New Media, Technology and Neurology Education

Abstract

The use of technology in neurology education has revolutionized many aspects of medical teaching, addressing some important challenges of modern education such as information overload and the unique needs of millennial learners. However, it also has inherent problems, such as depersonalization and high development costs. Due to the heterogeneity of different applications, it is difficult to establish general principles to guide front line educators, but it may be possible to describe “minimum” best practice elements. In this article, we examine commonalities of some of the most successful uses of technology in neurology education. We suggest the following for effective application of technology: (1) match technology to predetermined educational objectives, (2) characterize learners in relationship to technology, (3) optimize how technological components fit into the learning environment, (4) monitor and manage learner engagement with technology, (5) perform cost analyses, and (6) explore opportunities for educational scholarship and research.

Keywords

technology - neurological education - curriculum

• References

• 1 Asch DA, Bilimoria KY, Desai SV. Resident duty hours and medical education policy - raising the evidence bar. N Engl J Med 2017; 376 (18) 1704-1706
• 2 Levin KH, Shanafelt TD, Keran CM. , et al. Burnout, career satisfaction, and well-being among US neurology residents and fellows in 2016. Neurology 2017; 89 (05) 492-501
• 3 Frank A, Gifford K. Electronic portfolio use in pediatric residency and perceived efficacy as a tool for teaching lifelong learning. BMC Med Educ 2017; 17 (01) 202
• 4 Serrat MA, Dom AM, Buchanan Jr JT, Williams AR, Efaw ML, Richardson LL. Independent learning modules enhance student performance and understanding of anatomy. Anat Sci Educ 2014; 7 (05) 406-416
• 5 Williams VN, Medina J, Medina A, Clifton S. Bridging the Millennial Generation expectation gap: perspectives and strategies for physician and interprofessional faculty. Am J Med Sci 2017; 353 (02) 109-115
• 6 Micieli G, Cavallini A, Santalucia P, Gensini G. Simulation in neurology. Neurol Sci 2015; 36 (10) 1967-1971
• 7 De Leeuw RA, Westerman M, Nelson E, Ket JC, Scheele F. Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model. BMC Med Educ 2016; 16: 168
• 8 Wittich CM, Agrawal A, Cook DA. , et al. E-learning in graduate medical education: survey of residency program directors. BMC Med Educ 2017; 17 (01) 114
• 9 Muttappallymyalil J, Mendis S, John LJ, Shanthakumari N, Sreedharan J, Shaikh RB. Evolution of technology in teaching: blackboard and beyond in medical education. Nepal J Epidemiol 2016; 6 (03) 588-592
• 10 Parent F, Coppieters Y, Parent M. Information technologies, health, and “globalization”: anyone excluded?. J Med Internet Res 2001; 3 (01) E11
• 11 Medical Education Partnership Initiative. Year 5 report. Available at: http://mepinetwork.org/images/stories/MEPI Year 5 Report_No Appendixes.pdf . Accessed December 29, 2017
• 12 Lakhan SE, Tepper D, Snarski RD, Mays M. Opinion and special articles: electronic media in neurology education: progress, promise, and pitfalls. Neurology 2013; 81 (08) e47-e50
• 13 Annear MJ, Eccleston CE, McInerney FJ. , et al. A new standard in dementia knowledge measurement: comparative validation of the dementia knowledge assessment scale and the Alzheimer's disease knowledge scale. J Am Geriatr Soc 2016; 64 (06) 1329-1334
• 14 Milligan C, Littlejohn A. How health professionals regulate their learning in massive open online courses. Internet High Educ 2016; 31: 113-121
• 15 American Academy of Neurology. Neurolearn Online Learning Program. Available at: https://tools.aan.com/education/webcme/index.cfm?event=neurolearn:home . Accessed November 26, 2017
• 16 Johnson TR, Lyons R, Chuah JH, Kopper R, Lok BC, Cendan JC. Optimal learning in a virtual patient simulation of cranial nerve palsies: the interaction between social learning context and student aptitude. Med Teach 2013; 35 (01) e876-e884
• 17 Lau KH. Computer-based teaching module design: principles derived from learning theories. Med Educ 2014; 48 (03) 247-254
• 18 London ZN, Gallagher GW, Ebright MJ. A self-study curriculum in electromyography and nerve conduction studies for residents and fellows. MedEdPORTAL 2017; 13: 10581
• 19 Prober CG, Khan S. Medical education reimagined: a call to action. Acad Med 2013; 88 (10) 1407-1410
• 20 Moeller J, Farooque P, Leydon G, Dominguez M, Schwartz M, Sadler R. A video-based introductory EEG curriculum for neurology residents and other EEG learners. MedEdPORTAL 2017; 13: 10570
