  The COVID-19 Omicron variant, reported to be the most immune evasive variant
of COVID-19, is resulting in a surge of COVID-19 cases globally. This has
caused schools, colleges, and universities in different parts of the world to
transition to online learning. As a result, social media platforms such as
Twitter are seeing an increase in conversations related to online learning in
the form of tweets. Mining such tweets to develop a dataset can serve as a data
resource for different applications and use-cases related to the analysis of
interest, views, opinions, perspectives, attitudes, and feedback towards online
learning during the current surge of COVID-19 cases caused by the Omicron
variant. Therefore, this work presents a large-scale open-access Twitter
dataset of conversations about online learning from different parts of the
world since the first detected case of the COVID-19 Omicron variant in November
2021. The dataset is compliant with the privacy policy, developer agreement,
and guidelines for content redistribution of Twitter, as well as with the FAIR
principles (Findability, Accessibility, Interoperability, and Reusability)
principles for scientific data management. The paper also briefly outlines some
potential applications in the fields of Big Data, Data Mining, Natural Language
Processing, and their related disciplines, with a specific focus on online
learning during this Omicron wave that may be studied, explored, and
investigated by using this dataset.
