Mobile Learning for COVID-19 Prevention
DOI:
https://doi.org/10.4108/eetel.4410Keywords:
Mobile Learning, COVID-19 Prevention, Public Health, Personalized Learning MaterialsAbstract
In recent years, due to the explosion of COVID-19, people's expectation for accessing personalized learning resources anytime and anywhere has become stronger. The features of m-learning such as accessibility and personalization greatly satisfy people's needs and are therefore widely used. In this paper, a study was conducted to investigate and analyze how m-learning can help prevent COVID-19. The study shows that m-learning can help disseminate outbreak-related messages and provide people with personalized knowledge, so that it can enhance public health and community safety. While there are still many challenges, m-learning remains a valuable tool for preventing and mitigating the spread of COVID-19 globally, and provides a solid reference for deepening m-learning development in the future.
References
M. Sarrab, "Mobile Learning (M-Learning) and Educational Environments," International Journal of Distributed and Parallel systems, vol. 3, pp. 31-38, 2012.
V. Matzavela and E. Alepis, "M-learning in the COVID-19 era: physical vs digital class," Education and Information Technologies, vol. 26, pp. 7183-7203, 2021.
S. Aleema, A. Q. Ijaz, M. S. Donna, and Z. Memoona, "Effect of m-learning on students’ academic performance mediated by facilitation discourse and flexibility," Knowledge Management & E-Learning: An International Journal, pp. 158-200, 2019.
C. Martha and T. Mgutshini, "Using Mobile Handheld Devices as Tools of Learning and Teaching for Student EHPS: A Blessing or a Curse?," International Journal of Humanities, Arts and Social Sciences, vol. 1, 2015.
M. G. Domingo and A. B. Garganté, "Exploring the use of educational technology in primary education: Teachers' perception of mobile technology learning impacts and applications' use in the classroom," Computers in Human Behavior, vol. 56, pp. 21-28, 2016.
M. Wang, R. Shen, D. Novak, and X. Pan, "The impact of mobile learning on students' learning behaviours and performance: Report from a large blended classroom," British Journal of Educational Technology, vol. 40, pp. 673-695, 2009.
S. Schuck, M. Kearney, and K. Burden, "Exploring mobile learning in the Third Space," Technology, Pedagogy and Education, vol. 26, pp. 121-137, 2016.
P. Nedungadi and R. Raman, "A new approach to personalization: integrating e-learning and m-learning," Educational Technology Research and Development, vol. 60, pp. 659-678, 2012.
K. R. Premlatha and T. V. Geetha, "Learning content design and learner adaptation for adaptive e-learning environment: a survey," Artificial Intelligence Review, vol. 44, pp. 443-465, 2015.
C. Dichev, D. Dicheva, G. Angelova, and G. Agre, "From Gamification to Gameful Design and Gameful Experience in Learning," Cybernetics and Information Technologies, vol. 14, pp. 80-100, 2015.
X. Tian, S. Tang, H. Zhu, and D. Xia, "Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom," Concurrency and Computation: Practice and Experience, vol. 34, 2022.
J. Borup, R. E. West, and C. R. Graham, "Improving online social presence through asynchronous video," The Internet and Higher Education, vol. 15, pp. 195-203, 2012.
E. Wenger, "Communities of Practice and Social Learning Systems," Organization, pp. 225-246, 2000.
K.-Y. Tang, C.-C. Chen, G.-J. Hwang, and Y.-F. Tu, "Did library learners benefit from m-learning strategies? Research-based evidence from a co-citation network analysis of the literature," Educational technology research and development, vol. 70, pp. 1719-1753, 2022.
M. O. M. El-Hussein and J. C. Cronje, "Defining Mobile Learning in the Higher Education Landscape," Journal of Educational Technology & Society, 2010.
