Open Access Peer-reviewed Research Article

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Dilkhaz Yaseen Mohammed corresponding author


COVID-19, a pandemic that the world has not seen in decades, has caused several new obstacles for student learning and education throughout the globe. As a consequence of the worldwide surge of COVID-19 instances, several schools and institutions in almost every region of the globe have closed in 2020 or switched to online or remote learning, which will have a variety of repercussions for student learning. This has led to educators and students spending more time online than ever before, with both groups researching, learning, and familiarizing themselves with information, resources, tools, and frameworks to adapt to online or remote learning. Data mining and analysis are being done to analyze such online activity. For the construction of this dataset, the web-based data in the form of search interests connected to online learning, gathered through Google searches, was mined using Google Trends. Currently, the dataset comprises web-based data related to online learning for the 20 nations that COVID-19 negatively touched at the time of its construction. This project aims to create and evaluate time-series forecasting models of a country's end-of-term performance, explore how the pandemic has influenced the migrations of people throughout the globe, and estimate the nations' future online learning needs. Regression techniques such as linear regression, multilayer regression, and SMO regression are utilized. This is done by looking at previous data, identifying the trends, and creating short-term or long-term projections. The data demonstrate that the approach of SMO regression causes fewer errors with improved accuracy compared to others.

Covid-19, pandemic, online learning, regression techniques, Weka forecast

Article Details

How to Cite
Mohammed, D. Y. (2022). The web-based behavior of online learning: An evaluation of different countries during the COVID-19 pandemic. Advances in Mobile Learning Educational Research, 2(1), 263-267.


  1. Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: the challenges and opportunities. Interactive learning environments, 1-13.
  2. Advanced data mining with weka MOOC - Material. (n.d.). Department of Computer Science: University of Waikato.
  3. Brownlee, J. (2020). What is time series forecasting? Machine Learning Mastery.
  4. Kallou, S., & Kikilia, A. (2021). A transformative educational framework in tourism higher education through digital technologies during the COVID-19 pandemic. Advances in Mobile Learning Educational Research, 1(1), 37-47.
  5. Karakose, T., Ozdemir, T. Y., Papadakis, S., Yirci, R., Ozkayran, S. E., & Polat, H. (2022). Investigating the Relationships between COVID-19 Quality of Life, Loneliness, Happiness, and Internet Addiction among K-12 Teachers and School Administrators—A Structural Equation Modeling Approach. International Journal of Environmental Research and Public Health, 19(3), 1052. MDPI AG.
  6. Karakose, T., Polat, H., & Papadakis, S. (2021). Examining Teachers’ Perspectives on School Principals’ Digital Leadership Roles and Technology Capabilities during the COVID-19 Pandemic. Sustainability, 13(23), 13448. MDPI AG.
  7. Karakose, T., Yirci, R., & Papadakis, S. (2021). Exploring the Interrelationship between COVID-19 Phobia, Work–Family Conflict, Family–Work Conflict, and Life Satisfaction among School Administrators for Advancing Sustainable Management. Sustainability, 13(15), 8654. MDPI AG.
  8. Karakose, T., Yirci, R., & Papadakis, S. (2022). Examining the Associations between COVID-19-Related Psychological Distress, Social Media Addiction, COVID-19-Related Burnout, and Depression among School Principals and Teachers through Structural Equation Modeling. International Journal of Environmental Research and Public Health, 19(4), 1951. MDPI AG.
  9. Karakose, T., Yirci, R., Papadakis, S., Ozdemir, T. Y., Demirkol, M., & Polat, H. (2021). Science Mapping of the Global Knowledge Base on Management, Leadership, and Administration Related to COVID-19 for Promoting the Sustainability of Scientific Research. Sustainability, 13(17), 9631. MDPI AG.
  10. Katsaris, I., & Vidakis, N. (2021). Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research, 1(2), 124-145.
  11. Konstantopoulou, G., Dimitra, V., Papakala, I., Styliani, R., Vasiliki, T., Ioakeimidi, M., Niros, A., Boutis, M., & Iliou, T. (2022). The mental resilience of employees in special education during the pandemic Covid-19. Advances in Mobile Learning Educational Research, 2(1), 246-250.
  12. Thakur, N., Pradhan, S., & Han, c. Y. (2021). A Dataset on Online Learning-based Web Behavior from Different Countries Before and After COVID-19. IEEE Dataport.
  13. Papadopoulou, E., Parlapani, E., & Armakolas, S. (2022). Online conferencing platforms as operational tools by health professionals: A pilot study. Advances in Mobile Learning Educational Research, 2(1), 225-233.
  14. Poultsakis, S., Papadakis, S., Kalogiannakis, M., & Psycharis, S. (2021). The management of Digital Learning Objects of Natural Sciences and Digital Experiment Simulation Tools by teachers. Advances in Mobile Learning Educational Research, 1(2), 58-71.
  15. Ritchie, H., Mathieu, E., Rod´es-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., Hasell, J., Macdonald, B., Beltekian, D., & Roser, M. (2020, March 5). Coronavirus pandemic (COVID-19). Our World in Data. R
  16. Rossinot, H., Fantin, R., & Venne, J. (2020). Behavioral changes during COVID-19 confinement in France: a web-based study. International journal of environmental research and public health, 17(22), 8444.
  17. Tzimopoulos, N., Provelengios, P. & Iosifidou, M. (2021). Emergency remote teaching in Greece during the first period of the 2020 Covid-19 pandemic. Advances in Mobile Learning Educational Research, 1(1), 19-27.
  18. Waikato environment for knowledge analysis (WEKA). (n.d.). Weka - Data Mining with Open Source Machine Learning Software in Java.