ABSTRACT
The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning. © 2022 IEEE.
ABSTRACT
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.