N. Balacheff, S. Ludvigsen, T. De Jong, A. Lazonder, and S. Barnes, Technology-Enhanced Learning, 2009.
O. R. E. Pereira and J. J. P. C. Rodrigues, "Survey and analysis of current mobile learning applications and technologies," ACM Computing Surveys, vol. 46, pp. 1-35, 2013.
O. Ozan and G. T. Yamamoto, "Mobile learning technologies and educational applications," 2015.
H. K. Flora, X. Wang, and S. V. Chande, "An Investigation on the Characteristics of Mobile Applications: A Survey Study," International Journal of Information Technology and Computer Science, vol. 6, pp. 21-27, 2014.
C. R. Heil, J. S. Wu, J. J. Lee, and T. Schmidt, "A Review of Mobile Language Learning Applications: Trends, Challenges, and Opportunities," The EuroCALL Review, vol. 24, 2016.
M. Á. Conde, F. J. García-Peñalvo, M. J. Rodríguez-Conde, M. Alier, M. J. Casany, and J. Piguillem, "An evolving Learning Management System for new educational environments using 2.0 tools," Interactive Learning Environments, vol. 22, pp. 188-204, 2012.
F. Demir, C. Bruce-Kotey, and F. Alenezi, "User Experience Matters: Does One size Fit all? Evaluation of Learning Management Systems," Technology, Knowledge and Learning, vol. 27, pp. 49-67, 2021.
A. Priyatno, "Promoting learner autonomy through schoology m-learning platform in an EAP class at an Indonesian university," Teaching English with Technology, 2017.
D. Churchill, J. Lu, T. K. F. Chiu, and B. Fox, Mobile Learning Design: Theories and Application, 2016.
S. C.-Y. Yuen, G. Yaoyuneyong, and E. Johnson, "Augmented Reality An Overview and Five Directions," Journal of Educational Technology Development and Exchange, vol. 4, 2011.
K. McKnight, K. O'Malley, R. Ruzic, M. K. Horsley, J. J. Franey, and K. Bassett, "Teaching in a Digital Age: How Educators Use Technology to Improve Student Learning," Journal of Research on Technology in Education, vol. 48, pp. 194-211, 2016.
J. C. Miller, "Spread of infectious disease through clustered populations," Journal of The Royal Society Interface, vol. 6, pp. 1121-1134, 2009.
L. Yang, "EDNC: ensemble deep neural network for Covid-19 recognition," Tomography, vol. 8, pp. 869-890, 2022.
X. Qian, R. Ren, Y. Wang, Y. Guo, J. Fang, Z.-D. Wu, et al., "Fighting against the common enemy of COVID-19: a practice of building a community with a shared future for mankind," Infectious Diseases of Poverty, vol. 9, 2020.
J. R. Mascola, B. J. Mathieson, P. M. Zack, M. C. Walker, S. B. Halstead, and D. S. Burke, "Summary Report: Workshop on the Potential Risks of Antibody-Dependent Enhancement in Human HIV Vaccine Trials," AIDS Research and Human Retroviruses, vol. 9, pp. 1175-1184, 1993.
S.-H. Wang, "DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy," ACM Transactions on Management Information Systems, vol. 13, Article ID: 2, 2021.
M. D. Hillier, "Using effective hand hygiene practice to prevent and control infection," Nursing Standard, vol. 35, pp. 45-50, 2020.
M. Jayaweera, H. Perera, B. Gunawardana, and J. Manatunge, "Transmission of COVID-19 virus by droplets and aerosols: A critical review on the unresolved dichotomy," Environmental Research, vol. 188, 2020.
I. Purnamasari and A. E. Raharyani, "Preventive Health Behaviors of Community During Covid-19 Pandemic: A Descriptive Study," Indonesian Journal of Global Health Research, vol. 2, 2019.
O. A. Olajuyin, T. G. Olajide, O. F. Ogunboyo, A. B. Olajuyin, A. A. Olajuyin, and S. A. Deji, "Knowledge and Practice of Sneeze and Cough Etiquet," Journal of Tropical Diseases, 2019.
