ABSTRACT
The COVID-19 epidemic has been deemed a pandemic by the World Health Organization. It is triggered due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated and spread from Wuhan, China, in December 2019. At present, the entire world is struggling from this virus due to large confirmed positive and death cases of COVID-19. People of every nation have been isolated, and lockdowns are instituted. Despite the introduction of several precautionary measures, the spread of the virus is still increasing at an alarming pace. Although promising development has been made for the development of vaccines for SARS-CoV-2, no vaccines have been reported to cure the infection. Different antiviral therapies have also been attempted but do not seem to be successful for every patient. To deter the dissemination and control the spread of virus, the frontline healthcare staff and police officers deployed numerous autonomous systems for an increased line of protection. Robots are deployed to conduct different operations including decontamination, package delivery, etc. It also acts as a mediator for two-way communication between the doctors and patients. Recent advancement in robotics for its application in healthcare facilities has been found very effective for the healthcare officials to communicate with the virus affected patients, and this literature has addressed it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The COVID-19 pandemic resulted in the hurried adoption of e-learning with no proper need analysis to inform the design and subsequent evaluation of students' performance in e-learning in medical education. Consequently, several studies evaluating performance in e-learning in medical education do so by conducting pre-Test and post-Test with no defined framework or model to guide the evaluation. This makes the findings from these studies subjective and biased since factors that possibly impact students' performance were neither considered in the design of the course nor measured and reported in the evaluation studies. We, therefore, introduce an essential pedagogical e-learning concept by developing a framework to inform the design and evaluation of students' performance in e-learning in medical education via the thoughtful fusion of the Task-Technology Fit Model and the Kirkpatrick Evaluation Model. Our hybrid framework was piloted at the University of KwaZulu-Natal, Durban, South Africa and findings emphasize the need for alignment between learning tasks, technology infrastructures, individual traits, and contextual limitations of students as key factors in determining how well students perform in the classroom and their clinical practices at work. This study advances the body of knowledge by providing a well-brainstormed and intricately designed framework to guide the design of courses and evaluation of student's performance in an e-learning context in medical education. © 2022 IEEE.