Mobile Learning Revolution: Harnessing the Potential of Smartphones in the Learning Process
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Utilizing smartphone-based mobile learning in the learning process will provide the latest innovative alternatives to the learning process. Today's students tend to experience distractions in their learning process. Not infrequently the learning system that applies in a school makes students bored and not enthusiastic about learning. This could have been because the school did not make any changes in the existing education system. Where the effect of the incident will cause students to feel bored and bored in the learning process. In this era of the mobile revolution, it will create various advances in the field of technology that can be used to support the learning process. The purpose of this research is to find out the potential utilization of smartphones as a form of mobile learning revolution in the learning process. Through research on the Mobile Learning Revolution: Utilizing the Potential of Smartphones in the Learning Process, researchers hope that by utilizing the potential of smartphones as a manifestation of the mobile learning revolution in the learning process, it can facilitate the learning process itself. The method used in this research is a quantitative method. The researcher conducted a survey using the Google form which consisted of 15 statements related to the research title. Researchers found that the use of the potential of smartphones in the era of the mobile learning revolution had a major impact on the learning process. The existence of a smartphone in the learning process greatly facilitates the implementation of the teaching and learning process in schools. The limitations of this study are that researchers only conduct research in schools and researchers do not conduct research directly at schools but share survey links on Google forms containing statements about the Mobile Learning Revolution: Utilizing the Potential of Smartphones in the Learning Process.
Aceto, G., Ciuonzo, D., Montieri, A., & Pescape, A. (2019). Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges. IEEE Transactions on Network and Service Management, 16(2), 445–458. https://doi.org/10.1109/TNSM.2019.2899085
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
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
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
Ardiansyah, Abd. A., & Nana, N. (2020). The Role of Mobile Learning as an Innovation in Improving Student Learning Outcomes in School Learning. Indonesian Journal Of Educational Research and Review, 3(1), 47. https://doi.org/10.23887/ijerr.v3i1.24245
Arsyad, MN, & Lestari, DEG (2020). Effectiveness of Using Android-based Mobile Learning Media on Student Learning Outcomes at IKIP Budi Utomo Malang. AGASTYA: JOURNAL OF HISTORY AND ITS STUDY, 10(1), 89. https://doi.org/10.25273/ajsp.v10i1.5072
Bhat, G., Danelljan, M., Van Gool, L., & Timofte, R. (2019). Learning Discriminative Model Prediction for Tracking. 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , 6181–6190. https://doi.org/10.1109/ICCV.2019.00628
Bottani, E., & Vignali, G. (2019). Augmented reality technology in the manufacturing industry: A review of the last decade. IISE Transactions, 51(3), 284–310. https://doi.org/10.1080/24725854.2018.1493244
Budd, J., Miller, BS, Manning, EM, Lampos, V., Zhuang, M., Edelstein, M., Rees, G., Emery, VC, Stevens, MM, Keegan, N., Short, MJ, Pillay, D., Manley, E., Cox, I. J., Heymann, D., Johnson, A. M., & McKendry, R. A. (2020). Digital technologies in the public-health response to COVID-19. Nature Medicine, 26(8), 1183–1192. https://doi.org/10.1038/s41591-020-1011-4
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019). A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities. IEEE Communications Surveys & Tutorials, 21(3), 2419–2465. https://doi.org/10.1109/COMST.2019.2914030
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
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16(3), 031001. https://doi.org/10.1088/1741-2552/ab0ab5
De Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., & Išgum, I. (2019). A deep learning framework for unsupervised affine and deformable image registration. Medical Image Analysis, 52, 128–143. https://doi.org/10.1016/j.media.2018.11.010
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
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419–422. https://doi.org/10.1016/j.dsx.2020.04.032
Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020). Reliable Federated Learning for Mobile Networks. IEEE Wireless Communications, 27(2), 72–80. https://doi.org/10.1109/MWC.001.1900119
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
Martins, J., Costa, C., Oliveira, T., Gonçalves, R., & Branco, F. (2019). How smartphone advertising influences consumers' purchase intention. Journal of Business Research, 94, 378–387. https://doi.org/10.1016/j.jbusres.2017.12.047
Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019). Learning-Based Computation Offloading for IoT Devices With Energy Harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930–1941. https://doi.org/10.1109/TVT.2018.2890685
Mishra, L., Gupta, T., & Shree, A. (2020). Online teaching-learning in higher education during the lockdown period of COVID-19 pandemic. International Journal of Educational Research Open, 1, 100012. https://doi.org/10.1016/j.ijedro.2020.100012
Mishra, P., Pandey, C., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103/aca.ACA_157_18
Niknam, S., Dhillon, H.S., & Reed, J.H. (2020). Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges. IEEE Communications Magazine, 58(6), 46–51. https://doi.org/10.1109/MCOM.001.1900461
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
Pang, G., Shen, C., Cao, L., & Hengel, AVD (2022). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2), 1–38. https://doi.org/10.1145/3439950
Praveen Kumar, D., Amgoth, T., & Annavarapu, CSR (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1–25. https://doi.org/10.1016/j.inffus.2018.09.013
Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
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
Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862
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
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5
Villa-Henriksen, A., Edwards, G.T.C., Pesonen, L.A., Green, O., & Sørensen, CAG (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering, 191, 60–84. https://doi.org/10.1016/j.biosystemseng.2019.12.013
Warsita, B. (2018). MOBILE LEARNING AS AN EFFECTIVE AND INNOVATIVE LEARNING MODEL. Technodic Journal, 062–073. https://doi.org/10.32550/teknodik.v14i1.452
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. https://doi.org/10.1016/j.rse.2020.111716
Zhao, N., Liang, Y.-C., Niyato, D., Pei, Y., Wu, M., & Jiang, Y. (2019). Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks. IEEE Transactions on Wireless Communications, 18(11), 5141–5152. https://doi.org/10.1109/TWC.2019.2933417
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
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