The Impact of Using Social Media in the Learning Process on Student Social Interaction
Abstract
Background:Social media is a digital platform that allows users to carry out social activities and communicate. Likewise in education, the impact of using social media in the learning process has various impacts. Although social media can increase student motivation and broaden horizons, social media can also make student behavior deviant.
Research purposes:This research was conducted with the aim of knowing the impact of using social media in the learning process, namely to understand how the use of social media affects students' social interactions. Does this influence have a positive or negative impact?
Method:In conducting this research, researchers used quantitative methods in carrying out the research. The data obtained by the researcher was obtained through distributing questionnaires presented by the researcher via a goggle from application. The distribution of this questionnaire is carried out by researchers online, and then the results of the distribution of this questionnaire will be processed using an SPSS application.
Results:From this research conducted, researchers can conclude the research results that the use of social media can help students in the learning process and increase knowledge. However, its use can also cause students' social behavior to become deviant, such as excessive use of social media, laziness in studying, irregular allocation of time for studying, and so on.
Conclusion:Based on the results of this research which discusses the impact of using social media in the learning process on social media interactions, social media has advantages and disadvantages in its use. However, students must be able to use social media wisely, so that deviant behavior does not occur in using social media
Full text article
References
Amalia, SNI, Octaria, YC, Maryusman, T., & Imrar, IF (2023). The Associations Between Social Media Use with Eating Behavior, Physical Activity, and Nutrition Status among Adolescents in DKI Jakarta: Relationship between Social Media Use Patterns and Eating Behavior, Physical Activity, and Nutritional Status among Adolescents in DKI Jakarta. Amerta Nutrition, 7(2SP), 193–198.https://doi.org/10.20473/amnt.v7i2SP.2023.193-198
Bahadori, S., Williams, J. M., Collard, S., & Swain, I. (2023). Can a Purposeful Walk Intervention with a Distance Goal Using an Activity Monitor Improve Individuals' Daily Activity and Function Post Total Hip Replacement Surgery. A Randomized Pilot Trial. Cyborgs and Bionic Systems, 4, 0069.https://doi.org/10.34133/cbsystems.0069
Bakker, C., Vries, S. D., & De Glopper, K. (2023). Exchange on subject pedagogy during lesson study in initial teacher education. International Journal for Lesson & Learning Studies, 12(4), 301–314.https://doi.org/10.1108/IJLLS-04-2023-0034
Basharat, S., Afzal, S., Bamhdi, A.M., Khurshid, S., & Chachoo, M. (2023). Predicting and Mitigating the Effect of Skewness on Credibility Assessment of Social Media Content Using Machine Learning: A Twitter Case Study. International Journal of Computer Theory and Engineering, 15(3), 101–110.https://doi.org/10.7763/IJCTE.2023.V15.1338
Boursier, V., Gioia, F., Musetti, A., & Schimmenti, A. (2020). Facing Loneliness and Anxiety During the COVID-19 Isolation: The Role of Excessive Social Media Use in a Sample of Italian Adults. Frontiers in Psychiatry, 11, 586222.https://doi.org/10.3389/fpsyt.2020.586222
Bruns, A. (2019). After the 'APIcalypse': Social media platforms and their fight against critical scholarly research. Information, Communication & Society, 22(11), 1544–1566.https://doi.org/10.1080/1369118X.2019.1637447
Cao, G., & Tian, Q. (2022). Social media use and its effect on university students' learning and academic performance in the UAE. Journal of Research on Technology in Education, 54(1), 18–33.https://doi.org/10.1080/15391523.2020.1801538
Ding, Z., Li, M., Yang, X., & Xiao, W. (2023). Ambidextrous organizational learning and performance: Absorptive capacity in small and medium-sized enterprises. Management Decision, 61(11), 3610–3634.https://doi.org/10.1108/MD-02-2023-0138
