The Impact of Technology Integration in Learning on Increasing Student Engagement
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Integration, Student, Technology
Abstract
Background:The Impact of Technology Integration in Learning on Increasing Student Engagement refers to how this technology integration can be used well in learning. With the integration of technology in learning, it will have a direct impact on students, both student involvement in using technology, as well as the impact on student activities and creativity.
Research purposes:This research was conducted with the aim of finding out how much impact technology integration has on increasing student engagement. Apart from that, it also aims as an explanation of how important technology is today in the learning process.
Method:The method used in this research is a quantitative method.This method is a way of collecting numerical data that can be tested. Data was collected through distributing questionnaires addressed to students. Furthermore, the data that has been collected from the results of distributing the questionnaire will be accessible in Excel format which can then be processed using SPSS.
Results:From the research results, it can be stated that the impact of technology integration in learning can indeed have an influence on increasing student engagement. Because basically, today's students are more interested in using technology. However, as a teacher you also need to supervise your students in learning when using this technology. This aims to ensure that students actually use technology to learn.
Conclusion:From this research, it can be concluded that the impact of technology integration in learning has a very big influence on student engagement. With technology, students can be creative in how they learn, increase students' knowledge in using technology, make students more enthusiastic about learning, and make students less likely to get bored while studying.
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References
Allam, Z., & Jones, D.S. (2020). On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management. Healthcare, 8(1), 46.https://doi.org/10.3390/healthcare8010046
Breijyeh, Z., Jubeh, B., & Karaman, R. (2020). Resistance of Gram-Negative Bacteria to Current Antibacterial Agents and Approaches to Resolve It. Molecules, 25(6), 1340.https://doi.org/10.3390/molecules25061340
Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249–259.https://doi.org/10.1016/j.neunet.2018.07.011
Caena, F., & Redecker, C. (2019). Aligning teacher competency frameworks to 21st century challenges: The case for the European Digital Competence Framework for Educators (Digcompedu). European Journal of Education, 54(3), 356–369.https://doi.org/10.1111/ejed.12345
Casaló, L.V., Flavián, C., & Ibáñez-Sánchez, S. (2021). Be creative, my friend! Engaging users on Instagram by promoting positive emotions. Journal of Business Research, 130, 416–425.https://doi.org/10.1016/j.jbusres.2020.02.014
Chan, D.C. (2020). Mitochondrial Dynamics and Its Involvement in Disease. Annual Review of Pathology: Mechanisms of Disease, 15(1), 235–259.https://doi.org/10.1146/annurev-pathmechdis-012419-032711
Chinazzi, M., Davis, J.T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore Y Piontti, A., Mu, K., Rossi, L., Sun, K. , Viboud, C., Xiong, X., Yu, H., Halloran, M.E., Longini, I.M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395–400.https://doi.org/10.1126/science.aba9757
Demery, A.-J.C., Burns, K.J., & Mason, N.A. (2021). Bill size, bill shape, and body size constrain bird song evolution on a macroevolutionary scale. Ornithology, 138(2), ukab011.https://doi.org/10.1093/ornithology/ukab011
Duval, D., Palmer, J.C., Tudge, I., Pearce-Smith, N., O'Connell, E., Bennett, A., & Clark, R. (2022). Long distance airborne transmission of SARS-CoV-2: Rapid systematic review. BMJ, e068743.https://doi.org/10.1136/bmj-2021-068743
Galperin, MY, Wolf, YI, Makarova, KS, Vera Alvarez, R., Landsman, D., & Koonin, EV (2021). COG database update: Focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Research, 49(D1), D274–D281.https://doi.org/10.1093/nar/gkaa1018
Gao, M., Yi, J., Zhu, J., Minikes, A.M., Monian, P., Thompson, C.B., & Jiang, X. (2019). Role of Mitochondria in Ferroptosis. Molecular Cell, 73(2), 354-363.e3.https://doi.org/10.1016/j.molcel.2018.10.042
Garcia-Alonso, L., Holland, C.H., Ibrahim, M.M., Turei, D., & Saez-Rodriguez, J. (2019). Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research, 29(8), 1363–1375.https://doi.org/10.1101/gr.240663.118
Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1), 1502242.https://doi.org/10.1080/23311916.2018.1502242
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027.https://doi.org/10.1016/j.rmal.2022.100027
Han, X., Xue, J., Peng, S., & Zhang, H. (2019). An interesting oxidation phenomenon of Cr coatings on Zry-4 substrates in high temperature steam environment. Corrosion Science, 156, 117–124.https://doi.org/10.1016/j.corsci.2019.05.017
Kellogg, K.C., Valentine, M.A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410.https://doi.org/10.5465/annals.2018.0174
Kraft, M. A., Blazar, D., & Hogan, D. (2018). The Effect of Teacher Coaching on Instruction and Achievement: A Meta-Analysis of the Causal Evidence. Review of Educational Research, 88(4), 547–588.https://doi.org/10.3102/0034654318759268
