Interdisciplinary Journal of Virtual Learning in Medical Sciences

Interdisciplinary Journal of Virtual Learning in Medical Sciences

Comparing Faculty and Student Acceptance of Mobile Learning in Blended Learning Education: A Cross-sectional Study

Document Type : Original Article

Authors
Mojtaba Rezai Rad
Mahdi Roudi
Department of Educational Technology, Sar.C., Islamic Azad University, Sari, Iran
10.30476/ijvlms.2026.110068.1367
Abstract
Background: Mobile learning is a key component of blended learning in higher education, particularly in medical education. Understanding faculty members and students’ perceptions and acceptance of mobile learning is critical for effective implementation. This study aimed to compare the perceptions and acceptance of mobile learning among faculty members and medical students in a blended learning environment.
Methods: This applied, comparative cross-sectional study was carried out between September 2024 and January 2025 at Islamic Azad University in Mazandaran, Iran, involving faculty members (n = 182) and students (n = 337) from medical disciplines. Using stratified random sampling, 123 faculty members and 181 students were selected for participation. Data were collected using four researcher-developed 30-item questionnaires assessing perception and acceptance of mobile learning. A panel of 10 experts confirmed content validity, and reliability coefficients (Cronbach’s alpha) ranged from 0.82 to 0.88. Data analysis included descriptive statistics; one-sample t-tests against the theoretical midpoint (cut-off = 3), independent-samples t-tests with 95% confidence intervals, Hedges’ g effect sizes, and Pearson correlation analysis.
Results: Both faculty members and students reported their perception and acceptance scores significantly above the theoretical midpoint (p < 0.001), indicating a positive orientation toward mobile learning. There was no statistically significant difference in perception scores between students and faculty members (p = 0.385), indicating a similar level of awareness in both groups. However, faculty demonstrated significantly higher acceptance than students (p < 0.001, Hedges’ g = 0.53), reflecting greater readiness, confidence, and willingness to adopt mobile learning. Perception scores were positively correlated with acceptance in both students (r = 0.62, p < 0.001) and faculty (r = 0.58, p < 0.001), highlighting the role of perceived value in shaping adoption.
Conclusion: Findings show that both faculty and students hold positive perceptions and acceptance of mobile learning, with faculty members demonstrating greater readiness. Successful implementation requires focused faculty development, student digital readiness training, and adequate technological infrastructure to optimize integration and educational outcomes in medical education.

Highlights

Mojtaba Rezaei Rad (Google Scholar)

