Necessity and efficacy of Self-learning during Covid-19 Pandemic
Keywords:
Self-learning, Covid-19, R-programming, logistic regression, ggplot, data cleaning, correlationAbstract
Self-learning is the process when someone is trying to gather information and process it on their own. It is the modern way of improving one`s own skills or learning something new on their own. Many people do it in their own interest while some have to do it because of the need of the situation. Because of the Covid-19 pandemic, the traditional teaching methods cannot be followed leading students to learn on their own. In this paper, we are going to discuss the necessity and effectiveness among students related to Self-Learning before the pandemic and after the pandemic. Data was gathered by performing surveys where students have answered some choice-based questions, that will help us with our analysis.
References
Deepansh Sharma, Gulsimar Kaur, Himanshu Arora, Ishan Sharma, Jyoti Diwakar, Muskan Jain “Comprehensive Analysis of Different Regression Techniques and its Applications used in Machine Learning”, International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue:07 July 2020.
V.Lakshmi, K.Harika, H.Bhavishya, Ch. Sri Harsha “SENTIMENT ANALYSIS OF TWITTER DATA” International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 02 Feb -2017.
Navid Aghdaei, Georgios Kokogiannakis, Daniel Daly, Timothy McCarthy, “Linear Regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings”, International Conference on Improving Residential Energy Efficiency, IREE 2017, ppno: 79- 86, 2017.
Macedo Maia; Andrea Freitas; Siegfried Handschuh, FinSSLx: “A Sentiment Analysis Model for the Financial Domain Using Text Simplification”, 2018 IEEE 12th International Conference on Semantic Computing (ICSC) 2 Feb. 2018.
Di Renzo L, Gualtieri P, Pivari F, Soldati L, Attinà A, Cinelli G, et al. “Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey”. J Transl Med. 2020;18(1):229. pmid:32513197.
Palvia S, Aeron P, Gupta P, Mahapatra D, Parida R, Rosner R, et al. Online Education: Worldwide Status, Challenges, Trends, and Implications. Journal of Global Information Technology Management; Volume 21(4): 233–241. 2018.
Rizun M, Strzelecki A. Student’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. Int J Environ Res Public Health. 2020;17(18): 6468.
Jeffery, K. A., and Bauer, C. F. Students’ responses to emergency remote online teaching reveal critical factors for all teaching. J. Chem. Educ. 97: 2472–2485. 2020.
Li, L. Y., & Tsai, C. C. Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers and Education, 114, 286–297.2017.
Lubis, M.. Peluang Pemanfaatan Pembelajaran Berorientasi Teknologi Informasi di Lingkup Madrasah (Mempersiapkan Madrasah Berwawasan Global). Tadris: Jurnal Keguruan Dan Ilmu Tarbiyah, 1(2), 147–153. 2016.
Mandusic, D., & Blaskovic, L.” The impact of collaborative learning on critical thinking”. Trakia Journal of Science, 13(1), 426–428. 2015.
Nguyen T. “The Effectiveness of Online Learning: Beyond No Significant Difference and Future Horizons”. MERLOT Journal of Online Learning and Teaching, 11(2), 309. 2015.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
