Impact of Covid-19 epidemic on online learning and educational resources in China




education, online learning, teaching, Covid-19, multimedia


Online education was widely launched worldwide with the increasing impact of Covid-19 and multiple online platforms were developed or improved due to their demands. Several surveys have been conducted to analyze the Covid-19 impact on education, revealing the importance of information and communication technology (ICT). This epidemic has significantly changed all education levels and dramatically increased online learning. Online learning has various benefits with few drawbacks such as resources, economic effects, time, travelling, and so on. In this paper, we describe the impact of Covid-19 on education in China, the education of international students, problems in online learning, and the supportive technologies during this epidemic. Distance learning has been studied for years, expressed that it enhances the learners with lower paybacks; therefore, it was diminished dramatically. All these concerns will help us to understand global reforms and situations. We have also described affected regions, virus types, the Covid-19 cycle, and procedures to secure our education systems. Furthermore, we have highlighted some key issues of biosafety that will support the community to understand the standard procedures for developing a safe environment. Teachers play a key role in the development of a nation, and this study also enlightened the perspectives which should be addressed in future research.


Download data is not yet available.


P. G. Altbach, “Impact and adjustment: Foreign students in comparative perspective,” High. Educ., vol. 21, no. 3, pp. 305–323, 1991. DOI:

M. Tang, “The current situation and learning strategies of foreign students in Chinese learning following entrepreneurial psychology,” Front. Psychol., vol. 12, p. 746043, 2022. DOI:

L. Zhou and F. Li, “A review of the largest online teaching in China for elementary and middle school students during the COVID-19 pandemic,” Best Evid Chin Edu, vol. 5, no. 1, pp. 549–567, 2020. DOI:

S. Ghory and H. Ghafory, “The impact of modern technology in the teaching and learning process,” Int. J. Innov. Res. Sci. Stud., vol. 4, no. 3, pp. 168–173, 2021. DOI:

Z.-Y. Liu, N. Lomovtseva, and E. Korobeynikova, “Online learning platforms: Reconstructing modern higher education,” Int. J. Emerg. Technol. Learn., vol. 15, no. 13, pp. 4–21, 2020. DOI:

R. A. Rasheed, A. Kamsin, and N. A. Abdullah, “Challenges in the online component of blended learning: A systematic review,” Comput. Educ., vol. 144, p. 103701, 2020. DOI:

L. A. Cárdenas-Robledo and A. Peña-Ayala, “Ubiquitous learning: A systematic review,” Telemat. Informatics, vol. 35, no. 5, pp. 1097–1132, 2018. DOI:

X. Zhao, X. Li, J. Wang, and C. Shi, “Augmented reality (AR) learning application based on the perspective of situational learning: high efficiency study of combination of virtual and real,” Psychology, vol. 11, no. 9, pp. 1340–1348, 2020. DOI:

R. Chen et al., “Mental health status and change in living rhythms among college students in China during the COVID-19 pandemic: A large-scale survey,” J. Psychosom. Res., vol. 137, p. 110219, 2020. DOI:

Z. K. Yang, D. Wu, and X. D. Zhen, “Education informatization 2.0: the key historical transition of information technology reform education in the new era,” Educ. Res., no. 4, pp. 16–22, 2018.

C. McKown, “Challenges and opportunities in the applied assessment of student social and emotional learning,” Educ. Psychol., vol. 54, no. 3, pp. 205–221, 2019. DOI:

W. H. Bergquist and S. R. Phillips, “Components of an effective faculty development program,” J. Higher Educ., vol. 46, no. 2, pp. 177–211, 1975. DOI:

J. G. Gaff, General Education Today. A Critical Analysis of Controversies, Practices, and Reforms. ERIC, 1983.

L. Huigang, H. Cui, Z. Xiaoli, and Y. Zhiming, “Significance of and outlook for the biosecurity law of the People’s Republic of China,” J. Biosaf. Biosecurity, vol. 3, no. 1, pp. 46–50, 2021. DOI:

M. Bassi, A. Delle Fave, P. Steca, and G. V. Caprara, “Adolescents’ regulatory emotional self-efficacy beliefs and daily affect intensity,” Motiv. Emot., vol. 42, no. 2, pp. 287–298, 2018. DOI:

J. Xu, J. Du, and X. Fan, “Individual and group-level factors for students’ emotion management in online collaborative groupwork,” internet High. Educ., vol. 19, pp. 1–9, 2013. DOI:

H. Järvenoja et al., “Capturing Motivation and Emotion Regulation during a Learning Process.,” Front. Learn. Res., vol. 6, no. 3, pp. 85–104, 2018. DOI:

Z. Zhang, T. Liu, and C. B. Lee, “Language learners’ enjoyment and emotion regulation in online collaborative learning,” System, vol. 98, p. 102478, 2021. DOI:

Y. Li, “Constructing and sharing open educational resources: Policy and capacity,” in International Conference on ICT in Teaching and Learning, 2013, pp. 35–42. DOI:

M. Vitiello, S. Walk, V. Chang, R. Hernandez, D. Helic, and C. Guetl, “MOOC dropouts: A multi-system classifier,” in European Conference on Technology Enhanced Learning, 2017, pp. 300–314. DOI:

J. Chen, J. Feng, X. Sun, N. Wu, Z. Yang, and S. Chen, “MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine,” Math. Probl. Eng., vol. 2019, 2019. DOI:

S. Nagrecha, J. Z. Dillon, and N. V Chawla, “MOOC dropout prediction: lessons learned from making pipelines interpretable,” in Proceedings of the 26th International Conference on World Wide Web Companion, 2017, pp. 351–359. DOI:

X. Wang, D. Yang, M. Wen, K. Koedinger, and C. P. Rosé, “Investigating How Student’s Cognitive Behavior in MOOC Discussion Forums Affect Learning Gains.,” Int. Educ. Data Min. Soc., 2015.

