Implementation of Virtual Clashroom: Connecting Teachers and Students Online for Effective Distance Learning
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The global pandemic has pushed educational communities around the world to look for innovative solutions to make distance learning effective. One of the emerging solutions is to use Virtual Clashroom, a concept that combines cutting-edge video conferencing technology with student-focused interactions. This research discusses the application of Virtual Clashroom as a solution to connect teachers and students online in distance education. Virtual Clashroom provides an interactive platform that allows teachers to teach in a more interesting and interesting way. With features like digital whiteboards, screen sharing, and interactive chat, teachers can facilitate active and collaborative learning. Students can ask questions, participate in discussions, and collaborate with other students in a safe virtual environment. This research also evaluates the effectiveness of Virtual Clashroom in increasing student engagement, subject understanding, and teacher-student interactions. The results show that Virtual Clashroom can create a more interesting and effective distance learning experience than conventional methods. Students reported high levels of satisfaction in using the platform, while teachers felt they could teach more effectively in a virtual environment. The Virtual Clashroom application opens up new opportunities in the world of education, especially during emergency situations such as a pandemic. By utilizing this technology, distance learning can become more interactive, inclusive and effective. This study provides valuable information about how Virtual Clashroom can be the right solution to improve the quality of education in the future. The method used in this research is a quantitative method. Researchers conducted a survey using a Google form consisting of 15 statements related to the title of the research. The limitation of this research is that the researcher only conducted research in schools and the researcher did not conduct research directly in schools but shared a survey link on a Google form containing a statement about Implementing Virtual Clashroom: Connecting Teachers and Students Online for Effective Distance Learning.
Agarwal, S., & Kaushik, J. S. (2020). Student’s Perception of Online Learning during COVID Pandemic. The Indian Journal of Pediatrics, 87(7), 554–554. https://doi.org/10.1007/s12098-020-03327-7
Al-Balas, M., Al-Balas, H. I., Jaber, H. M., Obeidat, K., Al-Balas, H., Aborajooh, E. A., Al-Taher, R., & Al-Balas, B. (2020). Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: Current situation, challenges, and perspectives. BMC Medical Education, 20(1), 341. https://doi.org/10.1186/s12909-020-02257-4
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
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499
Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (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
Cui, W., Shen, K., & Yu, W. (2019). Spatial Deep Learning for Wireless Scheduling. IEEE Journal on Selected Areas in Communications, 37(6), 1248–1261. https://doi.org/10.1109/JSAC.2019.2904352
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
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419
Kyaw, B. M., Saxena, N., Posadzki, P., Vseteckova, J., Nikolaou, C. K., George, P. P., Divakar, U., Masiello, I., Kononowicz, A. A., Zary, N., & Tudor Car, L. (2019). Virtual Reality for Health Professions Education: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration. Journal of Medical Internet Research, 21(1), e12959. https://doi.org/10.2196/12959
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
Liboni, L. B., Cezarino, L. O., Jabbour, C. J. C., Oliveira, B. G., & Stefanelli, N. O. (2019). Smart industry and the pathways to HRM 4.0: Implications for SCM. Supply Chain Management: An International Journal, 24(1), 124–146. https://doi.org/10.1108/SCM-03-2018-0150
