Dalveren, Gonca Gökçe MenekşeGurcan, FatihDalveren, Gonca Gokce MenekseDerawi, MohammadInformation Systems Engineering2024-07-052024-07-05202272169-353610.1109/ACCESS.2022.32240342-s2.0-85144022803https://doi.org/10.1109/ACCESS.2022.3224034https://hdl.handle.net/20.500.14411/1669GURCAN, Fatih/0000-0001-9915-6686; Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; Derawi, Mohammad/0000-0003-0448-7613E-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.eninfo:eu-repo/semantics/openAccessE-learning trendstopic modelingn-gramscovid-19topic discoveryArticleQ2Q110123349123357WOS:000894940000001