Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic
Abstract
:1. Introduction
2. Literature Study
3. Materials and Methods
3.1. Google Trend Analysis on Whiteboard Software’s
3.2. Text Analysis
- Query: <Zoom meeting app download, Zoom Chinese app>,
- <Google meet for pc, how to use Google to meet>,
- < Byjus bnat>,
- <Whitehat junior salary>,
- < Whitehat junior dashboard>,
- < Khan academy>,
- < Un academy scholarship test>,
- < Udemy free courses during a lockdown, Swayam biomedical research>.
4. Results
4.1. Pattern Identification on COVID-19
4.2. Google Trend Analysis
5. Discussion
5.1. The Socio-Economic Impact through the New Education System of Online Learning
5.2. Social Impact of Digital Learning
5.3. Recommendations and Future Scope
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Ethical Approval
References
- Battineni, G.; Chintalapudi, N.; Amenta, F. Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model. Appl. Comput. Inform. 2020. [Google Scholar] [CrossRef]
- Chavez, S.; Long, B.; Koyfman, A.; Liang, S.Y. Coronavirus Disease (COVID-19): A primer for emergency physicians. Am. J. Emerg. Med. 2020. [Google Scholar] [CrossRef] [PubMed]
- Chawla, S.; Mittal, M.; Chawla, M.; Goyal, L. Corona Virus—SARS-CoV-2: An Insight to Another way of Natural Disaster. EAI Endorsed Trans. Pervasive Health Technol. 2020, 6, 1–5. [Google Scholar] [CrossRef]
- Chhetri, B.; Goyal, L.M.; Mittal, M.; Battineni, G. Estimating the prevalence of stress among Indian students during the COVID-19 pandemic: A cross-sectional study from India. J. Taibah Univ. Med Sci. 2021, 16, 260–267. [Google Scholar] [CrossRef] [PubMed]
- Take This Pandemic Moment to Improve Education|EdSource. Available online: https://edsource.org/2020/take-this-pandemic-moment-to-improve-education/633500 (accessed on 7 May 2021).
- Arora, M.; Goyal, L.M.; Chintalapudi, N.; Mittal, M. Factors affecting digital education during COVID-19: A statistical modeling approach. In Proceedings of the 2020 5th International Conference on Computing, Communication and Security (ICCCS), Patna, India, 14–16 October 2020; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2020; pp. 1–5. [Google Scholar]
- El Firdoussi, S.; Lachgar, M.; Kabaili, H.; Rochdi, A.; Goujdami, D.; El Firdoussi, L. Assessing Distance Learning in Higher Education during the COVID-19 Pandemic. Educ. Res. Int. 2020, 2020, 8890633. [Google Scholar] [CrossRef]
- Chintalapudi, N.; Battineni, G.; Amenta, F. Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models. Infect. Dis. Rep. 2021, 13, 329–339. [Google Scholar] [CrossRef]
- Li, C.; Chen, L.J.; Chen, X.; Zhang, M.; Pang, C.P.; Chen, H. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance 2020, 25, 2000199. [Google Scholar] [CrossRef]
- Carneiro, H.A.; Mylonakis, E. Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks. Clin. Infect. Dis. 2009, 49, 1557–1564. [Google Scholar] [CrossRef]
- Strzelecki, A. The second worldwide wave of interest in coronavirus since the COVID-19 outbreaks in South Korea, Italy and Iran: A Google Trends study. Brain, Behav. Immun. 2020, 88, 950–951. [Google Scholar] [CrossRef]
- Chintalapudi, N.; Battineni, G.; Di Canio, M.; Sagaro, G.G.; Amenta, F. Text mining with sentiment analysis on seafarers’ medical documents. Int. J. Inf. Manag. Data Insights 2021, 1, 100005. [Google Scholar] [CrossRef]
- Saire, J.E.C.; Cruz, J.F.O. Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach. In Proceedings of the 2020 International Computer Symposium (ICS), Tainan, Taiwan, 17–19 December 2020; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2020; Volume 2020, pp. 242–246. [Google Scholar]
- Han, X.; Wang, J.; Zhang, M.; Wang, X. Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China. Int. J. Environ. Res. Public Health 2020, 17, 2788. [Google Scholar] [CrossRef] [Green Version]
- Ferreira-Mello, R.; André, M.; Pinheiro, A.; Costa, E.; Romero, C. Text mining in education. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2019, 9. [Google Scholar] [CrossRef]
- Tieben, N.; Wolbers, M.H.J. Transitions to post-secondary and tertiary education in the Netherlands: A trend analysis of unconditional and conditional socio-economic background effects. High. Educ. 2009, 60, 85–100. [Google Scholar] [CrossRef] [Green Version]
- Ayyoubzadeh, S.M.; Zahedi, H.; Ahmadi, M.; Kalhori, S.R.N. Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study. JMIR Public Heal. Surveill. 2020, 6, e18828. [Google Scholar] [CrossRef] [PubMed]
- Farzanegan, M.R.; Feizi, M.; Sadati, S.M. Google It Up! A Google Trends-Based Analysis of COVID-19 Outbreak in Iran. MAGKS Pap Econ. Published Online 2020. Available online: https://ideas.repec.org/p/mar/magkse/202017.html (accessed on 7 May 2021).
