Document Type : Original Article


1 Department of Palliative Care, Nursing and Midwifery Care Research Centre, Faculty of Nursing and Midwifery, Isfahan University of Medical Sciences, Isfahan, Iran

2 Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

3 Department of Adult Health Nursing, Nursing and Midwifery Care Research Centre, Faculty of Nursing and Midwifery, Isfahan University of Medical Sciences, Isfahan, Iran


Pandemic 2019 is observed in all sectors of the world which had caused a huge disruption in the
education system in India as well as worldwide adding challenges to student’s life. We aimed to
provide an outline on E‑Learning and the difficulties experienced by students of various colleges in
the southern parts of India and to conduct knowledge, attitude and practices (KAP) analysis based
on student’s perception regarding E‑learning by collecting an online survey, 346 valid questionnaires
were retrieved. In order to evaluate the association between the variables of KAP, structural equation
modeling was used for data analysis. The influencing factors of KAP were observed to know the
effect of the pandemic on E‑learning from the model. The result finding moderately fit the collected
data and reveals a good fit of the model in the means of satisfying the threshold values.


1. WHO Director General’s Opening Remarks at the Media
Briefing on COVID 19; April 03, 2020. URL: https://www.who.
2. Dong E, Du H, Gardner L. An interactive web‑based dashboard
to track COVID‑19 in real time. Lancet Infect Dis 2020;20:533‑4.
3. Soni VD. Global Impact of E learning during COVID 19. SSRN
3630073 Electron J 2020; pg. no-1.
4. Sahu P. Closure of universities due to coronavirus disease
2019 (COVID‑19): Impact on education and mental health of
students and academic staff. Cureus 2020;12:e7541.
5. Mahdy MA. The impact of COVID‑19 pandemic on the academic
performance of veterinary medical students. Front Vet Sci
6. Alsoufi A, Alsuyihili A, Msherghi A, Elhadi A, Atiyah H,
Ashini A, et al. Impact of the COVID‑19 pandemic on medical
education: Medical students’ knowledge, attitudes, and practices
regarding electronic learning. PLoS One 2020;15:e0242905.
7. Renganathan R, Balach S, Govindarajan K. Customer perception
towards banking sector: Structural equation modeling approach.
African Journal of Business Management. 2012 Nov 30;6(46):1142636.
8. Hu LT, Bentler PM. Cut-off criteria for fit indexes in covariance
structure analysis: Conventional criteria versus new alternatives.
Structural equation modeling: a multidisciplinary journal. 1999
Jan 1;6(1):1-55.
9. Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data
analysis. Englewood Cliff. New jersey, USA. 1998;5(3):207-19.
10.  Mulaik SA, James LR, Van Alstine J, Bennett N, Lind S, Stilwell
CD. Evaluation of goodness-of-fit indices for structural equation
models. Psychological bulletin. 1989 May;105(3):430.
11. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham,
R.L. (2006) Multivariate Data Analysis. Vol. 6, Pearson Prentice
Hall, Upper Saddle River.
12. Schumacker RE, Lomax RG. A beginner's guide to structural
equation modeling. psychology press; 2004 Jun 24.
13. Byrne BM. Structural equation modeling with Mplus: Basic
concepts, applications, and programming. routledge; 2013 Jun 17.
14. Barrot JS, Llenares II, Del Rosario LS. Students’ online learning
challenges during the pandemic and how they cope with
them: The case of the Philippines. Education and Information
Technologies. 2021 Nov;26(6):7321-38.
15. Ghadrdoost B, Sadeghipour P, Amin A, Bakhshandeh H, Noohi F,
Maleki M, et al. Validity and reliability of a virtual education
satisfaction questionnaire from the perspective of cardiology
residents during the COVID‑19 pandemic. Educ Health Promot