"COVID-19 Exposure Prevention Plan."
R. K. Webster, S. K. Brooks, L. E. Smith, L. Woodland, S. Wessely, and G. J. Rubin, "How to improve adherence with quarantine: rapid review of the evidence," Public Health, vol. 182, pp. 163-169, 2020.
M. A. Honein, A. Christie, D. A. Rose, J. T. Brooks, D. Meaney-Delman, A. Cohn, et al., "Summary of Guidance for Public Health Strategies to Address High Levels of
Community Transmission of SARS-CoV-2 and Related Deaths, December 2020 Margaret A. Honein, PhD1; Athalia Christie, MIA1; Dale A. Rose, PhD1; John T. Brook," MMWR. Morbidity and Mortality Weekly Report, vol. 69, pp. 1860-1867, 2020.
N. Harring, S. C. Jagers, and Å. Löfgren, "COVID-19: Large-scale collective action, government intervention, and the importance of trust," World Development, vol. 138, 2021.
R. Si, Y. Yao, X. Zhang, Q. Lu, and N. Aziz, "Investigating the Links Between Vaccination Against COVID-19 and Public Attitudes Toward Protective Countermeasures: Implications for Public Health," Frontiers in Public Health, vol. 9, 2021.
Y. D. Zhang, "Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis," CMC-Computers Materials & Continua, vol. 69, pp. 3145-3162, 2021.
X. Zhang, "Diagnosis of COVID-19 pneumonia via a novel deep learning architecture," Journal of Computer Science and Technology, vol. 37, pp. 330-343, 2022.
Y.-P. Yuan, G. Wei-Han Tan, K.-B. Ooi, and W.-L. Lim, "Can COVID-19 pandemic influence experience response in mobile learning?," Telematics and Informatics, vol. 64, 2021.
L. C. Ming, N. Untong, N. A. Aliudin, N. Osili, N. Kifli, C. S. Tan, et al., "Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review," JMIR mHealth and uHealth, vol. 8, 2020.
J. Joko, A. B. Santoso, S. Muslim, and R. Harimurti, "Effectiveness_of_Mobile_Learning_Implementation_in_Increasing_Student_Competence_and_Preventing_the_Spread_and_Impact_of_COVID-19," the third International Conference on Vocational Education and Electrical Engineering (ICVEE), 2020.
O. E. Odeyemi, S. Y. Williams, S. O. Adeyemi, J. O. Eyitayo, O. E. Dada, M. A. Adesina, et al., "Mobile Health Technology (Mhealth) in Combating COVID-19 Pandemic: Use, Challenges and Recommendations," European Journal of Medical and Educational Technologies, vol. 13, 2020.
Bokolo and A. Jnr, "Application of telemedicine and eHealth technology for clinical services in response to COVID‑19 pandemic," Health and Technology, vol. 11, pp. 359-366, 2021.
H. S. Kang and M. Exworthy, "Wearing the Future—Wearables to Empower Users to Take Greater Responsibility for Their Health and Care: Scoping Review," JMIR mHealth and uHealth, vol. 10, 2022.
M. Sharples, "Mobile learning: research, practice and challenges," Distance Education in China, 2013.
Bacolod and D. Blacer, "Mobile Learning as a Solution for Restricted Learning during the COVID-19 Pandemic," Journal of Digital Educational Technology, vol. 2, 2022.
F. Ferri, P. Grifoni, and T. Guzzo, "Online Learning and Emergency Remote Teaching: Opportunities and Challenges in Emergency Situations," Societies, vol. 10, 2020.
E. Vaportzis, M. Giatsi Clausen, and A. J. Gow, "Older Adults Perceptions of Technology and Barriers to Interacting with Tablet Computers: A Focus Group Study," Frontiers in Psychology, vol. 8, 2017.
