Increasing Public Interest in Online Education during the COVID-19 Pandemic in the United States: An Analysis of Google Trends Data
DOI:
https://doi.org/10.12775/JEHS.2024.72.57390Keywords
Google Trends, Online education, Search engine, Infodemiology, Date miningAbstract
Objective: Current evidence suggests that the shift to online learning during the COVID-19 pandemic has profoundly impacted teaching and learning models. This study aims to quantify trends in public interest in different forms of education and associated online search behaviors during the pandemic. Furthermore, it seeks to "nowcast" potential future scenarios concerning the evolution of online education.
Methods: Google Trends, a publicly available database, was employed to systematically and quantitatively analyze search query data for key terms related to online education. This study involved querying multiple search volumes for online education, identifying the most commonly used terms, and extracting data from the United States for the period between January 1, 2019, and January 1, 2023. The results are presented using the Google metric 'search volume index' in relative terms.
Results: The public search interest for keywords related to online education experienced a significant surge starting in March 2020, followed by a gradual decline beginning in August 2020. When comparing the average relative search volume (RSV) changes for terms such as "online school," "online education," "online teaching," and "online learning" in the five months preceding and following March 1, 2020, the average search volumes increased by 46.6%, 30.7%, 103.8%, and 188.3%, respectively. Online search interest in e-learning software demonstrated a similar trend. Among platforms like Zoom, Skype, WebEx, and Google Meet, the majority of Google users displayed a clear preference for Zoom.
Conclusion: During the COVID-19 pandemic, public interest in online education surged to unprecedented levels, potentially reshaping teaching and learning practices for the foreseeable future. This suggests that the integration and use of digital media in education hold significant potential and offer considerable room for further development.
References
Adelhoefer, S., Siegfried, H., Travis, S. H., Blankstein, R., Graham, G., Blaha, M. J., & Dzaye, O. (2021). Declining interest in clinical imaging during the COVID-19 pandemic: An analysis of Google Trends data. Clinical Imaging, 20–22. https://doi.org/10.1016/j.clinimag.2021.05.015
Bhasin, B., Gupta, G., & Malhotra, S. (2021). Impact of COVID-19 pandemic on education system. International Journal of Engineering Research & Technology, 10(5), 894–898.
Bhimavarapu, U. (2023). Analysing student performance for online education using the computational models. Universal Access in the Information Society, 23(3), 1051–1058. https://doi.org/10.1007/s10209-023-01033-7
Carneiro, H. A., & Mylonakis, E. (2009). Google Trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557–1564. https://doi.org/10.1086/630200
Cervellin, G., Comelli, I., & Lippi, G. (2017). Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. Journal of Epidemiology and Global Health, 7(3), 185–189. https://doi.org/10.1016/j.jegh.2017.06.001
Chen, T., Peng, L., Jing, B., Wu, C., Yang, J., & Cong, G. (2020). The impact of the COVID-19 pandemic on user experience with online education platforms in China. Sustainability, 12(18), 7329. https://doi.org/10.3390/su12187329
Chen, Y., Hou, A. Y. C., & Huang, L. (2022). Development of distance education in Chinese higher education in perspectives of accessibility, quality and equity under COVID-19. Asian Education and Development Studies, 11(2), 356–365. https://doi.org/10.1108/AEDS-05-2020-0118
Czerniewicz, L., & Carvalho, L. (2023). Open, distance, and digital education (ODDE): An equity view. In O. Zawacki-Richter & I. Jung (Eds.), Handbook of Open, Distance and Digital Education (pp. 441–459). Springer. https://doi.org/10.1007/978-981-19-2080-6_93
Dost, S., Hossain, A., Shehab, M., Abdelwahed, A., & Al-Nusair, L. (2020). Perceptions of medical students towards online teaching during the COVID-19 pandemic: A national cross-sectional survey of 2721 UK medical students. BMJ Open, 10(11), e042378. https://doi.org/10.1136/bmjopen-2020-042378
Hagen, K. (2022). Confucian education: From conformity to cultivating personal distinction. Dao, 21, 213–234. https://doi.org/10.1007/s11712-022-09826-y
Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188–195. https://doi.org/10.1016/j.neucom.2018.01.046
Intania, E. V., & Sutama, S. (2020). The role of character education in learning during the COVID-19 pandemic. International Journal of Multicultural and Multireligious Understanding, 7(10), 460–467. https://doi.org/10.18415/ijmmu.v7i10.2173
Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69–87. https://doi.org/10.1016/j.techfore.2017.11.009
Kruthika, R., Balasubramanian, P., & Sureshkumar, V. (2018). Relationship between Google Trends data and index returns. In 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC) (pp. 1–5). https://doi.org/10.1109/ICCPEIC.2018.8525230
