ABSTRACT
This study seeks to explore Malaysian undergraduates' perspectives on the implementation of remote learning in their university during the period of the movement control order (MCO). Since teaching and learning activities have been impacted by the pandemic, it is imperative to consider students' perspectives on carrying out classes via the online platform as many studies claim that the pandemic has disrupted teaching and learning activities. A total of 1,028 undergraduate students participated in this voluntary study by answering an open-ended survey sent out to their student email addresses during the MCO period that restricted students and lecturers from going to the university. The qualitative responses from the students were critically analyzed for thematic patterns. The four themes emerging from the data provide future teaching and learning plans that should embed self-learning techniques that could aid students if a similar predicament should hit us in the future. Course instructors can use this information to design future lessons that could assist their learners better. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
End of the year 2019, the world has been shocked by a novel coronavirus disease 2019 (COVID-19), and the world is facing a huge health crisis due to the rapid transmission of COVID-19. World Health Organization (WHO) advised wearing a face cover in public places and crowded areas as a part of a comprehensive element to preventive and control measures to limit the spread of COVID-19. However, it is not easy to monitor people manually in these areas. In this paper, a detector model using deep learning for face coverings detection will be presented based on DEtection TRansformer (DETR) algorithm. The custom dataset has been used through this research, which comprises different face covering such as face mask, face shield, niqab, and purdah. The results concluded that the proposed model achieved the highest accuracy percentage of 92.38% and the highest recall percentage of 86.21% as a detector model. Finally, a comparative result with other face coverings model has been presented at the end of the research. The proposed model has achieved higher accuracy, precision, recall and specificity than the other model. © 2021 IEEE.