• 21 Bensalem-Owen M, Chau DF, Sardam SC, Fahy BG. Education research: evaluating the use of podcasting for residents during EEG instruction: a pilot study. Neurology 2011; 77 (08) e42-e44
• 22 Vasilopoulos T, Chau DF, Bensalem-Owen M, Cibula JE, Fahy BG. Prior podcast experience moderates improvement in electroencephalography evaluation after educational podcast module. Anesth Analg 2015; 121 (03) 791-797
• 23 Ermak DM, Bower DW, Wood J, Sinz EH, Kothari MJ. Incorporating simulation technology into a neurology clerkship. J Am Osteopath Assoc 2013; 113 (08) 628-635
• 24 Spiotta AM, Turner RD, Turk AS, Chaudry MI. The case for a milestone-based simulation curriculum in modern neuroendovascular training. J Neurointerv Surg 2016; 8 (04) 429-433
• 25 Braksick SA, Kashani K, Hocker S. Neurology education for critical care fellows using high-fidelity simulation. Neurocrit Care 2017; 26 (01) 96-102
• 26 MacDougall BJ, Robinson JD, Kappus L, Sudikoff SN, Greer DM. Simulation-based training in brain death determination. Neurocrit Care 2014; 21 (03) 383-391
• 27 Rigby H, Schofield S, Mann K, Benstead T. Education research: an exploration of case-based learning in neuroscience grand rounds using the Delphi technique. Neurology 2012; 79 (03) e19-e26
• 28 Ursino M, Giannessi M. A model of cerebrovascular reactivity including the circle of Willis and cortical anastomoses. Ann Biomed Eng 2010; 38 (03) 955-974
• 29 George P, Newey CR, Bhimraj A. The tablet device in hospital neurology and in neurology graduate medical education: a preliminary study. Neurohospitalist 2015; 5 (01) 15-21
• 30 Wittich CM, Wang AT, Fiala JA. , et al. Measuring participants' attitudes toward mobile device conference applications in continuing medical education: validation of an instrument. J Contin Educ Health Prof 2016; 36 (01) 69-73
• 31 Issa N, Schuller M, Santacaterina S. , et al. Applying multimedia design principles enhances learning in medical education. Med Educ 2011; 45 (08) 818-826
• 32 Issenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach 2005; 27 (01) 10-28
• 33 Strowd RE, Gamaldo CE, Kwan AB, Cruz TE, Salas RME. Flipping the switch: The feasibility of a “think aloud” flipped-classroom approach to clinical reasoning instruction. J Contemp Med Educ 2016; 4: 40-46
• 34 Carrick FR, Abdulrahman M, Hankir A. , et al. Randomized controlled study of a remote flipped classroom neuro-otology curriculum. Front Neurol 2017; 8: 349
• 35 Greer DM, Varelas PN, Haque S, Wijdicks EF. Variability of brain death determination guidelines in leading US neurologic institutions. Neurology 2008; 70 (04) 284-289
• 36 White C, Bradley E, Martindale J. , et al. Why are medical students ‘checking out’ of active learning in a new curriculum?. Med Educ 2014; 48 (03) 315-324
• 37 Berkhout JJ, Helmich E, Teunissen PW, van der Vleuten CPM, Jaarsma ADC. Context matters when striving to promote active and lifelong learning in medical education. Med Educ 2018; 52 (01) 34-44
• 38 Cook DA. Learning and cognitive styles in web-based learning: theory, evidence, and application. Acad Med 2005; 80 (03) 266-278
• 39 Cook DA, Gelula MH, Dupras DM, Schwartz A. Instructional methods and cognitive and learning styles in web-based learning: report of two randomised trials. Med Educ 2007; 41 (09) 897-905
• 40 Cook DA. Revisiting cognitive and learning styles in computer-assisted instruction: not so useful after all. Acad Med 2012; 87 (06) 778-784
• 41 Banning M. The think aloud approach as an educational tool to develop and assess clinical reasoning in undergraduate students. Nurse Educ Today 2008; 28 (01) 8-14
• 42 edX. Quality education for everyone, everywhere. Available at: https://www.edx.org/about-us . Accessed November 24, 2017
• 43 Razik R, Mammo Z, Gill HS, Lam WC. Academic screen casting: internet-based dissemination of ophthalmology grand rounds. Can J Ophthalmol 2011; 46 (01) 72-76
• 44 Achike F. The challenges of integration in an innovative modern medical curriculum. Med Sci Educ 2016; 26: 153-158
• 45 Weber D, McCarthy D, Pathmanathan J. An effective automated method for teaching EEG interpretation to neurology residents. Seizure 2016; 40: 10-12
• 46 Lau KHV, Farooque P, Leydon G, Schwartz ML, Sadler RM, Moeller JJ. Using learning analytics to evaluate a video-based lecture series. Med Teach 2018; 40 (01) 91-98
• 47 YouTube. YouTube Analytics basics 2017. Available at: https://support.google.com/youtube/answer/1714323?hl=en . Accessed January 13, 2018
• 48 Barak M, Watted A, Haick H. Motivation to learn in massive open online courses: examining aspects of language and social engagement. Comput Educ 2016; 94: 49-60
• 49 Parve S, Ershadi A, Karimov A. , et al. Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences. Afr Health Sci 2016; 16 (03) 860-865