Support Student Engagement through Technology-Based Collaborative Platforms
Downloads
Keeping students engaged in teaching and learning activities is a very difficult task for teachers and schools. Changes in school systems are continuously changing the way students interact and engage in learning, forcing educators to explore resources and strategies to better support students. Educational technology helps educators meet today's needs and is an important resource for the learning process. Increased use of technology in learning environments can support and enhance student engagement. The emergence of collaborative platforms is a form of technological interest to support student engagement. The purpose of this research is to explore whether technology with collaborative platforms can support student involvement in the learning process. Through research on supporting student engagement through this technology-based collaboration platform, researchers hope that using technology-based collaboration platforms in learning can help students in the learning process. The method used in this research is a quantitative method. Researchers conducted a survey using a Google form consisting of 15 sentences related to the research title. The researchers found that supporting student engagement through technology-based collaborative platforms had a major impact on student engagement. The presence of a collaborative learning platform makes students more enthusiastic and excited. Students feel that with cooperation in the learning process, learning becomes more differentiated and attracts students' attention. The limitations of this research are that researchers only conduct research in schools and researchers do not conduct research directly in schools but share survey links on Google forms containing statements about supporting student engagement through technology-based collaborative platforms.
Ahmed, T., Chandran, VGR, Klobas, J.E., Liñán, F., & Kokkalis, P. (2020). Entrepreneurship education programs: How learning, inspiration and resources affect intentions for new venture creation in a developing economy. The International Journal of Management Education, 18(1), 100327. https://doi.org/10.1016/j.ijme.2019.100327
Aledhari, M., Razzak, R., Parizi, R.M., & Saeed, F. (2020). Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Access, 8, 140699–140725. https://doi.org/10.1109/ACCESS.2020.3013541
Alqahtani, A.Y., & Rajkhan, A.A. (2020). E-Learning Critical Success Factors during the COVID-19 Pandemic: A Comprehensive Analysis of E-Learning Managerial Perspectives. Education Sciences, 10(9), 216. https://doi.org/10.3390/educsci10090216
Anwar, S., Bascou, N.A., Menekse, M., & Kardgar, A. (2019). A Systematic Review of Studies on Educational Robotics. Journal of Pre-College Engineering Education Research (J-PEER), 9(2). https://doi.org/10.7771/2157-9288.1223
Brunton, S.L., Noack, B.R., & Koumoutsakos, P. (2020). Machine Learning for Fluid Mechanics. Annual Review of Fluid Mechanics, 52(1), 477–508. https://doi.org/10.1146/annurev-fluid-010719-060214
Chick, RC, Clifton, GT, Peace, KM, Propper, BW, Hale, DF, Alseidi, AA, & Vreeland, TJ (2020). Using Technology to Maintain the Education of Residents During the COVID-19 Pandemic. Journal of Surgical Education, 77(4), 729–732. https://doi.org/10.1016/j.jsurg.2020.03.018
Cozzolino, A., Corbo, L., & Aversa, P. (2021). Digital platform-based ecosystems: The evolution of collaboration and competition between incumbent producers and entrant platforms. Journal of Business Research, 126, 385–400. https://doi.org/10.1016/j.jbusres.2020.12.058
Enikolopov, R., Makarin, A., & Petrova, M. (2020). Social Media and Protest Participation: Evidence From Russia. Econometrica, 88(4), 1479–1514. https://doi.org/10.3982/ECTA14281
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
Fang, C., Li, J., Zhang, M., Zhang, Y., Yang, F., Lee, J.Z., Lee, M.-H., Alvarado, J., Schroeder, M.A., Yang, Y. , Lu, B., Williams, N., Ceja, M., Yang, L., Cai, M., Gu, J., Xu, K., Wang, X., & Meng, Y.S. (2019). Quantifying inactive lithium in lithium metal batteries. Nature, 572(7770), 511–515. https://doi.org/10.1038/s41586-019-1481-z
Felszeghy, S., Pasonen-Seppänen, S., Koskela, A., Nieminen, P., Härkönen, K., Paldanius, K.M.A., Gabbouj, S., Ketola, K., Hiltunen, M., Lundin, M. , Haapaniemi, T., Sointu, E., Bauman, E. B., Gilbert, G. E., Morton, D., & Mahonen, A. (2019). Using online game-based platforms to improve student performance and engagement in histology teaching. BMC Medical Education, 19(1), 273. https://doi.org/10.1186/s12909-019-1701-0
Grani?, A., & Maranguni?, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019). Deep learning for time series classification: A review. Data Mining and Knowledge Discovery, 33(4), 917–963. https://doi.org/10.1007/s10618-019-00619-1
Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 27. https://doi.org/10.1186/s40537-019-0192-5
