Abstract
Recent efforts to enrich the medical education experience recommended interinstitutional and collaborative efforts. Within this context, the author describes a model for school-specific augmented medical education. The evidence-backed conceptual model is composed of six foundational elements, which include the following: technology-enriched learning environments, analytics to drive instructional interventions, cognitive neuroscience and educational psychology research (the Science of Learning), self-regulated learning strategies, competency-based approaches, and blended learning instructional design. Harnessing the creativity of our leadership, medical educators, and learners is fundamental to improving the learning experience for all. This model could be used to meaningfully guide implementation processes.
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References
Garrison D, Vaughan N. Blended learning in higher education. San Francisco, CA: Jossey-Bass; 2008.
Garrison D, Vaughan N. Institutional change and leadership associated with blended learning innovation: two case studies. Inter High Edu. 2013;18:24–8.
Green DP. A Gap Analysis of Course Directors’ Effective Implementation of Technology-enriched Course Designs: An Innovation Study [dissertation]. Los Angeles, Ca: University of Southern California. 2018. Retrieved from the University of Southern California Digital Library: http://digitallibrary.usc.edu/cdm/ref/collection/p15799coll40/id/462270. Accessed 30 May 2018.
VanDerLinden K. Blended learning as transformational institutional learning. New Dir High Edu. 2014;165:75–85. https://doi.org/10.1002/he.20085.
Le TT, Prober CG. A proposal for a shared medical school curricular ecosystem. Acad Med. 2018;93:1125–8. https://doi.org/10.1097/ACM.0000000000002194.
Song HS, Kalet AL, Plass JL. Assessing medical students’ self-regulation as aptitude in computer-based learning. Adv Health Sci Edu. 2011;16(1):97–107.
Chen HC, van den Broek WS, ten Cate O. The case for use of entrustable professional activities in undergraduate medical education. Acad Med. 2015;90(4):431–6. https://doi.org/10.1097/ACM.0000000000000586.
Mehta NB, Hull AL, Young JB, Stoller JK. Just imagine: new paradigms for medical education. Acad Med. 2013;88(10):1418–23. https://doi.org/10.1097/ACM.0b013e3182a36a07.
Miller BM, Moore DE Jr, Stead WW, Balser JR. Beyond Flexner: a new model for continuous learning in the health professions. Acad Med. 2010;85(2):266–72. https://doi.org/10.1097/ACM.0b013e3181c859fb.
Prober CG, Khan S. Medical education reimagined: a call to action. Acad Med. 2013;88(10):1407–10. https://doi.org/10.1097/ACM.0b013e3182a368bd.
Touchie C, Cate O. The promise, perils, problems and progress of competency-based medical education. Med Edu. 2016;50(1):93–100. https://doi.org/10.1111/medu.12839.
Clark RE, Estes F. Turning research into results: A guide to selecting the right performance solutions. Charlotte, NC: Information Age Publishing. In: Inc; 2008.
Hurtubise L, Hall E, Sheridan L, Han H. The flipped classroom in medical education: engaging students to build competency. J Med Edu Curr Dev. 2015;2:35–43. https://doi.org/10.4137/JMECD.S23895.
Prober CG, Heath C. Lecture halls without lectures--a proposal for medical education. NEJM. 2012;366(18):1657–9. https://doi.org/10.1056/NEJMp1202451.
Aronson ID, Plass JL, Bania T. Optimizing educational video through comparative trials in clinical environments. Edu Tech Res Dev. 2012;60(3):469–82. https://doi.org/10.1007/s11423-011-9231-4.
Cook DA, Levinson AJ, Garside S, Dupras DM, Erwin PJ, Montori VM. Instructional design variations in internet-based learning for health professions education: a systematic review and meta-analysis. Acad Med. 2010;85(5):909–22. https://doi.org/10.1097/ACM.0b013e3181d6c319.
Grunwald T, Corsbie-Massay C. Guidelines for cognitively efficient multimedia learning tools: educational strategies, cognitive load, and interface design. Acad Med. 2006;81(3):213–23. https://doi.org/10.1097/00001888-200603000-00003.
