Learning from the Perspective of Parent and Teacher Creativity in Using Learning Media
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One of the impacts of the pandemic in the world of education is that the learning process must be carried out online. So efforts to provide learning media are too forced. Not only from an economic perspective, but also the ability to understand and use learning media. It takes creativity and solidarity between parents and teachers to outsmart and avoid bad things that will happen. The aim of this research is to determine the role of parents and determine teachers' strategies, designs and plans in determining learning media. This research uses qualitative methods using survey models and in-depth interviews. The results of this research are that parents and teachers have the same point of view and innovation in the use of learning media. This research confirms that the use of learning media is not only focused on learning objectives, but it is important to take into account several things: 1) how to select media, which consists of media selection models and reasons for using media. 2) media selection criteria consisting of the goals and objectives of media use, 3) media characteristics, 4) time and 5) cost of media use and 6) availability of the learning media. It is hoped that all steps chosen really take into account the principles of using learning media. Therefore, the limitation of this research is that researchers only focus on solution actions regarding innovation that make things easier for the parties involved in the learning process. It is hoped that future researchers can continue this research in certain subjects.
Agrawal, S., & Awekar, A. (2018). Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms. In G. Pasi, B. Piwowarski, L. Azzopardi, & A. Hanbury (Eds.), Advances in Information Retrieval (Vol. 10772, pp. 141–153). Springer International Publishing. https://doi.org/10.1007/978-3-319-76941-7_11
Ainscow, M., & Messiou, K. (2018). Engaging with the views of students to promote inclusion in education. Journal of Educational Change, 19(1), 1–17. https://doi.org/10.1007/s10833-017-9312-1
Alizadeh, R., Abad, JMN, Ameri, A., Mohebbi, M.R., Mehdizadeh, A., Zhao, D., & Karimi, N. (2021). A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems—Heat transfer in a hybrid nanofluid flow in porous media. Journal of the Taiwan Institute of Chemical Engineers, 124, 290–306. https://doi.org/10.1016/j.jtice.2021.03.043
Amini, S., & Mohaghegh, S. (2019). Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media. Fluids, 4(3), 126. https://doi.org/10.3390/fluids4030126
Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128. https://doi.org/10.1080/17439884.2020.1686014
Arce, M.I., Mendoza-Lera, C., Almagro, M., Catalán, N., Romaní, A.M., Martí, E., Gómez, R., Bernal, S., Foulquier, A., Mutz, M., Marcé, R., Zoppini, A., Gionchetta, G., Weigelhofer, G., del Campo, R., Robinson, CT, Gilmer, A., Rulik, M., Obrador, B., … von Schiller, D . (2019). A conceptual framework for understanding the biogeochemistry of dry riverbeds through the lens of soil science. Earth-Science Reviews, 188, 441–453. https://doi.org/10.1016/j.earscirev.2018.12.001
Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining: Survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1230. https://doi.org/10.1002/widm.1230
Carlson, J., Rahman, M., Voola, R., & De Vries, N. (2018). Customer engagement behaviors in social media: Capturing innovation opportunities. Journal of Services Marketing, 32(1), 83–94. https://doi.org/10.1108/JSM-02-2017-0059
Cheng, K.-H., & Tsai, C.-C. (2019). A case study of immersive virtual field trips in an elementary classroom: Students' learning experience and teacher-student interaction behaviors. Computers & Education, 140, 103600. https://doi.org/10.1016/j.compedu.2019.103600
Chhinzer, N., & Russo, A. M. (2018). An exploration of employer perceptions of graduate student employability. Education + Training, 60(1), 104–120. https://doi.org/10.1108/ET-06-2016-0111
Christodoulou, E., Ma, J., Collins, G.S., Steyerberg, E.W., Verbakel, J.Y., & Van Calster, B. (2019). A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology, 110, 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004
Doyle, L., McCabe, C., Keogh, B., Brady, A., & McCann, M. (2020). An overview of the qualitative descriptive design within nursing research. Journal of Research in Nursing, 25(5), 443–455. https://doi.org/10.1177/1744987119880234
Fan, C., Wu, F., & Mostafavi, A. (2020). A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations From Social Media in Disasters. IEEE Access, 8, 10478–10490. https://doi.org/10.1109/ACCESS.2020.2965550
Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., & Roth, D. (2019). A comparative study of fairness-enhancing interventions in machine learning. Proceedings of the Conference on Fairness, Accountability, and Transparency, 329–338. https://doi.org/10.1145/3287560.3287589
