Developing a Conceptual Model of Self-Directed Learning in Virtual Environments for Medical Sciences Students
Keywords:self-directed learning, virtual learning environment, medical student, student
Identification of key factors affecting the self-directed learning process in the virtual environment of medical education is vital. In this article, we designed a model that describes the self-directed learning process in the virtual learning environment for post graduate students of medical sciences in Iran. This study was carried out in two steps: first, using a qualitative study, we explored the formation of a self-directed learning process in the virtual environment. Second, a review of the literature was conducted to identify the conceptual models. Finally, based on the results, a self-directed learning model for virtual learning was developed. A total of 25 people were research participants in the qualitative part, and individual interviews were conducted with both faculty members and students. There were 1,049 codes, 80 subcategories, 15 categories, and 5 themes extracted from the interviews and through analysis. The themes included (a) backgrounds and requirements, (b) support, discipline, and coordination of the educational system, (c) students’ effort to manage to learn, (d) efficiency, attractiveness, and organization of educational environments and context, and (e) personal excellence, growth, and development. The self-directed learning process in virtual environments consists of some elements and structures, and a description of the relationship between these elements can be the basis of educational planning to develop and compile an effective evaluation of this skill.
Allen, I. E. & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. ERIC. http://www.onlinelearningsurvey.com/reports/changingcourse.pdf
Antonietti, C., Schmitz, M.-L., Consoli, T., Cattaneo, A., Gonon, P., & Petko, D. (2023). Development and validation of the ICAP Technology Scale to measure how teachers integrate technology into learning activities. Computers & Education, 192, 1-14. https://doi.org/10.1016/j.compedu.2022.104648
Bagheri-Nesami, M., Ahmady, S., & Kohan, N. (2021). Relationship between information and communications technology engagement with online self-regulated learning in nursing students of Mazandaran University of medical sciences. Journal of Nursing and Midwifery Sciences, 8, 253-259. DOI: 10.4103/jnms.jnms_27_21
Brathwaite, A. C. (2003). Selection of a conceptual model/framework for guiding research interventions. Internet Journal of Advanced Nursing Practice, 6, 1-10. https://ispub.com/IJANP/6/1/8576
Brockett, R.G., & Hiemstra, R. (1991). Self-direction in adult learning: Perspectives on theory, research and practice (1st ed.). Routledge. https://doi.org/10.4324/9780429457319
Candy, P. C. (1991). Self-direction for lifelong learning: A comprehensive guide to theory and practice. Jossey-Bass. https://doi.org/10.1177/074171369204200307
Connolly, S. & Wicks, K. (2023). Part-time higher education students’ interactions with a virtual learning environment as an exploration of theories of connectivism. Compass: Journal of Learning and Teaching, 16, 71-92. https://pubmed.ncbi.nlm.nih.gov/30304614/
Cook, D. A., Hatala, R., Brydges, R., Zendejas, B., Szostek, J. H., Wang, A. T., Erwin, P. J., & Hamstra, S. J. (2011). Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. Jama, 306, 978-988. https://doi.org/10.1001/jama.2011.1234
Cummings, C., Mason, D., Shelton, K., & Baur, K. (2017). Active learning strategies for online and blended learning environments. Flipped Instruction: Breakthroughs in Research and Practice. IGI Global. DOI: 10.4018/978-1-5225-1803-7.ch006
Education, A. C. F. G. M. (2013). ACGME common program requirements. Accreditation Council for Graduate Medical Education, 10-22. https://medicine.umich.edu/sites/default/files/content/downloads/CPRs2013.pd f