- Tripathy, S.; Devarapalli, S. Emerging trend set by a start-ups on Indian online education system: A case of Byju’s. J. Public Aff. 2021, 21. [Google Scholar] [CrossRef]
- Mishra, L.; Gupta, T.; Shree, A. Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. Int. J. Educ. Res. Open 2020, 1, 100012. [Google Scholar] [CrossRef]
- Pokhrel, S.; Chhetri, R. A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning. High. Educ. Futur. 2021, 8, 133–141. [Google Scholar] [CrossRef]
- Ferrel, M.N.; Ryan, J.J. The Impact of COVID-19 on Medical Education. Cureus 2020, 12, e7492. [Google Scholar] [CrossRef] [Green Version]
- Espino-Díaz, L.; Fernandez-Caminero, G.; Hernandez-Lloret, C.-M.; Gonzalez-Gonzalez, H.; Alvarez-Castillo, J.-L. Analyzing the Impact of COVID-19 on Education Professionals. Toward a Paradigm Shift: ICT and Neuroeducation as a Binomial of Action. Sustainability 2020, 12, 5646. [Google Scholar] [CrossRef]
- Jena, P.K. Impact of Pandemic COVID-19 on Education in India. Available online: https://papers.ssrn.com/abstract=3691506 (accessed on 7 May 2021).
- Joshi, A.; Vinay, M.; Bhaskar, P. Impact of coronavirus pandemic on the Indian education sector: Perspectives of teachers on online teaching and assessments. Interact. Technol. Smart Educ. 2020. [Google Scholar] [CrossRef]
- COVID-19 Open Research Dataset Challenge (CORD-19)|Kaggle. Available online: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge (accessed on 7 May 2021).
- Wang, Y. Government policies, national culture and social distancing during the first wave of the COVID-19 pandemic: International evidence. Saf. Sci. 2021, 135, 105138. [Google Scholar] [CrossRef]
- Johnson, C.P.; Myers, S.M.; Disabilities, A.A.O.P.C.O.C.W. Identification and Evaluation of Children With Autism Spectrum Disorders. Pediatrics 2007, 120, 1183–1215. [Google Scholar] [CrossRef] [Green Version]
- Barret, J.P.; Chong, S.J.; Depetris, N.; Fisher, M.D.; Luo, G.; Moiemen, N.; Pham, T.; Qiao, L.; Wibbenmeyer, L.; Matsumura, H. Burn center function during the COVID-19 pandemic: An international multi-center report of strategy and experience. Burns 2020, 46, 1021–1035. [Google Scholar] [CrossRef] [PubMed]
- Chatterjee, I.; Chakraborty, P. Use of Information Communication Technology by Medical Educators Amid COVID-19 Pandemic and Beyond. J. Educ. Technol. Syst. 2021, 49, 310–324. [Google Scholar] [CrossRef]
- Mittal, M.; Kaur, I.; Pandey, S.; Verma, A.; Goyal, L. Opinion Mining for the Tweets in Healthcare Sector using Fuzzy Association Rule. EAI Endorsed Trans. Pervasive Health Technol. 2018, 4. [Google Scholar] [CrossRef]
- Aggarwal, A.; Mittal, M.; Battineni, G. Generative adversarial network: An overview of theory and applications. Int. J. Inf. Manag. Data Insights 2021, 1, 100004. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kansal, A.K.; Gautam, J.; Chintalapudi, N.; Jain, S.; Battineni, G. Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic. Infect. Dis. Rep. 2021, 13, 418-428. https://doi.org/10.3390/idr13020040
Kansal AK, Gautam J, Chintalapudi N, Jain S, Battineni G. Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic. Infectious Disease Reports. 2021; 13(2):418-428. https://doi.org/10.3390/idr13020040
Chicago/Turabian StyleKansal, Ashwani Kumar, Jyoti Gautam, Nalini Chintalapudi, Shivani Jain, and Gopi Battineni. 2021. "Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic" Infectious Disease Reports 13, no. 2: 418-428. https://doi.org/10.3390/idr13020040
APA StyleKansal, A. K., Gautam, J., Chintalapudi, N., Jain, S., & Battineni, G. (2021). Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic. Infectious Disease Reports, 13(2), 418-428. https://doi.org/10.3390/idr13020040