Keywords
Learning
Mobile Learning
Blended Learning
Technology
Technology Acceptance
Education
Medical
  1. Alenezi, M., Wardat, S., & Akour, M. The need of integrating digital education in higher education: Challenges and opportunities. Sustainability. 2023;15(6):4782.‏ doi: 10.3390/su15064782.
  2. Berezi IU. Virtual learning environment: Redefining higher educational delivery for efficiency and accessibility. Int J Educ Manag. 2025;1(1):451-67.
  3. Zeighami M, Abolghasemi M, Sharifirad G. Application of Active Teaching Methods in Medical Education. J Health. 2023;14(3):335-55. [In Persian]
  4. Arbab A, Shatat A, Aldada A. Navigating the digital landscape: Addressing challenges in e-learning for the modern era. In: 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS); 2024 January 28; Manama, Bahrain. USA: IEEE; 2024. doi: 10.1109/ICETSIS61505.2024.10459474.
  5. Prajapati A. Transforming education: The revolutionary impact of information and communication technology in 21st century classrooms. Vidya-A J Gujarat Univ. 2025;4(2):408-15. doi: 10.47413/ksy5q983.
  6. Ustun AB. Effects of mobile learning in blended learning environments. Bilgi ve İletişim Teknolojileri Dergisi. 2019;1(1):1-14.
  7. Sosnova M, Hlianenko K, Sosnova V, Tsyna V, Tsyna A. Enhancing the effectiveness of learning through hybrid education methods. Revista Eduweb. 2025;19(2):95-106. doi: 10.46502/issn.1856-7576/2025.19.02.7.
  8. Elgohary M, Palazzo FS, Breckwoldt J, Cheng A, Pellegrino J, Schnaubelt S, Greif R, Lockey A. Blended learning for accredited life support courses - A systematic review. Resusc Plus. 2022;10:100240. doi: 10.1016/j.resplu.2022.100240. PubMed PMID: 35592876; PubMed Central PMCID: PMC9112020.
  9. Criollo-C S, Guerrero-Arias A, Jaramillo-Alcázar Á, Luján-Mora S. Mobile learning technologies for education: Benefits and pending issues. Appl. Sci. 2021;11(9):4111. doi: 10.3390/app11094111.
  10. Qashou A. Influencing factors in M-learning adoption in higher education. Educ Inf Technol. 2021;26(2):1755-85. doi: 10.1007/s10639-020-10323-z.
  11. Moradi R, Arasteh Saleh Kohi M. Enriching the learning Environment based on Mobile Technologies and Investigating its Impact on Students' learning and Academic Engagement in Chemistry. New Educational Approaches. 2024;18(1):47-66. doi: 10.22108/nea.2023.136616.1868. [In Persian]
  12. Bahrami F, Nazarzadeh Zare M. The effect of institutional factors on faculty adoption of blended learning. Technol Educ. 2023;17(3):671-82. doi: 10.22061/tej.2023.9693.2887. [In Persian]
  13. Al-Emran M, Mezhuyev V, Kamaludin A. Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technol Soc. 2020;61:101247. doi: 10.1016/j.techsoc.2020.101247.
  14. Mirmoghtadaie Z, Ahmady S. The Effectiveness of Blended Learning in the Field of Medical Education: Explaining Dimensions and Components Based on Stakeholder Experiences. J Med Educ Dev. 2019;12(33):26-33. doi: 10.29252/edcj.12.33.42.
  15. Masters K, Ellaway RH, Topps D, Archibald D, Hogue RJ. Mobile technologies in medical education: AMEE Guide No. 105. Med Teach. 2016;38(6):537-49. doi: 10.3109/0142159X.2016.1141190. PubMed PMID: 27010681.
  16. Almulhem JA. Factors, Barriers, and Recommendations Related to Mobile Health Acceptance among the Elderly in Saudi Arabia: A Qualitative Study. Healthcare (Basel). 2023;11(23):3024. doi: 10.3390/healthcare11233024. PubMed PMID: 38063592; PubMed Central PMCID: PMC11080935.
  17. Akbari Aghdam H, Najimi A, Zandi-esfahani H. Evaluation of Learning and Satisfaction of Medical Internship Students in Offering Orthopedic Courses: Traditional Education and Blended Education. Iranian Journal of Medical Education. 2022;22:110-19. [In Persian]
  18. Fagan MH. Factors influencing student acceptance of mobile learning in higher education. Comput Sch. 2019;36(2):105-121. doi: 10.1080/07380569.2019.1603051.
  19. Almaiah MA, Alismaiel OA. Examination of factors influencing the use of mobile learning system: An empirical study. Educ Inf Technol. 2019;24(1):885-909. doi: 10.1007/s10639-018-9810-7.
  20. Ghoraishi Khorasgani MS. Students' lived experience of m-learning in the COVID-19 crisis: A phenomenological study of the challenges. TEJ. 2022;16(2):323-38. doi: 10.22061/tej.2022.8400.2658. [In Persian]
  21. Pérez-Garcias A, Tur Ferrer G, Villatoro Moral S, Darder-Mesquida A. Flexible Learning Itineraries in Digital Environments for Personalised Learning in Teacher Training. RIED-Revista Iberoamericana de Educación a Distancia. 2022;25(2):173-193. doi: 10.5944/ried.25.2.32326.
  22. Oo MS, Schofield S, Aung ST, Thwin MM, Oo SS, Yee KT, Lwin MM, Zakaria AR. Integrating a Mobile-Learning Platform for Enhancing Clinical Teaching: The Learners’ Perspective. Int J Learn Teach Educ Res. 2022;21(11):87-111. doi: 10.26803/ijlter.21.11.6.
  23. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149-1160. doi: 10.3758/BRM.41.4.1149. PubMed PMID: 19897823.
  24. Zeib F, Tariq R. Equity challenges in academic satisfaction through online learning platforms and post-COVID implications using multigroup analysis. Educational Technology & Society. 2024;27(4):302-318. doi: 10.30191/ETS.202410_27(4).SP06.
  25. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Lawrence Erlbaum Associates; 1988. doi: 10.4324/9780203771587.
  26. Nikolopoulou K, Gialamas V, Lavidas K. Acceptance of mobile phone by university students for their studies: an investigation applying UTAUT2 model. Educ Inf Technol. 2020;25(5):4139-4155. doi: 10.1007/s10639-020-10157-9.
  27. Liesa-Orús M, Latorre-Cosculluela C, Vázquez-Toledo S, Sierra-Sánchez V. The Technological Challenge Facing Higher Education Professors: Perceptions of ICT Tools for Developing 21st Century Skills. Sustainability. 2020;12(13):5339. doi: 10.3390/su12135339.
  28. Biswas B, Roy SK, Roy F. Students Perception of Mobile Learning during COVID-19 in Bangladesh: University Student Perspective. Aquademia. 2020;4(2):ep20023. doi: 10.29333/aquademia/8443.
  29. Klimova B, Polakova P. Students’ perceptions of an EFL vocabulary learning mobile application. Educ Sci. 2020;10(2):37-45. doi: 10.3390/educsci10020037.
  30. Keiper MC, White A, Carlson CD, Lupinek JM. Student perceptions on the benefits of Flipgrid in a HyFlex learning environment. J Educ Bus. 2021;96(6):343-351. doi: 10.1080/08832323.2020.1832431.
  31. Rezaeirad M, Ahangari Kalaei Z. Comparison of present and future attitudes of medical students and faculty members toward the use of mobile learning: A case study of Islamic Azad University of Mazandaran. Res Curric Plann. 2022;19(74):46-54.
  32. Sprenger DA, Schwaninger A. Technology acceptance of four digital learning technologies (classroom response system, classroom chat, e-lectures, and mobile virtual reality) after three months’ usage. Int J Educ Technol High Educ. 2021;18(1):8. doi: 10.1186/s41239-021-00243-4.
  33. Antonietti C, Cattaneo A, Amenduni F. Can teachers' digital competence influence technology acceptance in vocational education? Comput Hum Behav. 2022;132:107266. doi: 10.1016/j.chb.2022.107266.
  34. Nikolopoulou K, Gialamas V, Lavidas K, et al. Teachers’ Readiness to Adopt Mobile Learning in Classrooms: A Study in Greece. Tech Know Learn. 2021;26:53–77. doi:10.1007/s10758-020-09453-7.
  35. Boone HN Jr, Boone DA. Analyzing Likert Data. J Ext. 2012;50(2):Article 2TOT2. doi: 10.34068/joe.50.02.48.
  36. Carifio J, Perla R. Resolving the 50-year debate around using and misusing Likert scales. Med Educ. 2008;42(12):1150-1152. doi: 10.1111/j.1365-2923.2008.03172.x. PubMed PMID: 19120943.

  • Receive Date 14 December 2025
  • Revise Date 11 February 2026
  • Accept Date 18 February 2026
  • Publish Date 01 March 2026