Volume 11, Issue 4 (2023)
Health Educ Health Promot 2023, 11(4): 667-673 |
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Rabiei H, Barati M, Ayubi E, Borzouei S. Factors Associated with the Use of E-Learning among Medical University Students; An Application of Technology Acceptance Model. Health Educ Health Promot 2023; 11 (4) :667-673
URL: http://hehp.modares.ac.ir/article-5-72130-en.html
URL: http://hehp.modares.ac.ir/article-5-72130-en.html
1- Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
2- Autism Spectrum Disorders Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3- Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
4- Department of Internal Medicine, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
2- Autism Spectrum Disorders Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3- Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
4- Department of Internal Medicine, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
Abstract: (473 Views)
Aims: Determining the effective factors in the use of e-learning among students can be useful in providing appropriate and practical solutions to increase the use of e-learning. Therefore, this study aimed to determine the factors related to the behavior of using e-learning among medical university students using the technology acceptance model (TAM) as a conceptual framework.
Instrument & Methods: This cross-sectional study was conducted among 425 students of Hamadan University of Medical Sciences who were selected by stratified random sampling. The data collection tools included a questionnaire, including demographic data and TAM constructs. The data were analyzed in SPSS 23 software using one-way analysis of variance, independent t-test, Pearson correlation coefficient, and linear regression analysis.
Findings: The age range of participants was between 18 and 54 years with an average age of 24.2±3.96 years. The history of participating in e-learning classes was significantly related to all the constructs of the TAM (p<0.05). Also, undergraduate and graduate students had a significantly more positive attitude, higher perceived usefulness, and more usage intention and behavior concerning e-learning than professional doctorate students (p<0.05). Attitude (β=0.394), perceived usefulness (β=0.313), and external variables (β=0.196) were respectively the strongest predictors of intention to use e-learning (p<0.05). The intention of e-learning usage directly and significantly predicted the behavior of using it (β=0.483, p<0.05).
Conclusion: The TAM constructs predict the behavior of using e-learning among students.
Instrument & Methods: This cross-sectional study was conducted among 425 students of Hamadan University of Medical Sciences who were selected by stratified random sampling. The data collection tools included a questionnaire, including demographic data and TAM constructs. The data were analyzed in SPSS 23 software using one-way analysis of variance, independent t-test, Pearson correlation coefficient, and linear regression analysis.
Findings: The age range of participants was between 18 and 54 years with an average age of 24.2±3.96 years. The history of participating in e-learning classes was significantly related to all the constructs of the TAM (p<0.05). Also, undergraduate and graduate students had a significantly more positive attitude, higher perceived usefulness, and more usage intention and behavior concerning e-learning than professional doctorate students (p<0.05). Attitude (β=0.394), perceived usefulness (β=0.313), and external variables (β=0.196) were respectively the strongest predictors of intention to use e-learning (p<0.05). The intention of e-learning usage directly and significantly predicted the behavior of using it (β=0.483, p<0.05).
Conclusion: The TAM constructs predict the behavior of using e-learning among students.
Keywords: Education, Distance [Mesh], Educational Technology [MeSH], Education, Medical [MeSH], Behavior [MeSH]
Article Type: Descriptive & Survey |
Subject:
Technology of Health Education
Received: 2023/10/25 | Accepted: 2023/11/25 | Published: 2023/11/30
Received: 2023/10/25 | Accepted: 2023/11/25 | Published: 2023/11/30
References
1. Akbarilakeh M, Razzaghi A, Delavar Pour Moghaddam H. Attitudes of faculty members towards using e-learning. Res Dev Med Educ. 2019;8(1):12-9. [Link] [DOI:10.15171/rdme.2019.003]
2. O'Doherty D, Dromey M, Lougheed J, Hannigan A, Last J, McGrath D. Barriers and solutions to online learning in medical education-an integrative review. BMC Med Educ. 2018;18(1):130. [Link] [DOI:10.1186/s12909-018-1240-0]
3. Gachanja F, Mwangi N, Gicheru W. E-learning in medical education during COVID-19 pandemic: experiences of a research course at Kenya Medical Training College. BMC Med Educ. 2021;21(1):612. [Link] [DOI:10.1186/s12909-021-03050-7]
4. Huynh R. The role of E-learning in medical education. Acad Med. 2017;92(4):430. [Link] [DOI:10.1097/ACM.0000000000001596]
5. Evgeniou E, Loizou P. The theoretical base of e-learning and its role in surgical education. J Surg Educ. 2012;69(5):665-9. [Link] [DOI:10.1016/j.jsurg.2012.06.005]
6. Bernard R, Abrami P, Lou Y, Borokhovski E, Wade A, Wozney L, et al. How does distance education compare with classroom instruction? a meta-analysis of the empirical literature. Rev Educ Res. 2004;74:379-439. [Link] [DOI:10.3102/00346543074003379]
7. Vallée A, Blacher J, Cariou A, Sorbets E. Blended learning compared to traditional learning in medical education: Systematic review and meta-analysis. J Med Internet Res. 2020;22(8):e16504. [Link] [DOI:10.2196/16504]
8. Co M, Cheung KYC, Cheung WS, Fok HM, Fong KH, Kwok OY, et al. Distance education for anatomy and surgical training-A systematic review. Surgeon. 2022;20(5):e195-205. [Link] [DOI:10.1016/j.surge.2021.08.001]
9. Zolfaghari M, Negarandeh R, Eybpoosh S. Developing a blended learning program for nursing and midwifery students in Iran: Process and preliminary outcomes. Iran J Nurs Midwifery Res. 2013;18(1):20-6. [Link]
10. Ali S, Uppal MA, Gulliver SR. A conceptual framework highlighting e-learning implementation barriers. Inf Technol People. 2018;31(1):156-80. [Link] [DOI:10.1108/ITP-10-2016-0246]
11. Almaiah MA, Al-Khasawneh A, Althunibat A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Educ Inf Technol. 2020;25(6):5261-80. [Link] [DOI:10.1007/s10639-020-10219-y]
12. Song L, Singleton ES, Hill JR, Koh MH. Improving online learning: Student perceptions of useful and challenging characteristics. Internet Higher Educ. 2004;7(1):59-70. [Link] [DOI:10.1016/j.iheduc.2003.11.003]
13. Iravani M, Nasab MB, Bahmaei H, Ghanbari S, Mohaghegh Z, Siahkal SF. The level of satisfaction and quality of E-learning in medical universities of Iran during the epidemic of COVID-19. J Educ Health Promot. 2022;11:9. [Link] [DOI:10.4103/jehp.jehp_1555_20]
14. Davis FD. A technology acceptance model for empirically testing new end-user information systems: Theory and results. Cambridge: Massachusetts Institute of Technology; 1985. [Link]
15. Al Kurdi B, Alshurideh M, Salloum SA. Investigating a theoretical framework for e-learning technology acceptance. Int J Electrical Comput Eng. 2020;10(6):6484-96. [Link] [DOI:10.11591/ijece.v10i6.pp6484-6496]
16. McKinnon K, Igonor A, editors. Explaining eLearning perceptions using the technology acceptance model and the theory of planned behavior. San Diego: Association for the Advancement of Computing in Education (AACE); 2008. [Link]
17. Punnoose AC. Determinants of intention to use eLearning based on the technology acceptance model. J Inf Technol Educ Res. 2012;11(1):301-37. [Link] [DOI:10.28945/1744]
18. Xing W, Ao L, Xiao H, Liang L. Chinese nurses' preferences for and attitudes about e-learning in continuing education: A correlational study. J Contin Educ Nurs. 2020;51(2):87-96. [Link] [DOI:10.3928/00220124-20200115-08]
