Experts' consensus over key components of online learning environments in medical education: a modified e-Delphi study
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Abstract
Objective: To achieve expert consensus on the essential components for effective online learning environments in medical education using a modified e-Delphi approach.
Methods: A purposive sampling strategy was used to recruit 18 professionals from seven countries, including medical educationists, instructional designers, and health professions faculty, all with minimum two years of experience in technology-enhanced learning environments. Two Delphi rounds were conducted online, where experts rated nine components and 25 subcomponents using a 4-point Likert scale for appropriateness and applicability. Statistical analysis included descriptive statistics (mean, standard deviation) and Friedman’s non-parametric rank correlation test, with ≥75% agreement set as the consensus threshold.
Results: Fifteen out of 18 experts participated (response rate: 83.3%) and evaluated nine components and 24 subcomponents for appropriateness and applicability. Consensus was achieved across all components, with "Institutional Support" (3.6±0.50) and "Digital Capability" (3.6±0.51) receiving 100% agreement, while "Learning Facilitator" had the lowest score (3.2±0.51; 86.7% agreement). Expert feedback led to refinements in definitions and nomenclature, e.g. renaming "Pedagogical Practices" to "Cybergogical Practices" for better conceptual clarity. Friedman test showed no significant differences in rankings (p >0.05), confirming consensus. The finalized framework supports curriculum design, faculty development, and policymaking.
Conclusion: Modified e-Delphi study established a consensus-driven framework for optimizing online learning in medical education. By refining key components—Digital Capability, Cognitive Enhancement, and Cybergogical Practices—it enhances clarity in e-learning terminology and supports curriculum design, faculty development, and policymaking. With strong expert agreement, it ensures adaptability in hybrid education while paving the way for future research and innovation.
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References
1. Adams A, Liyanagunawardena T, Rassool N, Williams S. Use of open educational resources in higher education. Br J Educ Technol 2013;44(5):E149-50. https://doi.org/10.1111/bjet.12014
2. Isba R, Rousseva C, Woolf K, Byrne-Davis L. Development of a brief learning environment measure for use in healthcare professions education: The Healthcare Education Micro Learning Environment Measure (HEMLEM). BMC Med Educ 2020;20(1):110. https://doi.org/10.1186/s12909-020-01996-8
3. Peng H, Ma S, Spector JM. Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learn Environ 2019;6(1). https://doi.org/10.1186/s40561-019-0089-y
4. Evanick J. Designing Effective Online Learning Environments For Medical And Science Practitioners. In: elearning industry. 2023. Accessed on: January 25, 2024. Available from URL: https://elearningindustry.com/designing-effective-online-learning-environments-for-medical-and-science-practitioners
5. Khodyakov D, Grant S, Denger B, Kinnett K, Martin A, Peay H, et al. Practical Considerations in Using Online Modified-Delphi Approaches to Engage Patients and Other Stakeholders in Clinical Practice Guideline Development. Patient 2020;13(1):11. https://doi.org/10.1007/s40271-019-00389-4
6. Nasa P, Jain R, Juneja D. Delphi methodology in healthcare research: How to decide its appropriateness. World J Methodol 2021;11(4):116. https://doi.org/10.5662/wjm.v11.i4.116
7. Alizadeh S, Maroufi SS, Sohrabi Z, Norouzi A, Dalooei RJ, Ramezani G. Large or Small Panel in the Delphi Study? Application of Bootstrap Technique. J Evol Med Dent Sci 2020;9(15):1267-71 https://doi.org/10.14260/jemds/2020/275
8. Naeem N i K, Yusoff MSB, Hadie SNH, Ismail IM, Iqbal H. Understanding the Functional Components of Technology-Enhanced Learning Environment in Medical Education: A Scoping Review. Med Sci Educ 2023;33(1):1-15. https://doi.org/10.1007/s40670-023-01747-6