M. F. Spivey and J. J. McMillan, “Using the Blackboard course management system to analyze student effort and performance,” J. Financ. Educ., pp. 19–28, 2013.

J. Qiu et al., “Modeling and predicting learning behavior in MOOCs,” in Proceedings of the ninth ACM international conference on web search and data mining, 2016, pp. 93–102. DOI:

H. B. Shapiro, C. H. Lee, N. E. W. Roth, K. Li, M. Çetinkaya-Rundel, and D. A. Canelas, “Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers,” Comput. Educ., vol. 110, pp. 35–50, 2017. DOI:

M. Wen, D. Yang, and C. Rose, “Sentiment Analysis in MOOC Discussion Forums: What does it tell us?,” 2014.

C. Tucker, B. K. Pursel, and A. Divinsky, “Mining student-generated textual data in MOOCs and quantifying their effects on student performance and learning outcomes,” in 2014 ASEE Annual Conference & Exposition, 2014, pp. 24–907.

J. Bruner, “Learning how to do things with words,” in Psycholinguistic Research (PLE: Psycholinguistics), Psychology Press, 2013, pp. 279–298. DOI:

M. Kloft, F. Stiehler, Z. Zheng, and N. Pinkwart, “Predicting MOOC dropout over weeks using machine learning methods,” in Proceedings of the EMNLP 2014 workshop on analysis of large scale social interaction in MOOCs, 2014, pp. 60–65. DOI:

R. Umer, T. Susnjak, A. Mathrani, and S. Suriadi, “Prediction of students’ dropout in MOOC environment,” Int. J. Knowl. Eng., vol. 3, no. 2, pp. 43–47, 2017. DOI:

Y. Liu, Z. Ren, J. Li, and J. Li, “Design of Informatization College and University Teaching Management System Based on Improved Decision Tree Algorithm,” Wirel. Commun. Mob. Comput., vol. 2022, 2022. DOI:

J. Liang, J. Yang, Y. Wu, C. Li, and L. Zheng, “Big data application in education: dropout prediction in edx MOOCs,” in 2016 IEEE second international conference on multimedia big data (BigMM), 2016, pp. 440–443. DOI:

G. Balakrishnan and D. Coetzee, “Predicting student retention in massive open online courses using hidden markov models,” Electr. Eng. Comput. Sci. Univ. Calif. Berkeley, vol. 53, pp. 57–58, 2013.

M. Fei and D.-Y. Yeung, “Temporal models for predicting student dropout in massive open online courses,” in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015, pp. 256–263. DOI:

S. Crossley, L. Paquette, M. Dascalu, D. S. McNamara, and R. S. Baker, “Combining click-stream data with NLP tools to better understand MOOC completion,” in Proceedings of the sixth international conference on learning analytics & knowledge, 2016, pp. 6–14. DOI:

W. Li, M. Gao, H. Li, Q. Xiong, J. Wen, and Z. Wu, “Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning,” in 2016 international joint conference on neural networks (IJCNN), 2016, pp. 3130–3137. DOI:

J. Bughin, M. Chui, M. Harrysson, and S. Lijek, “Advanced social technologies and the future of collaboration,” McKinsey Glob. Inst., 2017.

Z. R. Alashhab, M. Anbar, M. M. Singh, Y.-B. Leau, Z. A. Al-Sai, and S. A. Alhayja’a, “Impact of coronavirus pandemic crisis on technologies and cloud computing applications,” J. Electron. Sci. Technol., vol. 19, no. 1, p. 100059, 2021. DOI:

A. Engelbrecht, J. P. Gerlach, A. Benlian, and P. Buxmann, “How employees gain meta-knowledge using enterprise social networks: A validation and extension of communication visibility theory,” J. Strateg. Inf. Syst., vol. 28, no. 3, pp. 292–309, 2019. DOI:

P. M. Leonardi, “Social media, knowledge sharing, and innovation: Toward a theory of communication visibility,” Inf. Syst. Res., vol. 25, no. 4, pp. 796–816, 2014. DOI:

P. M. Leonardi, “Ambient awareness and knowledge acquisition,” MIS Q., vol. 39, no. 4, pp. 747–762, 2015. DOI:

J. Lee, H. Zo, and H. Lee, “Smart learning adoption in employees and HRD managers,” Br. J. Educ. Technol., vol. 45, no. 6, pp. 1082–1096, 2014. DOI:

M. Macià and I. García, “Informal online communities and networks as a source of teacher professional development: A review,” Teach. Teach. Educ., vol. 55, pp. 291–307, 2016. DOI:




How to Cite

Ibrar M, Karim S, Yin S, Li H, Laghari AA. Impact of Covid-19 epidemic on online learning and educational resources in China. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Aug. 21 [cited 2024 Jun. 25];9. Available from: