Covid-19 and E-Learning: An Exploratory Analysis of Research Topics and Interests in E-Learning During the Pandemic

Abstract

E-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic. Therefore, it is critical to examine trends and interests in e-learning research and applications during the pandemic period. This paper aims to identify trends and research interests in e-learning articles related to COVID-19 pandemic. Consistent with this aim, a semantic content analysis was conducted on 3562 peer-reviewed journal articles published since the beginning of the COVID-19 pandemic, using the N-gram model and Latent Dirichlet Allocation (LDA) topic modeling approach. Findings of the study revealed the high-frequency bigrams such as “online learn”, “online education”, “online teach” and “distance learn”, as well as trigrams such as “higher education institution”, “emergency remote teach”, “education online learn” and “online teach learn”. Moreover, the LDA topic modeling analysis revealed 42 topics. The topics of “Learning Needs”, “Higher Education” and “Social Impact” respectively were the most focused topics. These topics also revealed concepts, dimensions, methods, tools, technologies, applications, measurement and evaluation models, which are the focal points of e-learning field during the pandemic. The findings of the study are expected to provide insights to researchers and future studies.

Keywords

• E-learning trends
• topic modeling
• n-grams
• covid-19
• topic discovery