Lee JE, Recker M. What do studies of learning analytics reveal about learning and instruction? A systematic literature review. Learn, des, and tech: intl comp theory, res, prac, and pol. 2018;2018:1–37. https://doi.org/10.1007/978-3-319-17727-4_116-1.
Chan T, Sebok-Syer S, Thoma B, Wise A, Sherbino J, Pusic M. Learning analytics in medical education assessment: the past, the present, and the future. AEM Edu and Train. 2018;2(2):178–87. https://doi.org/10.1002/aet2.10087.
Cirigliano MM, Guthrie C, Pusic MV, Cianciolo AT, Lim-Dunham JE, Spickard A III, et al. Yes, and … exploring the future of learning analytics in medical education. Teach and Learn in Med. 2017;29(4):368–72. https://doi.org/10.1080/10401334.2017.1384731.
Kredell M. “CHARIOT begins testing wearable technology”. 2018. Summer. Retrieved October 7, 2018, from: https://www.rossier.usc.edu/magazine/ss2018/chariot-begins-testing-wearable-technology/. Accessed 30 May 2018.
Immordino-Yang MH, Christodoulou JA, Singh V. Rest is not idleness: Implications of the brain's default mode for human development and education Perspec on. Psy Sci: Assoc for Psy Sci. 2012;7(4):352–64. https://doi.org/10.1177/1745691612447308.
Taub M, Azevedo R, Bouchet F, Khosravifar B. Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments. Comp Edu. 2014;39(October):356–67. https://doi.org/10.1016/j.chb.2014.07.018.
Anguera JA, Boccanfuso J, Rintoul JL, Al-Hashimi O, Faraji F, Janowich J, et al. Video game multitasking training enhances cognitive control in older adults. Nature. 2013;501(September):97–101. https://doi.org/10.1038/nature12486.
Berman NB, Fall LH, Maloney CG, Levine DA. Computer-assisted instruction in clinical education: a roadmap to increasing CAI implementation. Adv in Health Sci Edu. 2008;13(3):373–83. https://doi.org/10.1007/s10459-006-9041-3.
Mayer RE. Applying the science of learning to medical education. Med Edu. 2010;44(6):543–9. https://doi.org/10.1111/j.1365-2923.2010.03624.x.
Mayer RE. Applying the science of learning. Boston, MA: Pearson Education; 2011.
Dembo MH, Seli H. Academic self-regulation. In: Dembo MH, Seli H, editors. Motivation and learning strategies for college success: a focus on self-regulated learning. 5th ed. New York, N.Y: Routledge, Taylor and Francis Group, Inc; 2016. p. 3–27.
Zimmerman BJ. Academic studying and the development of personal skill: a self-regulatory perspective. Edu Psy. 1998;33(2):73–86.
Pettepher CC, Lomis KD, Osheroff N. From theory to practice: utilizing competency-based milestones to assess professional growth and development in the foundational science blocks of a pre-clerkship medical school curriculum. Med Sci Edu. 2016;26(3):491–7. https://doi.org/10.1007/s40670-016-0262-7.
Englander R, Cameron T, Ballard AJ, Dodge J, Bull J, Aschenbrener CA. Toward a common taxonomy of competency-based domains for the health professions and competencies for physicians. Acad Med. 2013;88(8):1088–94. https://doi.org/10.1097/ACM.0b013e31829a3b2b.
Hawkins RE, Welcher CM, Holmboe ES, Kirk LM, Norcini JJ, Simons KB, et al. Implementation of competency-based medical education: are we addressing the concerns and challenges? Med Edu. 2015;49(11):1086–102. https://doi.org/10.1111/medu.12831.
Levine MF, Shorten G. Competency-based medical education: its time has arrived. Can J Anes. 2016;63(7):802–6. https://doi.org/10.1007/s12630-016-0638-6.
Green DP. Next generation medical education: facilitating student-centered learning environments. EDUCAUSE Learning Initiative Brief. 2016; 1–6. Retrieved from: https://library.educause.edu/resources/2016/3/next-generation-medical-education. Accessed 30 May 2018.
