The interdisciplinary work at the intersections of Big Data, Data Mining, and Data Analysis presented in this paper, focuses on the mining and analysis of web behavior on Google related to different online learning platforms from different countries, since the beginning of COVID-19. This paper makes multiple scientific contributions to these fields. First, a comprehensive review of about 150 recent works was conducted to identify a list of 101 online learning platforms that were used in different parts of the world during COVID-19. Second, using Google Trends, the search interests related to these online learning platforms emerging from all 38 OECD countries for 133 weeks between March 11, 2020, and October 1, 2022, were mined, and a database was developed. Third, K-means clustering was run on this database 10,000 times to identify clusters based on search interests. Fourth, a recursive algorithm was developed and run on this database to identify the list of online learning platforms that recorded very high search interests, the specific countries from which these platforms recorded such interests, and the associated queries on Google related to these platforms that contributed to the high search interests. These results, along with the original database, were published as a dataset on IEEE Dataport. Finally, two comprehensive comparative studies are presented that compare the findings of this paper with about 150 prior works in this field to uphold its novelty and significance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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