Lin, L., Dou, Q., Jin, Y.-M., Zhou, G.-Q., Tang, Y.-Q., Chen, W.-L., Su, B.-A., Liu, F., Tao, C.-J., Jiang, N., Li, J.-Y., Tang, L.-L., Xie, C.-M., Huang, S.-M., Ma, J., Heng, P.-A., Wee, J. T. S., Chua, M. L. K., Chen, H., & Sun, Y. (2019). Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma. Radiology, 291(3), 677–686. https://doi.org/10.1148/radiol.2019182012
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep Learning for Generic Object Detection: A Survey. International Journal of Computer Vision, 128(2), 261–318. https://doi.org/10.1007/s11263-019-01247-4
Merchant, R. M., & Lurie, N. (2020). Social Media and Emergency Preparedness in Response to Novel Coronavirus. JAMA, 323(20), 2011. https://doi.org/10.1001/jama.2020.4469
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
Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions. IEEE Access, 7, 137184–137206. https://doi.org/10.1109/ACCESS.2019.2942390
Mu’minah, I. H., & Gaffar, A. A. (2020). OPTIMALISASI PENGGUNAAN GOOGLE CLASSROOM SEBAGAI ALTERNATIF DIGITALISASI DALAM PEMBELAJARAN JARAK JAUH (PJJ). BIO EDUCATIO?: (The Journal of Science and Biology Education), 5(2). https://doi.org/10.31949/be.v5i2.2610
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
Rostami, M., Kolouri, S., Eaton, E., & Kim, K. (2019). Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sensing, 11(11), 1374. https://doi.org/10.3390/rs11111374
Salam, M., Awang Iskandar, D. N., Ibrahim, D. H. A., & Farooq, M. S. (2019). Service learning in higher education: A systematic literature review. Asia Pacific Education Review, 20(4), 573–593. https://doi.org/10.1007/s12564-019-09580-6
Sriyani, I.-. (2021). GOOGLE CLASSROOM SEBAGAI SOLUSI PEMBELAJARAN JARAK JAUH MATA PELAJARAN ADMINISTRASI UMUM. Indonesian Journal of Education and Learning, 4(2), 456. https://doi.org/10.31002/ijel.v4i2.3111
Tao, Q., Yan, W., Wang, Y., Paiman, E. H. M., Shamonin, D. P., Garg, P., Plein, S., Huang, L., Xia, L., Sramko, M., Tintera, J., De Roos, A., Lamb, H. J., & Van Der Geest, R. J. (2019). Deep Learning–based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study. Radiology, 290(1), 81–88. https://doi.org/10.1148/radiol.2018180513
Tomasik, M. J., Helbling, L. A., & Moser, U. (2021). Educational gains of IN?PERSON vs. Distance learning in primary and secondary schools: A natural experiment during the COVID ?19 pandemic school closures in Switzerland. International Journal of Psychology, 56(4), 566–576. https://doi.org/10.1002/ijop.12728
Universitas Potensi Utama, & Alfina, O. (2020). PENERAPAN LMS-GOOGLE CLASSROOM DALAM PEMBELAJARAN DARING SELAMA PANDEMI COVID-19. Majalah Ilmiah METHODA, 10(1), 38–46. https://doi.org/10.46880/methoda.Vol10No1.pp38-46
Wen, F., Sun, Z., He, T., Shi, Q., Zhu, M., Zhang, Z., Li, L., Zhang, T., & Lee, C. (2020). Machine Learning Glove Using Self?Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications. Advanced Science, 7(14), 2000261. https://doi.org/10.1002/advs.202000261
Winter, R., Montanari, F., Noé, F., & Clevert, D.-A. (2019). Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chemical Science, 10(6), 1692–1701. https://doi.org/10.1039/C8SC04175J
Wu, C.-J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., Hazelwood, K., Isaac, E., Jia, Y., Jia, B., Leyvand, T., Lu, H., Lu, Y., Qiao, L., Reagen, B., Spisak, J., Sun, F., Tulloch, A., Vajda, P., … Zhang, P. (2019). Machine Learning at Facebook: Understanding Inference at the Edge. 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), 331–344. https://doi.org/10.1109/HPCA.2019.00048
Wu, D., Zheng, S.-J., Zhang, X.-P., Yuan, C.-A., Cheng, F., Zhao, Y., Lin, Y.-J., Zhao, Z.-Q., Jiang, Y.-L., & Huang, D.-S. (2019). Deep learning-based methods for person re-identification: A comprehensive review. Neurocomputing, 337, 354–371. https://doi.org/10.1016/j.neucom.2019.01.079
Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019). A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning. IEEE Access, 7, 19990–19999. https://doi.org/10.1109/ACCESS.2018.2890566
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19. https://doi.org/10.1145/3298981
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
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
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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