Educational paradigm shift: Long-term ramifications of COVID-19 in the Indian context

Uzma Khan, Sana Naseem, Aarif Mohammad Khan, Shaha Faisal

Article ID: 4629
Vol 8, Issue 8, 2024

VIEWS - 164 (Abstract) 160 (PDF)

Abstract


The COVID-19 pandemic has brought about a significant transformation in the global educational landscape, which has far-reaching implications for learning systems. This study focuses on the impact of the pandemic on India’s education sector, exploring its long-term consequences. The research delves into the enduring effects of COVID-19 and how it has reshaped traditional educational methods. The Indian education system has undergone a dynamic evolution, from the rapid integration of digital technologies to the redefinition of pedagogical approaches. The study employs surveys to comprehensively assess the multifaceted dimensions of adaptation. By synthesizing qualitative insights from 386 respondents, the research aims to contribute to a better understanding of the challenges, opportunities, and long-term implications of COVID-19. The hypothesis results show a significant difference among genders for factors 2 (consequences of COVID-19 on teachers, students, and teaching), 3 (impact of COVID-19 on education), and 4 (education system responses to COVID-19). In contrast, factor 3 is significant for education. Likewise, awareness factors 1 and 3 show that factors 1 (policies and strategies on education during COVID-19) and 5 (possible solutions and implementation) are significant for an application. Finally, binary logistic regression demonstrates that for gender factors three, education factors four, and five, online participation is significant for awareness factors one, three, and four. However, all of the factors are insignificant for an application. These findings can guide policy decisions, inform educational practices, and lay a valuable foundation for future research endeavours to shape a resilient, equitable, and innovative educational future.

Keywords


pandemic; COVID-19; education; Binary Logistic Regression; factor analysis

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References


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DOI: https://doi.org/10.24294/jipd.v8i8.4629

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