Korkmaz, G., & Toraman, Ç. (2020). Are we ready for the post-COVID-19 educational practice? An investigation into what educators think as to online learning. International Journal of Technology in Education and Science, 4(4), 293–309. https://doi.org/10.46328/ijtes.v4i4.110
Li, C., Tan, Q., Zou, M., Zeng, L., Kang, M., & Chen, L. (2022). Significantly Increased Public Interest in Major Depressive Disorder During the COVID-19 Pandemic: Insights From a Google Trends Analysis. Cureus, 14(1), e21228. https://doi.org/10.7759/cureus.21228
Li, X. (2022). Targeted teaching: Meeting students where they are. Times Higher Education. Retrieved from https://www.timeshighereducation.com/campus/targeted-teaching-meeting-students-where-they-are
Li, W., Gillies, R., He, M., Wu, C., Liu, S., Gong, Z., & Sun, H. (2021). Barriers and facilitators to online medical and nursing education during the COVID-19 pandemic: Perspectives from international students from low- and middle-income countries and their teaching staff. Human Resources for Health, 19(1), 64. https://doi.org/10.1186/s12960-021-00609-9
Liu, Y., Peng, G., Hu, L., Dong, J., & Zhang, Q. (2020). Using Google Trends and Baidu Index to analyze the impacts of disaster events on company stock prices. Industrial Management & Data Systems, 120(7), 1337–1350. https://doi.org/10.1108/IMDS-12-2019-0640
Long, Q. (2022). Confucius’s educational thoughts in the Analects. Open Journal of Social Sciences, 10(3), 325–332. https://doi.org/10.4236/jss.2022.103027
Mavragani, A., & Ochoa, G. (2019). Google Trends in infodemiology and infoveillance: Methodology framework. JMIR Public Health and Surveillance, 5(2), e13439. https://doi.org/10.2196/13439
Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The use of Google Trends in health care research: A systematic review. PLOS ONE, 9(10), e109583. https://doi.org/10.1371/journal.pone.0109583
Pelat, C., Turbelin, C., Bar-Hen, A., Flahault, A., & Valleron, A. J. (2009). More diseases tracked by using Google Trends. Emerging Infectious Diseases, 15(8), 1327–1328. https://doi.org/10.3201/eid1508.090299
Rajab, M. H., Gazal, A. M., & Alkattan, K. (2020). Challenges to online medical education during the COVID-19 pandemic. Cureus, 12(7), e8966. https://doi.org/10.7759/cureus.8966
Sankey, T., Belmonte, A., Massey, R., & Leonard, J. (2020). Regional-scale forest restoration effects on ecosystem resiliency to drought: A synthesis of vegetation and moisture trends on Google Earth Engine. Remote Sensing in Ecology and Conservation, 6(3), 297–310. https://doi.org/10.1002/rse2.148
Schmidt, T., & Vosen, S. (2011). Forecasting private consumption: Survey-based indicators vs. Google Trends. Journal of Forecasting, 30(6), 565–578. https://doi.org/10.1002/for.1213
Sun, S., Wang, S., Wei, Y., Yang, X., & Tsui, K.-L. (2017). Forecasting tourist arrivals with machine learning and internet search index. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 1911–1916). https://doi.org/10.1109/BigData.2017.8258122
Tan, C. (2017). Confucianism and education. In G. W. Noblit (Ed.), Oxford Research Encyclopedia of Education. Oxford University Press. https://doi.org/10.1093/acrefore/9780190264093.013.2
Tan, Q. (2021). Barriers to Inclusive Education in Chinese Primary Schools: Culture, Policy and Practice. Taylor and Francis. https://doi.org/10.4324/9781003173649
Teng, L., Tan, Q., & Ehsani, A. (2022). Assessing the impact of cultural characteristics, economic situations, skills, and knowledge on the development and success of cloud-based e-learning systems in the COVID-19 era. Kybernetes, 9, 2795–2813. https://doi.org/10.1108/K-12-2020-0838
United Nations (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations. Retrieved from https://sustainabledevelopment.un.org
United Nations Development Programme (UNDP). (2020). The sustainable development goals report 2020. United Nations. Retrieved from https://undp.org
Vaughan, L., & Chen, Y. (2015). Data mining from web search queries: A comparison of Google Trends and Baidu Index. Journal of the Association for Information Science and Technology, 66(1), 13–22. https://doi.org/10.1002/asi.23201
Wijetunga, S. C. (2024). Student Engagement in the Online Learning Environment during the COVID-19 Pandemic in Sri Lanka. Asian Journal of Agricultural Extension, Economics & Sociology, 42(8), 69–86. https://doi.org/10.9734/ajaees/2024/v42i830952
Xue, T., & Liu, H. (2017). Prediction of social risk perception on petition in China. In 2017 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC) (pp. 1–6). IEEE. https://doi.org/10.1109/BESC.2017.8256375
Xu, J., Cao, Y., Qiao, Q., & Qian, Y. (2021). Sports in the transnational public sphere: Findings from the case of Daryl Morey’s Hong Kong tweet. The International Journal of the History of Sport, 38(2–3), 314–333. https://doi.org/10.1080/09523367.2021.1908290
Yang, S., Santillana, M., & Kou, S. C. (2015). ARGO: A model for accurate estimation of influenza epidemics using Google search data. Journal of Computational Biology, 22(10), 1–10. https://doi.org/10.1089/cmb.2014.0327
Zhang, H., Tang, K., Wang, Y., Fang, R., & Sun, Q. (2020). General interest in rosacea in the United States and China: A search engine-based pilot study. International Journal of Dermatology and Venereology, 3(1), 1–5. https://doi.org/10.1097/JD9.0000000000000118
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