W. Ng, "Can we teach digital natives digital literacy?," Computers & Education, vol. 59, pp. 1065-1078, 2012.
P. David, J.-H. Kim, J. S. Brickman, W. Ran, and C. M. Curtis, "Mobile phone distraction while studying," New Media & Society, vol. 17, pp. 1661-1679, 2014.
G. Kambourakis, "Security and Privacy in m-Learning and Beyond Cha," Science and Technology, 2013.
A.-N. Moldovan, S. Weibelzahl, and C. H. Muntean, "Energy-Aware Mobile Learning:Opportunities and Challenges," IEEE Communications Surveys & Tutorials, vol. 16, pp. 234-265, 2014.
Y.-D. Zhang, "ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module," Computer Modeling in Engineering & Sciences, vol. 127, pp. 1037-1058, 2021.
S.-H. Wang, K. M. Attique, and G. Vishnuvarthanan, "Deep rank-based average pooling network for COVID-19 recognition," Computers, Materials, & Continua, vol. 70, pp. 2797-2813, 2022.
J. Katusiime, W. Tumuhimbise, G. Rwambuka Mugyenyi, P. Kobutungi, A. Mugaba, R. Zender, et al., "The role of mobile health technologies in promoting COVID-19 prevention: A narrative review of intervention effectiveness and adoption," Digital Health, vol. 8, 2022.
S.-H. Wang, "Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization," International Journal of Computational Intelligence Systems, vol. 13, pp. 1332-1344, 2020.
Z. Zhang and X. Zhang, "MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray," Pattern Recognition Letters, vol. 150, pp. 8-16, 2021.
M. A. Almaiah, A. Al-Khasawneh, A. Althunibat, and O. Almomani, "Exploring the Main Determinants of Mobile Learning Application Usage During Covid-19 Pandemic in Jordanian Universities," in Emerging Technologies During the Era of COVID-19 Pandemic, ed, 2021, pp. 275-290.
S.-H. Wang and S. Fernandes, "AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM," IEEE Sensors Journal, vol. 22, pp. 17431 - 17438, 2022.
Y. D. Zhang and S. Satapathy, "A seven-layer convolutional neural network for chest CT-based COVID-19 diagnosis using stochastic pooling," IEEE Sensors Journal, vol. 22, pp. 17573 - 17582, 2022.
J. P. Allegrante, M. E. Auld, and S. Natarajan, "Preventing COVID-19 and Its Sequela: “There Is No Magic Bullet... It's Just Behaviors”," American Journal of Preventive Medicine, vol. 59, pp. 288-292, 2020.
S. Attfield, G. Kazai, and M. Lalmas, "Towards a science of user engagement (Position Paper)," 2011.
H. Kondylakis, D. G. Katehakis, A. Kouroubali, F. Logothetidis, A. Triantafyllidis, I. Kalamaras, et al., "COVID-19 Mobile Apps: A Systematic Review of the Literature," Journal of Medical Internet Research, vol. 22, 2020.
D. Baretta, M. A. Amrein, C. Bäder, G. G. Ruschetti, C. Rüttimann, M. Del Rio Carral, et al., "Promoting Hand Hygiene During the COVID-19 Pandemic: Parallel Randomized Trial for the Optimization of the Soapp App," JMIR mHealth and uHealth, vol. 11, 2023.
A.-T. Fadi, D. Ajantha, and N. Anand, Emerging Technologies for Battling Covid-19, 2021.
P. A. Parikh, B. V. Shah, A. G. Phatak, A. C. Vadnerkar, S. Uttekar, N. Thacker, et al., "COVID-19 Pandemic: Knowledge and Perceptions of the Public and Healthcare Professionals," Cureus, 2020.
C. O'Malley, G. N, Vavoula, J. Glew, J. Taylor, M. Sharples, et al., "Guidelines for learningteachingtutoring in a mob," Computer Science, Education, 2003.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 EAI Endorsed Transactions on e-Learning
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
