The Role of Social Media in Supporting Open and Distance Learning
DOI:
https://doi.org/10.55849/attasyrih.v10i1.213Keywords:
Distance, Open Learning, Social MediaAbstract
Today's education has developed very rapidly, also supported by technological developments which are increasing day by day. It is undeniable that people are competing with each other to gain knowledge wherever and whenever. Advances in technology are really very helpful in the educational process. With the development of technology, of course, this is not too bothered, because technology has solved this problem. Through technology, various social media appear that can support the learning process, both directly and distance and open learning. The purpose of this study is to determine the role of social media in supporting distance and open learning. The method used in this research is a quantitative research method with a research design in the form of a survey. The data in this study were obtained by filling out a questionnaire on the Google form link. The results of this study are that the role of social media in supporting distance and open learning plays a very important role. Because social media has a variety of applications that support distance and open learning. The conclusion from this study is that social media plays a very important role in supporting distance and open learning. Through social media, students can carry out online learning without having to be in the same place as the teacher so that they can study anytime and anywhere, because this is a hallmark of distance and open learning. The limitation in this study is that the researcher only conducted research at the school level. The researcher also hopes that further researchers will expand research to a higher level and the researcher also hopes that in the future other researchers will emerge who can develop this research and focus on one part of social media.
References
Ahmed, I., Ahmad, M., Rodrigues, J. J. P. C., Jeon, G., & Din, S. (2021). A deep learning-based social distance monitoring framework for COVID-19. Sustainable Cities and Society, 65, 102571. https://doi.org/10.1016/j.scs.2020.102571
Al Maarif, M. F., Afifah, R. A. N., Choirunnisa, A., Jannah, A. M., Zanuar, M. Y., Saddhono, K., & Yingxiang, S. (2023). Integrating and Strengthening National Vision in the Community as an Effort to Prevent Radicalization and Foster Love for the Motherland. Pengabdian: Jurnal Abdimas, 1(1), 20–29. https://doi.org/10.55849/abdimas.v1i1.151
Almarzooq, Z. I., Lopes, M., & Kochar, A. (2020). Virtual Learning During the COVID-19 Pandemic. Journal of the American College of Cardiology, 75(20), 2635–2638. https://doi.org/10.1016/j.jacc.2020.04.015
Appio, F. P., Lima, M., & Paroutis, S. (2019). Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technological Forecasting and Social Change, 142, 1–14. https://doi.org/10.1016/j.techfore.2018.12.018
Baugh, L. R. (2001). Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Research, 29(5), 29e–229. https://doi.org/10.1093/nar/29.5.e29
Cakir, F., He, K., Xia, X., Kulis, B., & Sclaroff, S. (2019). Deep Metric Learning to Rank. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1861–1870. https://doi.org/10.1109/CVPR.2019.00196
Chan, A. K. M., Nickson, C. P., Rudolph, J. W., Lee, A., & Joynt, G. M. (2020). Social media for rapid knowledge dissemination: Early experience from the COVID ‐19 pandemic. Anaesthesia, 75(12), 1579–1582. https://doi.org/10.1111/anae.15057
Farooq, A., Laato, S., & Islam, A. K. M. N. (2020). Impact of Online Information on Self-Isolation Intention During the COVID-19 Pandemic: Cross-Sectional Study. Journal of Medical Internet Research, 22(5), e19128. https://doi.org/10.2196/19128
Fawaz, M., & Samaha, A. (2021). E‐learning: Depression, anxiety, and stress symptomatology among Lebanese university students during COVID‐19 quarantine. Nursing Forum, 56(1), 52–57. https://doi.org/10.1111/nuf.12521
Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., Pervaiz, H., Sehgal, B., Kaila, S. S., Misra, S., Aslanpour, M. S., Mehta, H., Stankovski, V., & Garraghan, P. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118. https://doi.org/10.1016/j.iot.2019.100118
Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., Haraldsdóttir, H. S., Wachowiak, J., Keating, S. M., Vlasov, V., Magnusdóttir, S., Ng, C. Y., Preciat, G., Žagare, A., Chan, S. H. J., Aurich, M. K., Clancy, C. M., Modamio, J., Sauls, J. T., … Fleming, R. M. T. (2019). Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nature Protocols, 14(3), 639–702. https://doi.org/10.1038/s41596-018-0098-2
Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns. Journal of Marketing, 83(5), 78–96. https://doi.org/10.1177/0022242919854374