19. Samra RK, Nirola A, Verma A, Nagpal A, Thakur M. Dental students' perception on the impact of E-learning in continuing dental education during the current pandemic scenario. Indian J Dental Sci. 2021;13(2):61. [Link] [DOI:10.4103/ijds.ijds_14_21]
20. Venkatesh V. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res. 2000;11(4):342-65. [Link] [DOI:10.1287/isre.11.4.342.11872]
21. Thapa P, Bhandari SL, Pathak S. Nursing students' attitude on the practice of e-learning: A cross-sectional survey amid COVID-19 in Nepal. PloS one. 2021;16(6):e0253651. [Link] [DOI:10.1371/journal.pone.0253651]
22. Abbasi MS, Ahmed N, Sajjad B, Alshahrani A, Saeed S, Sarfaraz S, et al. E-Learning perception and satisfaction among health sciences students amid the COVID-19 pandemic. Work. 2020;67(3):549-56. [Link] [DOI:10.3233/WOR-203308]
23. Ikram UZ, Essink-Bot ML, Suurmond J. How we developed an effective e-learning module for medical students on using professional interpreters. Med Teach 2015;37(5):422-7. [Link] [DOI:10.3109/0142159X.2014.939579]
24. Khasawneh R, Simonsen K, Snowden J, Higgins J, Beck G. The effectiveness of e-learning in pediatric medical student education. Med Educ Online. 2016;21:10. [Link] [DOI:10.3402/meo.v21.29516]
25. Ajzen I. Understanding attitudes and predictiing social behavior. London: Pearson; 1980. [Link]
26. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Manag Sci. 1989;35(8):982-1003. [Link] [DOI:10.1287/mnsc.35.8.982]
27. Dillon A, Morris MG. User acceptance of information technology theory and models. LearnTech Lib. 1996;31:3-32. [Link]
28. Park SY. An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. J Educ Technol Soc. 2009;12(3):150-62. [Link]
29. Selim HM. An empirical investigation of student acceptance of course websites. Computers Educ. 2003;40(4):343-60. [Link] [DOI:10.1016/S0360-1315(02)00142-2]
30. Khorasani A, Abdolmaleki J, Zahedi H. Factors affecting E-learning acceptance among students of Tehran University of Medical Sciences based on technology acceptance model (TAM). Iran J Med Educ. 2012;11(6):664-73. [Persian] [Link]
31. 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. [Link] [DOI:10.1016/j.procs.2017.01.196]
32. Barzekar H, Safdari R, Eshraghiyan MR, Dargahi H. The study of organizational factors affecting the application of information technology by middle managers in TUMS hospitals. Payavard Salamat. 2013;7(2):123-32. [Persian] [Link]
33. dos Reis RA, Freitas MdCD. Critical factors on information technology acceptance and use: An analysis on small and medium Brazilian clothing industries. Procedia Comput Sci. 2014;31:105-14. [Link] [DOI:10.1016/j.procs.2014.05.250]
34. Horton W, editor. Designing courseware for mobile devices, Mobile learning for expending educational opportunities; 2005. [Link]
35. Khodadad Hoseiny SH, Noori A, Zabihi MR. E-learning acceptance in higher education: Application of flow theory, technology acceptance model & e-service quality. Res Plan Higher Educ. 2023;19(1):111-36. [Persian] [Link]
36. Kon CL. Mobile learning: Different technologies aspects. designing usable systems, computer and internet technologies university of Strathclyde [Internet]. Perth: Meta Press. 2009- [Cited 2009, April, 17]. Available from: http://inderscience.metapress.com/content/?k=Kon%2c+CL+(2009). [Link]
37. Kumar S, Vigil K. The net generation as preservice teachers: Transferring familiarity with new technologies to educational environments. J Digital Learn Teach Educ. 2011;27(4):144-53. [Link] [DOI:10.1080/21532974.2011.10784671]
38. Kunda D, Brooks L. Assessing important factors that support component‐based development in developing countries. Inf Technol Dev. 2000;9(3-4):123-39. [Link] [DOI:10.1080/02681102.2000.9525327]
39. Madadi Y, Iravani H, Mohammadi Y. Investigating the factors effective on the acquaintance with and use of Information and Communication Technology (ICT) in organizational responsibilities of the faculty members of the college of agriculture and natural resources, university of Tehran. J Inf Technol Manag. 2014;6(1):127-44. [Persian] [Link]
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