging in a virtual classroom [dissertation]. 2010.
  • 10. Prensky M. Digital game-based learning. New York: McGraw-Hill; 2000.
  • 11. Tinto V. Leaving College: Rethinking the causes and cures of student attrition. Chicago: University of Chicago Press; 1987.
  • 12. Svedberg MK. Self-directed learning and persistence in online asynchronous undergraduate programs [dissertation]. Virginia Polytechnic Institute and State University; 2010.
  • 13. Pascarella ET, Terenzini PT. How college affects students: Findings and insights from twenty years of research. San Francisco: Jossey-Bass; 1991.
  • 14. Wu B, Chen X. Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Comput Hum Behav. 2017;67:221-32. doi: 10.1016/j.chb.2016.10.028.
  • 15. Tajudeen Shittu A, Madarsha Basha K, Suryani Nik AbdulRahman N, Badariah Tunku Ahmad T. Investigating students' attitude and intention to use social software in higher institution of learning in Malaysia. Multicult Educ Technol J. 2011;5(3):194-208. doi: 10.1108/17504971111166929.
  • 16. Sujeet KS, Jyoti KC. Technology acceptance model for the use of learning through websites among students in Oman. Int Arab J E-Technol. 2013;3(1):44-9.
  • 17. Altawallbeh M, Soon F, Thiam W, Alshourah S. Mediating role of attitude, subjective norm and perceived behavioural control in the relationships between their respective salient beliefs and behavioural intention to adopt e-learning among instructors in jordanian Universities. J Educ Pract. 2015;6(11):152-9.
  • 18. Hussein Z. Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Comput Sci. 2017;105:159-64. doi: 10.1016/j.procs.2017.01.196.
  • 19. Mattheos N, Nattestad A, Schittek M, Attstrom R. A virtual classroom for undergraduate periodontology: A pilot study. Eur J Dent Educ. 2001;5(4):139-47. [PubMed: 11683890].
  • 20. Hrastinski S, Keller C, Carlsson SA. Design exemplars for synchronous e-learning: A design theory approach. Comput Edu. 2010;55(2):652-62. doi: 10.1016/j.compedu.2010.02.025.
  • 21. Falloon G. Making the connection: Moore's theory of transactional distance and its relevance to the use of a virtual classroom in postgraduate online teacher education. J Res Technol Educ. 2011;43(3):187-209. doi: 10.1080/15391523.2011.10782569.
  • 22. Fujioka-Ito N. Designing a curriculum for a distance learning class: An example of a first-year japanese course. Theory Pract Lang Studies. 2013;3(10). doi: 10.4304/tpls.3.10.1717-1725.
  • 23. Branon RF, Essex C. Synchronous and asynchronous communication tools in distance education. TechTrends. 2001;45(1):36. doi: 10.1007/bf02763377.
  • 24. Skylar AA. A comparison of asynchronous online text-based lectures and synchronous interactive web conferencing lectures. Issues Teach Educ. 2009;18(2):69-84.
  • 25. Bertea P. Measuring students attitude towards e-learning A case study. Proceedings of the 5th standing conference on e-learning and software for development, April 9-10. Bucharist: Romania. 2009. p. 1-8.
  • 26. Willging PA, Johnson SD. Factors that influence students' decision to drop out of online courses. J Asynchronous Learn Networks. 2004;8(4):105-18.
  • 27. Berg ZL, Huang YP. A model for sustainable student retention: A holistic perspective on the student dropout problem with special attention to e-learning. DEOSNEWS. 2004;13(5):97-108.
  • 28. Rovai AP. In search of higher persistence rates in distance education online programs. Internet High Educ. 2003;6(1):1-16. doi: 10.1016/s1096-7516(02)00158-6.
  • 29. Wise AF, Cui Y, Jin WQ, Vytasek J. Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. Internet High Educ. 2017;32:11-28. doi: 10.1016/j.iheduc.2016.08.001.

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