Krantz, P., Kjaergaard, M., Yan, F., Orlando, T.P., Gustavsson, S., & Oliver, W.D. (2019). A quantum engineer's guide to superconducting qubits. Applied Physics Reviews, 6(2), 021318. https://doi.org/10.1063/1.5089550
Li, H., Ota, K., & Dong, M. (2018). Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing. IEEE Network, 32(1), 96–101.https://doi.org/10.1109/MNET.2018.1700202
Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mold algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055
Pan, A., Liu, L., Wang, C., Guo, H., Hao, X., Wang, Q., Huang, J., He, N., Yu, H., Lin, Wei, S., & Wu, T. (2020). Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA, 323(19), 1915.https://doi.org/10.1001/jama.2020.6130
Ray, K.K., Molemans, B., Schoonen, W.M., Giovas, P., Bray, S., Kiru, G., Murphy, J., Banach, M., De Servi, S., Gaita, D., Gouni -Berthold, I., Hovingh, GK, Jozwiak, JJ, Jukema, JW, Kiss, RG, Kownator, S., Iversen, HK, Maher, V., Masana, L., … the DA VINCI study. (2021). EU-Wi de Cross-Section al Obser v at ion al Study of Lipid-Modifying Therapy Use in Se c ondary and Pr i mary Care: The DA VINCI study. European Journal of Preventive Cardiology, 28(11), 1279–1289.https://doi.org/10.1093/eurjpc/zwaa047
Richardson, S., Hirsch, JS, Narasimhan, M., Crawford, JM, McGinn, T., Davidson, KW, and the Northwell COVID-19 Research Consortium, Barnaby, DP, Becker, LB, Chelico, JD, Cohen, SL, Cookingham, J., Coppa, K., Diefenbach, MA, Dominello, AJ, Duer-Hefele, J., Falzon, L., Gitlin, J., Hajizadeh, N., … Zanos, TP (2020). Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA, 323(20), 2052.https://doi.org/10.1001/jama.2020.6775
Sakhavi, S., Guan, C., & Yan, S. (2018). Learning Temporal Information for Brain-Computer Interfaces Using Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5619–5629.https://doi.org/10.1109/TNNLS.2018.2789927
Sarwar, M.S., Niazi, MBK, Jahan, Z., Ahmad, T., & Hussain, A. (2018). Preparation and characterization of PVA/nanocellulose/Ag nanocomposite films for antimicrobial food packaging. Carbohydrate Polymers, 184, 453–464.https://doi.org/10.1016/j.carbpol.2017.12.068
Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019). Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning. IEEE Transactions on Industrial Informatics, 15(4), 2446–2455.https://doi.org/10.1109/TII.2018.2864759
Succi, C., & Canovi, M. (2020). Soft skills to enhance graduate employability: Comparing students and employers' perceptions. Studies in Higher Education, 45(9), 1834–1847.https://doi.org/10.1080/03075079.2019.1585420
Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223.https://doi.org/10.1016/j.comnet.2019.01.024
Théry, C., Witwer, KW, Aikawa, E., Alcaraz, MJ, Anderson, JD, Andriantsitohaina, R., Antoniou, A., Arab, T., Archer, F., Atkin?Smith, GK, Ayre, D.C., Bach, J., Bachurski, D., Baharvand, H., Balaj, L., Baldacchino, S., Bauer, N.N., Baxter, A.A., Bebawy, M., … Zuba?Surma, E.K. (2018). Minimal information for studies of extracellular vesicles 2018 (MISEV2018): A position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. Journal of Extracellular Vesicles, 7(1), 1535750.https://doi.org/10.1080/20013078.2018.1535750
Tomasello, M. (2018). How children come to understand false beliefs: A shared intentionality account. Proceedings of the National Academy of Sciences, 115(34), 8491–8498.https://doi.org/10.1073/pnas.1804761115
Toropova, A., Myrberg, E., & Johansson, S. (2021). Teacher job satisfaction: The importance of school working conditions and teacher characteristics. Educational Review, 73(1), 71–97.https://doi.org/10.1080/00131911.2019.1705247
Vu, D., Groenewald, M., De Vries, M., Gehrmann, T., Stielow, B., Eberhardt, U., Al-Hatmi, A., Groenewald, J. Z., Cardinali, G., Houbraken, J ., Boekhout, T., Crous, P. W., Robert, V., & Verkley, G. J. M. (2019). Large-scale generation and analysis of filamentous fungal DNA barcodes increases coverage for kingdom fungi and reveals thresholds for fungal species and higher taxon delimitation. Studies in Mycology, 92(1), 135–154.https://doi.org/10.1016/j.simyco.2018.05.001
Wessel, P., Luis, J.F., Uieda, L., Scharroo, R., Wobbe, F., Smith, WHF, & Tian, D. (2019). The Generic Mapping Tools Version 6. Geochemistry, Geophysics, Geosystems, 20(11), 5556–5564.https://doi.org/10.1029/2019GC008515
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concepts and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.https://doi.org/10.1145/3298981
Yao, X., Ye, F., Zhang, M., Cui, C., Huang, B., Niu, P., Liu, Zhan, S., Lu, R., Li, H., Tan, W., & Liu, D. (2020). In Vitro Antiviral Activity and Projection of Optimized Dosing Design of Hydroxychloroquine for the Treatment of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Clinical Infectious Diseases, 71(15), 732–739.https://doi.org/10.1093/cid/ciaa237
Yun, E.-T., Lee, J. H., Kim, J., Park, H.-D., & Lee, J. (2018). Identifying the Nonradical Mechanism in the Peroxymonosulfate Activation Process: Singlet Oxygenation Versus Mediated Electron Transfer. Environmental Science & Technology, 52(12), 7032–7042.https://doi.org/10.1021/acs.est.8b00959
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