El Islami, RAZ, Sari, IJ, & Utari, E. (2023). Conceptualizing bioinformatics education in STEM literacy development for pre-service biology teachers. International Journal of ADVANCED AND APPLIED SCIENCES, 10(12), 193–202.https://doi.org/10.21833/ijaas.2023.12.021
Farkas, X., & Bene, M. (2021). Images, Politicians, and Social Media: Patterns and Effects of Politicians' Image-Based Political Communication Strategies on Social Media. The International Journal of Press/Politics, 26(1), 119–142.https://doi.org/10.1177/1940161220959553
Ge, Y., Qiu, J., Liu, Z., Gu, W., & Xu, L. (2020). Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash. Information Processing & Management, 57(4), 102218.https://doi.org/10.1016/j.ipm.2020.102218
Gill, M., & Gosine-Boodoo, M. (2021). A case for purposeful mentorship in research and publishing at a Caribbean academic library. The Journal of Academic Librarianship, 47(2), 102302.https://doi.org/10.1016/j.acalib.2020.102302
Grotz, F., & Lewandowsky, M. (2020). Promoting or Controlling Political Decisions? Citizen Preferences for Direct-Democratic Institutions in Germany. German Politics, 29(2), 180–200.https://doi.org/10.1080/09644008.2019.1583329
Halawani, HT, Mashraqi, AM, Badr, SK, & Alkhalaf, S. (2023). Automated sentiment analysis in social media using Harris Hawks optimization and deep learning techniques. Alexandria Engineering Journal, 80, 433–443.https://doi.org/10.1016/j.aej.2023.08.062
Kawai, N., Guo, Z., & Nakata, R. (2021). A human voice, but not a human visual image makes people perceive food to taste better and to eat more: “Social” facilitation of eating in a digital media. Appetite, 167, 105644.https://doi.org/10.1016/j.appet.2021.105644
Kelliher, C., Richardson, J., & Boiarintseva, G. (2019). All of work? All of life? Reconceptualizing work?life balance for the 21st century. Human Resource Management Journal, 29(2), 97–112.https://doi.org/10.1111/1748-8583.12215
Khalvandi, A., Tayebi, L., Kamarian, S., Saber-Samandari, S., & Song, J. (2023). Data-driven supervised machine learning to predict the compressive response of porous PVA/Gelatin hydrogels and in-vitro assessments: Employing design of experiments. International Journal of Biological Macromolecules, 253, 126906.https://doi.org/10.1016/j.ijbiomac.2023.126906
Ku, H.-H., Shang, R.-A., & Fu, Y.-F. (2021). Social learning effects of complaint handling on social media: Self-construal as a moderator. Journal of Retailing and Consumer Services, 59, 102343.https://doi.org/10.1016/j.jretconser.2020.102343
Livingston, J., Holland, E., & Fardouly, J. (2020). Exposing digital posing: The effect of social media self-disclaimer captions on women's body dissatisfaction, mood, and impressions of the user. Body Image, 32, 150–154.https://doi.org/10.1016/j.bodyim.2019.12.006
Miyata, T., Yamashita, Y., Higashi, T., Taki, K., Izumi, D., Kosumi, K., Tokunaga, R., Nakagawa, S., Okabe, H., Imai, K., Hashimoto, D., Chikamoto, A., & Baba, H. (2018). The Prognostic Impact of Controlling Nutritional Status (CONUT) in Intrahepatic Cholangiocarcinoma Following Curative Hepatectomy: A Retrospective Single Institution Study. World Journal of Surgery, 42(4), 1085–1091.https://doi.org/10.1007/s00268-017-4214-1
Morales-Castañeda, B., Zaldívar, D., Cuevas, E., Fausto, F., & Rodríguez, A. (2020). A better balance in metaheuristic algorithms: Does it exist? Swarm and Evolutionary Computation, 54, 100671.https://doi.org/10.1016/j.swevo.2020.100671
Najafi, A., Homaee, O., Golshan, M., Jasinski, M., & Leonowicz, Z. (2023). Application of Extreme Learning Machine- Autoencoder to Medium Term Electricity Price Forecasting. IEEE Transactions on Industry Applications, 59(6), 7214–7223.https://doi.org/10.1109/TIA.2023.3303866
Nam, J. M., Lee, S., & Kang, J. H. (2023). Multilevel Analysis of the Effect of Corporate Entrepreneurship and Learning Capabilities on the Attitudes of Key Personnel in Small and Medium Enterprises in Korea: The Moderating Effect of Social Capital. SAGE Open, 13(2), 215824402311755.https://doi.org/10.1177/21582440231175585