9. Scott TE, Costich M, Fiorino EK, Paradise Black N. Using a Modified Delphi Methodology to Identify Essential Telemedicine Skills for Pediatric Residents. Acad Pediatr 2023;23(3):511-7. https://doi.org/10.1016/j.acap.2022.08.014
10. Mircioiu C, Atkinson J. A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale. Pharmacy 2017;5(2):26. https://doi.org/10.3390/pharmacy5020026
11. Gargon E, Crew R, Burnside G, Williamson PR. Higher number of items associated with significantly lower response rates in COS Delphi surveys. J Clin Epidemiol 2019;108:110-20. https://doi.org/10.1016/j.jclinepi.2018.12.010
12. Hunter KE, Webster AC, Clarke M, Page MJ, Libesman S, Godolphin PJ, et al. Development of a checklist of standard items for processing individual participant data from randomised trials for meta-analyses: Protocol for a modified e-Delphi study. PLoS One 2022;17(10). https://doi.org/10.1371/journal.pone.0275893
14. Poon J. Blended Learning: An Institutional Approach for Enhancing Students’ Learning Experiences. J Online Learn Teach 2013;9(2):271-89. https://jolt.merlot.org/vol9no2/poon_0613.pdf
15. Clayton JF. Development and Validation of an Instrument for Assessing Online Learning Environments in Tertiary Education: The Online Learning Environment Survey (OLLES) 2007. http://hdl.handle.net/20.500.11937/550
16. Walker SL, Fraser BJ. Development and Validation of an Instrument for Assessing Distance Education Learning Environments in Higher Education: The Distance Education Learning Environments Survey (DELES). Learn Environ Res 2005 ;8(3):289-308. Available from: https://link.springer.com/10.1007/s10984-005-1568-3
17. Guraya SS, Guraya SY, Yusoff MSB. Preserving professional identities, behaviors, and values in digital professionalism using social networking sites; a systematic review. BMC Med Educ 2021;21(1) https://doi.org/10.1186/s12909-021-02802-9
18. Forbes D. Professional Online Presence and Learning Networks: Educating for Ethical Use of Social Media. International Review of Research in Open and Distributed Learning 2017;18(7). Accessed on: January 25, 2024. Available from URL: https://files.eric.ed.gov/fulltext/EJ1163210.pdf
19. Gillett-Swan J. The Challenges of Online Learning Supporting and Engaging the Isolated Learner. J Learn Design 2017;10(1)20-30. https://doi.org/10.5204/jld.v9i3.293
20. van der Meer GH, Milota M, de Jonge RR, Jansen RS. Practical guidelines to build Sense of Community in online medical education. Med Educ 2021;55(8):925. https://doi.org/10.1111/medu.14477
21. Geng S, Law KMY, Niu B. Investigating self-directed learning and technology readiness in blending learning environment. Int J Educ Technol High Educ 2019;16(1):1-22. https://doi.org/10.1186/s41239-019-0147-0
22. Turan Z, Kucuk S, Cilligol Karabey S. The university students’ self-regulated effort, flexibility and satisfaction in distance education. Int J Educ Technol High Educ 2022;19(1):1-19. https://doi.org/10.1186/s41239-022-00342-w
23. Limniou M, Varga-Atkins T, Hands C, Elshamaa M. Learning, Student Digital Capabilities and Academic Performance over the COVID-19 Pandemic. Educ Sci 2021;11(7):361. https://doi.org/10.3390/educsci11070361
24. Bashir A, Bashir S, Rana K, Lambert P, Vernallis A. Post-COVID-19 Adaptations; the Shifts Towards Online Learning, Hybrid Course Delivery and the Implications for Biosciences Courses in the Higher Education Setting. Front Educ 2021;6:711619. https://doi.org/10.3389/feduc.2021.711619
25. Wang T, Lund BD, Marengo A, Pagano A, Mannuru NR, Teel ZA, et al. Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success. Appl Sci 2023;13(11):6716. https://www.mdpi.com/2076-3417/13/11/6716/htm
26. Oh CS, Bailenson JN, Welch GF. A systematic review of social presence: Definition, antecedents, and implications. Front Robot AI 2018;5(OCT):114. https://doi.org/10.3389/frobt.2018.00114
27. Kreijns K, Van Acker F, Vermeulen M, Van Buuren H. Community of Inquiry: Social Presence Revisited. E-Learning and Digital Media. 2014;11(1):5-18. https://doi.org/10.2304/elea.2014.11.1.5