Ellaway, R. & Masters, K. (2008). AMEE Guide 32: e-Learning in medical education Part 1: Learning, teaching and assessment. Medical Teacher, 30, 455-473. https://doi.org/10.1080/01421590802108331
Elshami, W., Taha, M. H., Abdalla, M. E., Abuzaid, M., Saravanan, C., & Al kawas, S. (2022). Factors that affect student engagement in online learning in health professions education. Nurse Education Today, 110, 105261. https://doi.org/10.1016/j.nedt.2021.105261
Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48, 18-33. https://doi.org/10.1177/074171369704800103
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice. Routledge. https://doi.org/10.4324/9780203838761
Goh, P.-S. & Sandars, J. (2020). A vision of the use of technology in medical education after the COVID-19 pandemic. MedEdPublish, 9, 49. https://doi.org/10.15694/mep.2020.000049.1
Goldberg, E. R. & Lannoye-Hall, C. (2023). Adapting learning materials for the digital age: Teacher education during emergency remote learning. Handbook of Research on Advancing Teaching and Teacher Education in the Context of a Virtual Age (pp. 176–202). IGI Global. DOI: 10.4018/978-1-6684-8407-4.ch009
Goldie, J. G. S. (2016). Connectivism: A knowledge learning theory for the digital age? Medical Teacher, 38, 1064-1069. DOI: 10.3109/0142159X.2016.1173661
Grow, G. O. (1991). Teaching learners to be self-directed. Adult Education Quarterly, 41, 125-149. https://doi.org/10.1177/0001848191041003001
Kara, M. (2022). Revisiting online learner engagement: Exploring the role of learner characteristics in an emergency period. Journal of Research on Technology in Education, 54, S236-S252. doi: 10.1080/15391523.2021.1891997
Keshavarz, M., Mirmoghtadaie, Z., and Nayyeri,S. (2022). Design and validation of the virtual classroom management questionnaire.The International Review of Research in Open and Distributed Learning, 23(2), 121–135. https://doi.org/10.19173/irrodl.v23i2.5774
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Follett Publishing Company.
Knowles, M. S. (1980). The modern practice of adult education: From pedagogy to andragogy. Prentice Hall/Cambridge.
Kohan, N., Janatolmakan, M., Rezaei, M., & Khatony, A. (2021). Relationship between learning styles and academic performance among virtual nursing students: A cross-sectional study. Education Research International, 2021, 1-6. doi: 10.1155/2021/8543052
Liyanagunawardena, T. R. & Williams, S. A. (2014). Massive open online courses on health and medicine. Journal of Medical Internet Research,16(8), e197. http://doi:10.2196/jmir.3439
Mirmoghtadaie, Z., Keshavarz, M., Mohammadimehr , M., & Rasouli, D. (2023). The design and psychometric properties of a peer observation tool for use in LMS-Based classrooms in medical sciences. The International Review of Research in Open and Distributed Learning, 24(1), 66-84. https://doi.org/10.19173/irrodl.v24i1.6689
Pilling-Cormick, J. (2002). Transformative and self‐directed learning in practice. New Directions for Adult and Continuing Education, 1997(74), 69-77. https://doi.org/10.1002/ace.7408
Rashid, T. & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604-612. https://doi.org/10.1016/j.chb.2016.05.084
Shokar, G. S., Shokar, N. K., Romero, C. M., & Bulik, R. J. (2002). Self-directed learning: looking at outcomes with medical students. Family Medicine-Kansas City, 34, 197-200. https://www.stfm.org/familymedicine/vol34issue3/Shokar197
Song, D. & Bonk, C. J. (2016). Motivational factors in self-directed informal learning from online learning resources. Cogent Education, 3(1), 1-11. https://doi.org/10.1080/2331186X.2016.1205838
Song, L. & Hill, J. R. (2007). A conceptual model for understanding self-directed learning in online environments. Journal of Interactive Online Learning, 6, 27-42. https://www.researchgate.net/publication/250699716
Uys, W. (2021). An integrative review of fourteen self-directed learning models. Journal for New Generation Sciences, 19(2), 48-66. doi: https://journals.co.za/doi/pdf/10.10520/ejc-newgen-v19-n2-a4
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