Nurpratama, MR (2020). Understanding Perception and Motivation in Sharing Information by Digital Natives in Social Media. Record and Library Journal, 6(1), 57.https://doi.org/10.20473/rlj.V6-I1.2020.57-68
Óskarsdóttir, H.G., Oddsson, G.V., Sturluson, J. Þ., & Sæmundsson, R.J. (2021). A Soft Systems Approach to Knowledge Worker Productivity: A Purposeful Activity Model for the Individual. Administrative Sciences, 11(4), 110.https://doi.org/10.3390/admsci11040110
Panigrahi, S., Sharma, H.B., & Dubey, B.K. (2020). Anaerobic co-digestion of food waste with pretreated yard waste: A comparative study of methane production, kinetic modeling and energy balance. Journal of Cleaner Production, 243, 118480.https://doi.org/10.1016/j.jclepro.2019.118480
Ramos Fonseca, M. G., & Da Ponte, J. P. (2023). An early childhood teacher experience in lesson study: The case of Sara. International Journal for Lesson & Learning Studies, 12(4), 343–354.https://doi.org/10.1108/IJLLS-05-2023-0064
Song, H., Xu, B., Luo, C., Zhang, Z., Ma, B., Jin, J., & Zhang, Q. (2019). The prognostic value of preoperative controlling nutritional status score in non-metastatic renal cell carcinoma treated with surgery: A retrospective single-institution study. Cancer Management and Research, Volume 11, 7567–7575.https://doi.org/10.2147/CMAR.S209418
Su, W., Han, X., Yu, H., Wu, Y., & Potenza, M.N. (2020). Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Computers in Human Behavior, 113, 106480.https://doi.org/10.1016/j.chb.2020.106480
Sun, Y., Ding, Z., Zhang, Z. (Justin), & Gauthier, J. (2020). The Sustainable Positive Effects of Enterprise Social Media on Employees: The Visibility and Vicarious Learning Lens. Sustainability, 12(7), 2855.https://doi.org/10.3390/su12072855
Tang, Z., Miller, A.S., Zhou, Z., & Warkentin, M. (2021). Does government social media promote users' information security behavior towards COVID-19 scams? Cultivation effects and protective motivations. Government Information Quarterly, 38(2), 101572.https://doi.org/10.1016/j.giq.2021.101572
Thorisdottir, I.E., Sigurvinsdottir, R., Asgeirsdottir, B.B., Allegrante, J.P., & Sigfusdottir, I.D. (2019). Active and Passive Social Media Use and Symptoms of Anxiety and Depressed Mood Among Icelandic Adolescents. Cyberpsychology, Behavior, and Social Networking, 22(8), 535–542.https://doi.org/10.1089/cyber.2019.0079
UIN Sunan Kalijaga Yogyakarta, Fanindy, MN, Mupida, S., & UIN Sunan Kalijaga Yogyakarta. (2021). Literacy Shift in the Millennial Generation Due to the Spread of Radicalism on Social Media. Millah, 20(2), 195–222.https://doi.org/10.20885/millah.vol20.iss2.art1
Vos, K., Peng, Z., Lee, E., & Wang, W. (2023). Aircraft fleet availability optimization: A reinforcement learning approach. The Aeronautical Journal, 127(1318), 2204–2218.https://doi.org/10.1017/aer.2023.104
Wu, B., Li, F., Zhou, L., Liu, M., & Geng, F. (2022). Are mindful people less involved in online trolling? A moderated mediation model of perceived social media fatigue and moral disengagement. Aggressive Behavior, 48(3), 309–318.https://doi.org/10.1002/ab.22013
Wu, G., & Pan, C. (2022). Audience engagement with news on Chinese social media: A discourse analysis of the People's Daily official account on WeChat. Discourse & Communication, 16(1), 129–145.https://doi.org/10.1177/17504813211026567
Yoo, W., Paek, H.-J., & Hove, T. (2020). Differential Effects of Content-Oriented Versus User-Oriented Social Media on Risk Perceptions and Behavioral Intentions. Health Communication, 35(1), 99–109.https://doi.org/10.1080/10410236.2018.1545169
Zhang, J., Cheng, L., Liu, C., Zhao, Z., & Mao, Y. (2023). Cost-aware scheduling systems for real-time workflows in cloud: An approach based on Genetic Algorithm and Deep Reinforcement Learning. Expert Systems with Applications, 234, 120972.https://doi.org/10.1016/j.eswa.2023.120972
Zhou, C., Li, K., & Lu, Y. (2021). Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness. Information Processing & Management, 58(6), 102679.https://doi.org/10.1016/j.ipm.2021.102679
Authors
Copyright (c) 2024 Xie Guilin

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.