Jin, W., Zhang, H., Liu, S., & Zhang, H. (2019). Technological innovation, environmental regulation, and green total factor efficiency of industrial water resources. Journal of Cleaner Production, 211, 61–69. https://doi.org/10.1016/j.jclepro.2018.11.172
Kraus, S., Palmer, C., Kailer, N., Kallinger, F. L., & Spitzer, J. (2018). Digital entrepreneurship: A research agenda on new business models for the twenty-first century. International Journal of Entrepreneurial Behavior & Research, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJEBR-06-2018-0425
Li, S., Wang, Y., Xue, J., Zhao, N., & Zhu, T. (2020). The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. International Journal of Environmental Research and Public Health, 17(6), 2032. https://doi.org/10.3390/ijerph17062032
Liang, C., Amelung, W., Lehmann, J., & Kästner, M. (2019). Quantitative assessment of microbial necromass contribution to soil organic matter. Global Change Biology, 25(11), 3578–3590. https://doi.org/10.1111/gcb.14781
Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. JMIR Public Health and Surveillance, 6(2), e19447. https://doi.org/10.2196/19447
Maryati, A., Ameer, A., & Egie, J. (2022). Utilization of Animiz Animation Application in Arabic Class 2 Madrasah Tsanawiyah Lessons at Diniyyah Pasia Modern Islamic Boarding School. Scientechno: Journal of Science and Technology, 1(1), 25–45. https://doi.org/10.55849/scientechno.v1i1.3
Mishra, L., Gupta, T., & Shree, A. (2020). Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. International Journal of Educational Research Open, 1, 100012. https://doi.org/10.1016/j.ijedro.2020.100012
Moghanibashi-Mansourieh, A. (2020). Assessing the anxiety level of Iranian general population during COVID-19 outbreak. Asian Journal of Psychiatry, 51, 102076. https://doi.org/10.1016/j.ajp.2020.102076
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013
Ramdani, N. S., Nugraha, H., & Hadiapurwa, A. (2021). POTENSI PEMANFAATAN MEDIA SOSIAL TIKTOK SEBAGAI MEDIA PEMBELAJARAN DALAM PEMBELAJARAN DARING. Akademika, 10(02), 425–436. https://doi.org/10.34005/akademika.v10i02.1406
Ranal, A., Husniyah, H., Fienti, Y., Putri, S. A., Lenin, F., Musrika, M., Diana, D., & Xin, D. (2023). Physical Activity Training Education for the Elderly at Nursing Homes. Pengabdian: Jurnal Abdimas, 1(1), 14–19. https://doi.org/10.55849/abdimas.v1i1.143
Rissman, J., Bataille, C., Masanet, E., Aden, N., Morrow, W. R., Zhou, N., Elliott, N., Dell, R., Heeren, N., Huckestein, B., Cresko, J., Miller, S. A., Roy, J., Fennell, P., Cremmins, B., Koch Blank, T., Hone, D., Williams, E. D., De La Rue Du Can, S., … Helseth, J. (2020). Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070. Applied Energy, 266, 114848. https://doi.org/10.1016/j.apenergy.2020.114848
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shafique, K., Khawaja, B. A., Sabir, F., Qazi, S., & Mustaqim, M. (2020). Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios. IEEE Access, 8, 23022–23040. https://doi.org/10.1109/ACCESS.2020.2970118
Shareef, M. A., Mukerji, B., Dwivedi, Y. K., Rana, N. P., & Islam, R. (2019). Social media marketing: Comparative effect of advertisement sources. Journal of Retailing and Consumer Services, 46, 58–69. https://doi.org/10.1016/j.jretconser.2017.11.001
Soleimani, M., Kalhor, A., & Mirzadeh, H. (2020). Transformation-induced plasticity (TRIP) in advanced steels: A review. Materials Science and Engineering: A, 795, 140023. https://doi.org/10.1016/j.msea.2020.140023
Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020). Deep learning on image denoising: An overview. Neural Networks, 131, 251–275. https://doi.org/10.1016/j.neunet.2020.07.025
Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., & Butt, Z. A. (2021). What social media told us in the time of COVID-19: A scoping review. The Lancet Digital Health, 3(3), e175–e194. https://doi.org/10.1016/S2589-7500(20)30315-0
Verdoliva, L. (2020). Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932. https://doi.org/10.1109/JSTSP.2020.3002101
Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https://doi.org/10.1016/j.addma.2020.101538
Yang, F., Yang, H., Fu, J., Lu, H., & Guo, B. (2020). Learning Texture Transformer Network for Image Super-Resolution. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5790–5799. https://doi.org/10.1109/CVPR42600.2020.00583
Zimmerman, K. O., Akinboyo, I. C., Brookhart, M. A., Boutzoukas, A. E., McGann, K. A., Smith, M. J., Maradiaga Panayotti, G., Armstrong, S. C., Bristow, H., Parker, D., Zadrozny, S., Weber, D. J., Benjamin, D. K., & FOR THE ABC SCIENCE COLLABORATIVE. (2021). Incidence and Secondary Transmission of SARS-CoV-2 Infections in Schools. Pediatrics, 147(4), e2020048090. https://doi.org/10.1542/peds.2020-048090
Downloads
Published
How to Cite
Issue
Section
License
