Document Type : Original Article
Authors
1 Department of Community Medicine, Vir Chandra Singh Garhwali Government Institute of Medical Science and Research, Srinagar, Uttarakhand
2 Department of Physiology, AIIMS, Patna, Bihar, India
Abstract
INTRODUCTION: Different types of learners based on sensory modalities are observed. Cognition or
physiological alterations in the sensory pathways might play its role in different modalities of visual,
auditory, read and write, and kinesthetic learners which are based on different sensory modalities
of perception
OBJECTIVE: The objective of this study is to ascertain an objective parameter (neurophysiological
parameters) for the classification of learners based on their preferred sensory modality
MATERIALS AND METHODS: An experimental cross‑sectional study was conducted among
100 medical students. Learners were classified into visual, auditory, read‑write, and kinesthetic
learners based on the interpretation drawn on the basis of the VARK questionnaire. Sensory‑evoked
potentials (SEPs), including pattern shift visual (PSVEPs), brain stem auditory (BAEPs), short‑latency
somatosensory (SSEP), and event‑related potentials (P300) were measured. SEPs measured in
microvolts were recorded from the scalp with the help of active and reference electrodes. Multiple
responses to sensory stimuli (using NIHON KOHDEN Corporation Neuropack X1, Tokyo, Japan)
were recored and averaged using the computerized signal averging technique.
RESULTS: No statistically significant difference was observed in conduction velocities (in terms of
latency and amplitude) of SEP among different type of learners, except latency N145 wave form in
VEP (P < 0.05). A characteristic pattern of minimal comparative latency was observed among the
majority of visual learners. Similary, P300 has shown a characteristic pattern of decreased comparative
latency among majority of read and write learners.
CONCLUSION: Study findings suggested that among existing teaching and learning modalities,
visual modalities were observed faster but to retain it in memory and for abstract thinking, students
should utilize read and writing skills which are lacking in the era of digitalization and overuse of
electronic devices.
Keywords
5th ed. Belmont, CA, NY: Thompson Learning, Springer; 2005.
2. Di Paola V, Marijuán PC, Lahoz‑Beltra R. Learning and evolution
in bacterial taxis: An operational amplifier circuit modeling the
computational dynamics of the prokaryotic ‘two component
system’ protein network. Biosystems 2004;74:29‑49.
3. Hobson JA, Pace‑Schott EF, Stickgold R. Dreaming and the brain:
Toward a cognitive neuroscience of conscious states. Behav Brain
Sci 2000;23:793‑842.
4. Pennartz CM. Identification and integration of sensory modalities:
Neural basis and relation to consciousness. Conscious Cogn
2009;18:718‑39.
5. Demiralp T, Ademoglu A, Schürmann M, Başar‑Eroglu C,
Başar E. Detection of P300 waves in single trials by the wavelet
transform (WT). Brain Lang 1999;66:108‑28.
6. Linden DE, Prvulovic D, Formisano E, Vollinger M, Zanella FE.
Rainer Goebel and Thomas Dierks. The functional neuroanatomy
of target detection: An fMRI study of visual and auditory oddball
tasks. Cereb Cortex 1999;9:815‑23.
7. Aminoff MJ. Electrodiagnosis in Clinical Neurology. 6th ed.
New York: Churchill Livingstone; 1997. p. 1‑630.
8. Fleming N. VARK: A Guide to Learning Styles; 2007. Available
from: HYPERLINK “http://www.vark” http://www.varklearn.
com/english/index.asp. [Last accessed o n 2011 Jul 24].
9. Teaching FN, Styles L. VARK Strategies. Christ Church,
New Zealand: Microfilm Digital Print and Copy Center; 2001.
10. Zoghi M, Brown T, Williams B, Roller L, Jaberzadeh S, Palermo C,
et al. Learning style preferences of Australian health science
students. J Allied Health 2010;39:95‑103.
11. Picton TW. The P300 wave of the human event‑related potential.
J Clin Neurophysiol 1992;9:456‑79.
12. Hall JW 3ed. Handbook of Auditory Evoked Responses. Needham
Heights, MA: Allyn and Bacon; 1992.
13. American Clinical Neurophysiology Society. Guideline
9B: Guidelines on visual evoked potentials. Am J
Electroneurodiagnostic Technol 2006;46:254‑74.
14. Jones DC, Blume WT. Aberrant wave forms to pattern reversal
stimulation: Clinical significance and electrographic ‘solutions’.
Electroencephalogr Clin Neurophysiol 1985;61:472‑81.
15. Aminoff MJ. Event Related Potentials.Electro diagnosis in
Clinical Neurology. 4th ed. San Francisco: Churchill Livingstone
Publishers; 1988. p. 573‑5.
16. Niedermeyer E, da Silva FL. Electroencephalography: Basic
Principles, Clinical Applications, and Related Fields. 7th ed.
[Netherland] Published online oxford university press: Lippincott
Williams and Wilkins; 2017. p. 140.
17. Duncan CC, Barry RJ, Connolly JF, Fischer C, Michie PT,
Näätänen R, et al. Event‑related potentials in clinical research:
Guidelines for eliciting, recording, and quantifying mismatch
negativity, P300, and N400. Clin Neurophysiol 2009;120:1883‑908.
18. Panambur S, Nambiar V, Heming T. Learning style preferences of
preclinical medical students in oman. Oman Med J 2014;29:461‑3.
19. Lujan HL, DiCarlo SE. First‑year medical students prefer multiple
learning styles. Adv Physiol Educ 2006;30:13‑6.
20. Banoub M, TetzlaffJE, Schubert A. Pharmacologic and physiologic
influences affecting sensory evoked potentials: Implications for
perioperative monitoring. Anesthesiology 2003;99:716‑37.
21. Sharma R, Joshi S, Singh KD, Kumar A. Visual Evoked Potentials:
Normative Values and Gender Differences. J Clin Diagn Res
2015;9:CC12‑5.
22. Picton TW, Hillyard SA, Krausz HI, Galambos R. Human
auditory evoked potentials. I. Evaluation of components.
Electroencephalogr Clin Neurophysiol 1974;36:179‑90.
23. Sur S, Sinha VK. Event‑related potential: An overview. Ind
Psychiatry J 2009;18:70‑3.
24. Patrick CJ, Bernat EM, Malone SM, Iacono WG, Krueger RF,
McGue M. P300 amplitude as an indicator of externalizing in
adolescent males. Psychophysiology 2006;43:84‑92.
25. Chuard PJ, Vrtílek M, Head ML, Jennions MD. Evidence that
nonsignificant results are sometimes preferred: Reverse P‑hacking
or selective reporting? PLoS Biol 2019;17:e3000127.
26. Barshay J. Lower Test Scores for Students who use Computers
Often in School, 31‑Country Study Finds. The Hechinger Report;
21 September, 2015.