Morton CE, Saleh SN, Smith SF, Hemani A, Ameen A, Bennie TD, et al. Blended learning: how can we optimise undergraduate student engagement? BMC Med Edu. 2016;16(1):195–202. https://doi.org/10.1186/s12909-016-0716-z.
O’Connor EE, Fried J, McNulty N, Shah P, Hogg JP, Lewis P, et al. Flipping radiology education right side up. Acad Rad. 2016;23(7):810–22. https://doi.org/10.1016/j.acra.2016.02.011.
Wood ML, Forgie SE. A first step to blended delivery: introducing an online component to an infectious diseases course using a photography-based social media platform. Med Sci Edu. 2015;25(2):101–3. https://doi.org/10.1007/s40670-015-0103-0.
Kalet AL, Song HS, Sarpel U, Schwartz R, Brenner J, Ark TK, et al. Just enough, but not too much interactivity leads to better clinical skills performance after a computer assisted learning module. Med Tea. 2012;34(10):833–9. https://doi.org/10.3109/0142159X.2012.706727.
Song SH, Pusic M, Nick MW, Sarpel U, Plass JL, Kalet AL. The cognitive impact of interactive design features for learning complex materials in medical education. Comp Edu. 2014;71(February):198–205. https://doi.org/10.1016/j.compedu.2013.09.017.
Immordino-Yang MH, Singh V. Perspectives from social and affective neuroscience on the design of digital learning technologies. In: Immordino-Yang MH, editor. Emotions, learning, and the brain: Exploring the educational implications of affective neuroscience. New York, N.Y: W. W. Norton & Company, Inc; 2016. p. 181–90.
Eccles J. Expectancy value motivational theory. In: Anderman EM, Anderman LH, editors. Psychology of Classroom Learning, vol. 1. Macmillan Reference USA: Detroit; 2006. p. 390–3. Retrieved from: http://go.galegroup.com.libproxy1.usc.edu/ps/i.do?id=GALE%7CCX3027800112&sid=summon&v=2.1&u=usocal_main&it=r&p=GVRL&sw=w&asid=6ab39823387b6b715067288033f65e7c. Accessed 30 May 2018.
Pintrich PR. A motivational science perspective on the role of student motivation in learning and teaching contexts. J Edu Psy. 2003;95(4):667–86. https://doi.org/10.1037/0022-0663.95.4.667.
Elmore RF. Bridging the gap between standards and achievement. Washington, DC: Albert Shanker Institute. 2002. Retrieved July 12, 2003, from http://www.shankerinstitute.org/resource/bridging-gap-between-standards-and-achievement. Accessed 30 May 2018.
Hentschke GC, Wohlstetter P. Cracking the code of accountability. Univ S Calif Urban Educ. 2004;2004(Spring/Summer):17–9.
Graham CR, Woodfield W, Harrison JB. A framework for institutional adoption and implementation of blended learning in higher education. Inter High Edu. 2013;18:4–14.
Porter WW, Graham CR, Bodily RG, Sandberg DS. A qualitative analysis of institutional drivers and barriers to blended learning adoption in higher education. Inter High Edu. 2016;28:17–27. https://doi.org/10.1016/j.iheduc.2015.08.003.
Acknowledgments
The author wishes to thank Melora Sundt, Kenneth Yates, Monique Datta, and Kathy Hanson. Additionally, conversations with Charles Prober aided with streamlining and improving this manuscript. Importantly, the author wishes to thank the Educational Development Office’s Division of Innovations in Medical Education at the University of Miami Miller School of Medicine for support and assistance.
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Portions of this work originated as part of the author’s dissertation study at the University of Southern California Rossier School of Education.
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Green, D.P. Foundational Elements of School-Specific Augmented Medical Education. Med.Sci.Educ. 29, 561–569 (2019). https://doi.org/10.1007/s40670-019-00702-8
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DOI: https://doi.org/10.1007/s40670-019-00702-8