A Dataset on Online Learning-based Web Behavior from Different Countries Before and After COVID-19

Citation Author(s):
Nirmalya
Thakur
Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA
Saumick
Pradhan
Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA
Chia Y
Han
Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA
Submitted by:
Saumick Pradhan
Last updated:
Sat, 11/27/2021 - 14:42
DOI:
10.21227/pa7d-nt11
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Abstract 

Any work using this dataset should cite the following paper:

Nirmalya Thakur, Saumick Pradhan, and Chia Y. Han, “Investigating the impact of COVID-19 on Online Learning-based Web Behavior”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022 (Submitted)

Abstract

COVID-19, a pandemic that the world has not seen in decades, has resulted in presenting a multitude of unprecedented challenges for student learning and education across the globe. The global surge in COVID-19 cases resulted in several schools, colleges, and universities closing in 2020 in almost all parts of the world and switching to online or remote learning, which has impacted student learning in different ways. This has resulted in both educators and students spending more time on the internet than ever before, which may be broadly summarized as both these groups investigating, learning, and familiarizing themselves with information, tools, applications, and frameworks to adapt to online or remote learning. Studying such web behavior, in the form of Big Data mining and analysis, originating from different countries of the world provides the scope for identifying, investigating, and quantifying the needs, interests, and challenges related to online learning in different countries of the world on account of COVID-19. Therefore, this work presents an open-access dataset that consists of the web behavior related to online learning that originated from different countries of the world on a monthly basis from 2004-2021. For the development of this dataset, the web behavior data in the form of search interests related to online learning, recorded from Google Searches, was mined using Google Trends, as Google is the most popular search engine across the world. Even though the first case of COVID-19 in humans was recorded in 2019, the dataset presents the web behavior data related to online learning starting from 2004, so that the degrees to which web behavior related to online learning changed and the trends in these changes in different countries of the world can be quantified and interpreted easily. At this point, the dataset consists of the web behavior data related to online learning for the 20 countries which were worst affected by COVID-19 at the time of development of this dataset. Future work on this dataset would involve incorporating more countries into the study and expanding the dataset.

Data Description

The dataset consists of one .csv file named – “Online_Learning_Data.csv”. The data was collected by using Google Trends on October 7th, 2021. This dataset has 21 attributes. The first attribute, “Month,” stands for the month from January 2004 to October 2021, as the data was collected on a monthly basis in this range. The remaining 20 attributes stand for each of the 20 countries - USA, India, Brazil, UK, Russia, France, Turkey, Iran, Argentina, Colombia, Spain, Italy, Indonesia, Germany, Mexico, Poland, South Africa, Philippines, Ukraine, and Peru, that were a part of this research study. Each of these attributes that are named after one of these countries represents the search interest related to online learning from that specific country on a monthly basis in this time range. The minimum value of this search interest is 0, and the maximum value is 100. These minimum and maximum values of search interests are as per the scaling factor used by Google Trends for all Google Search data.

Details on the methodology and procedure that were followed for the development of this dataset are included in the above-mentioned paper. For any questions related to this dataset or the paper, please contact Nirmalya Thakur at thakurna@mail.uc.edu

Instructions: 

For details on instructions on how to use the dataset, the above mentioned paper may be studied.