Kim, J. (2020). Learning and Teaching Online During Covid-19: Experiences of Student Teachers in an Early Childhood Education Practicum. International Journal of Early Childhood, 52(2), 145–158. https://doi.org/10.1007/s13158-020-00272-6
Lamberti, M.J., Wilkinson, M., Donzanti, B.A., Wohlhieter, G.E., Parikh, S., Wilkins, R.G., & Getz, K. (2019). A Study on the Application and Use of Artificial Intelligence to Support Drug Development. Clinical Therapeutics, 41(8), 1414–1426. https://doi.org/10.1016/j.clinthera.2019.05.018
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
Liu, R., Li, S., & Yang, L. (2020). Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy. Omega, 90, 101990. https://doi.org/10.1016/j.omega.2018.10.020
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015
Mishra, S. (2020). Social networks, social capital, social support and academic success in higher education: A systematic review with a special focus on 'underrepresented' students. Educational Research Review, 29, 100307. https://doi.org/10.1016/j.edurev.2019.100307
Olivier, E., Archambault, I., De Clercq, M., & Galand, B. (2019). Student Self-Efficacy, Classroom Engagement, and Academic Achievement: Comparing Three Theoretical Frameworks. Journal of Youth and Adolescence, 48(2), 326–340. https://doi.org/10.1007/s10964-018-0952-0
Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182. https://doi.org/10.1007/s10845-018-1433-8
Pavlik, J.V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 78(1), 84–93. https://doi.org/10.1177/10776958221149577
Rahmawati, TN (2020). TECHNOLOGY SUPPORTS STUDENT LEARNING IN INTEGRATED SOCIAL SCIENCE SUBJECTS. Journal of Social Incentives, 3(2), 133–142. https://doi.org/10.36787/jsi.v3i2.316
Riggs, E.R., Andersen, E.F., Cherry, A.M., Kantarci, S., Kearney, H., Patel, A., Raca, G., Ritter, D.I., South, S.T., Thorland, E.C., Pineda-Alvarez, D. , Aradhya, S., & Martin, C.L. (2020). Technical standards for the interpretation and reporting of constitutional copy-number variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genetics in Medicine, 22(2), 245–257. https://doi.org/10.1038/s41436-019-0686-8
Schiavone, F., Mancini, D., Leone, D., & Lavorato, D. (2021). Digital business models and ridesharing for value co-creation in healthcare: A multi-stakeholder ecosystem analysis. Technological Forecasting and Social Change, 166, 120647. https://doi.org/10.1016/j.techfore.2021.120647
Schrotter, G., & Hürzeler, C. (2020). The Digital Twin of the City of Zurich for Urban Planning. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(1), 99–112. https://doi.org/10.1007/s41064-020-00092-2
Smidstrup, S., Markussen, T., Vancraeyveld, P., Wellendorff, J., Schneider, J., Gunst, T., Verstichel, B., Stradi, D., Khomyakov, P.A., Vej-Hansen, U.G., Lee, M.-E., Chill, S.T., Rasmussen, F., Penazzi, G., Corsetti, F., Ojanperä, A., Jensen, K., Palsgaard, MLN, Martinez, U., … Stokbro, K . (2020). QuantumATK: An integrated platform of electronic and atomic-scale modeling tools. Journal of Physics: Condensed Matter, 32(1), 015901. https://doi.org/10.1088/1361-648X/ab4007
Theobald, EJ, Hill, MJ, Tran, E., Agrawal, S., Arroyo, E.N., Behling, S., Chambwe, N., Cintrón, D.L., Cooper, JD, Dunster, G., Grummer, J.A., Hennessey , K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M.E., Littlefield, C.E., … Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and mathematics. Proceedings of the National Academy of Sciences, 117(12), 6476–6483. https://doi.org/10.1073/pnas.1916903117
Ting, DSW, Carin, L., Dzau, V., & Wong, T.Y. (2020). Digital technology and COVID-19. Nature Medicine, 26(4), 459–461. https://doi.org/10.1038/s41591-020-0824-5
Van Mieghem, A., Verschueren, K., Petry, K., & Struyf, E. (2020). An analysis of research on inclusive education: A systematic search and meta review. International Journal of Inclusive Education, 24(6), 675–689. https://doi.org/10.1080/13603116.2018.1482012
Wang, J., Ma, Y., Zhang, L., Gao, R.X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.01.003
Wang, J., Xu, C., Zhang, J., Bao, J., & Zhong, R. (2020). A collaborative architecture of the industrial internet platform for manufacturing systems. Robotics and Computer-Integrated Manufacturing, 61, 101854. https://doi.org/10.1016/j.rcim.2019.101854
Yu, F., Yan, L., Wang, N., Yang, S., Wang, L., Tang, Y., Gao, G., Wang, S., Ma, C., Xie, R., Wang, F., Tan, C., Zhu, L., Guo, Y., & Zhang, F. (2020). Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clinical Infectious Diseases, 71(15), 793–798. https://doi.org/10.1093/cid/ciaa345
Zou, H., Guo, L., Xue, H., Zhang, Y., Shen, X., Liu, X., Wang, P., He, X., Dai, G., Jiang, P., Zheng, H., Zhang, B., Xu, C., & Wang, Z. L. (2020). Quantifying and understanding the triboelectric series of inorganic non-metallic materials. Nature Communications, 11(1), 2093. https://doi.org/10.1038/s41467-020-15926-1
Copyright (c) 2024 Dwi Priyatno, Rizky Wardhani, Cheriani Cheriani, Yoel Frans Alfredo, Fatmawati Sabur
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
