The Challenges Faced in Technology-Driven Classes During COVID-19

Abstract

In the wake of coronavirus pandemic, social distancing became a mandate that led to the transition from traditional classroom-based lectures to computer-based learning. This paper extensively deals with the ranking of the challenges faced by instructors and students. Primary data from 624 participants (399 students and 225 instructors) is collected through a questionnaire. To assign the ranking to the challenges, Technique of Order Preference Similarity to Ideal Solution (TOPSIS) is deployed. A contextual model is developed by using Interpretive Structural Model (ISM) technique that further provides recommendations for prioritizing the challenges that need to be addressed to mitigate the problems faced in online lectures in coronavirus situation. The number of variables is reduced to simplify the interpretation by exploratory factor analysis. The study also provides the basis to formulate the strategies for policymakers and administration after identifying which challenges need to be addressed first for mitigating all the other challenges.