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Artificial Intelligence Review

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03-06-2021

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

Authors: Mahyar Kamali Saraji, Abbas Mardani, Mario Köppen, Arunodaya Raj Mishra, Pratibha Rani

Published in: Artificial Intelligence Review | Issue 1/2022

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Abstract

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

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Title: An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions
Authors: Mahyar Kamali Saraji
Abbas Mardani
Mario Köppen
Arunodaya Raj Mishra
Pratibha Rani
Publication date: 03-06-2021
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 1/2022
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-10029-9

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