Ghura, A.S., & Damani, B. (2022). Sarvaay Solutions – creating value innovation for farmers. Emerald Emerging Markets Case Studies, 12(2), 1–21. https://doi.org/10.1108/EEMCS-01-2022-0018
Guest, G., Namey, E., & Chen, M. (2020). A simple method to assess and report thematic saturation in qualitative research. PLOS ONE, 15(5), e0232076. https://doi.org/10.1371/journal.pone.0232076
Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & SOCIETY, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
Haegele, J., Zhu, X., & Davis, S. (2018). Barriers and facilitators of physical education participation for students with disabilities: An exploratory study. International Journal of Inclusive Education, 22(2), 130–141. https://doi.org/10.1080/13603116.2017.1362046
Hami?D, N., Roehri?G, G., Li?Esnoor, D., Rachmah, H., Royyani, Muh. A., & Hanifah, M. (2021). Development Model for Environment-Based Learning to Improve Junior High School Students' Geographical Skills. Review of International Geographical Education Online. https://doi.org/10.33403/rigeo.833857
Haq, A.U., Li, J.P., Memon, M.H., Nazir, S., & Sun, R. (2018). A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms. Mobile Information Systems, 2018, 1–21. https://doi.org/10.1155/2018/3860146
Harmon, DJ, Attardi, SM, Barremkala, M., Bentley, DC, Brown, KM, Dennis, JF, Goldman, HM, Harrell, KM, Klein, BA, Ramnanan, CJ, Richtsmeier, JT, & Farkas, GJ ( 2021). An Analysis of Anatomy Education Before and During Covid?19: May–August 2020. Anatomical Sciences Education, 14(2), 132–147. https://doi.org/10.1002/ase.2051
Huang, A., Nguyen, PQ, Stark, JC, Takahashi, MK, Donghia, N., Ferrante, T., Dy, AJ, Hsu, KJ, Dubner, R.S., Pardee, K., Jewett, M.C., & Collins , J. J. (2018). BioBitsTM Explorer: A modular synthetic biology education kit. Science Advances, 4(8), eaat5105. https://doi.org/10.1126/sciadv.aat5105
Imants, J., & Van der Wal, M. M. (2020). A model of teacher agency in professional development and school reform. Journal of Curriculum Studies, 52(1), 1–14. https://doi.org/10.1080/00220272.2019.1604809
Kappel, D., Legenstein, R., Habenschuss, S., Hsieh, M., & Maass, W. (2018). A Dynamic Connectome Supports the Emergence of Stable Computational Functions of Neural Circuits through Reward-Based Learning. Eneuro, 5(2), ENEURO.0301-17.2018. https://doi.org/10.1523/ENEURO.0301-17.2018
Karabulut-Ilgu, A., Jaramillo Cherrez, N., & Jahren, C.T. (2018). A systematic review of research on the flipped learning method in engineering education: Flipped Learning in Engineering Education. British Journal of Educational Technology, 49(3), 398–411. https://doi.org/10.1111/bjet.12548
Koomen, M. H., Rodriguez, E., Hoffman, A., Petersen, C., & Oberhauser, K. (2018). Authentic science with citizen science and student?driven science fair projects. Science Education, 102(3), 593–644. https://doi.org/10.1002/sce.21335
Lee, J. S. (2019). EFL students' views of willingness to communicate in the extramural digital context. Computer Assisted Language Learning, 32(7), 692–712. https://doi.org/10.1080/09588221.2018.1535509
Martin, F., Sun, T., & Westine, C. D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & Education, 159, 104009. https://doi.org/10.1016/j.compedu.2020.104009
Medeiros, R.P., Ramalho, G.L., & Falcao, T.P. (2019). A Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education. IEEE Transactions on Education, 62(2), 77–90. https://doi.org/10.1109/TE.2018.2864133
Mehta, P., Bukov, M., Wang, C.-H., Day, AGR, Richardson, C., Fisher, C.K., & Schwab, D.J. (2019). A high-bias, low-variance introduction to Machine Learning for physicists. Physics Reports, 810, 1–124. https://doi.org/10.1016/j.physrep.2019.03.001
Meng, X., & Karniadakis, G. E. (2020). A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. Journal of Computational Physics, 401, 109020. https://doi.org/10.1016/j.jcp.2019.109020
Mungai, E.M. (2021). Climate financing: Case study of Kenya Climate Venture Ltd. Emerald Emerging Markets Case Studies, 11(2), 1–25. https://doi.org/10.1108/EEMCS-09-2020-0355
Newman, G., Shi, T., Yao, Z., Li, D., Sansom, G., Kirsch, K., Casillas, G., & Horney, J. (2020). Citizen Science-Informed Community Master Planning: Land Use and Built Environment Changes to Increase Flood Resilience and Decrease Contaminant Exposure. International Journal of Environmental Research and Public Health, 17(2), 486. https://doi.org/10.3390/ijerph17020486
Page, J. (2018). Characterizing the principles of Professional Love in early childhood care and education. International Journal of Early Years Education, 26(2), 125–141. https://doi.org/10.1080/09669760.2018.1459508
Patel, S., Pelletier-Bui, A., Smith, S., Roberts, M.B., Kilgannon, H., Trzeciak, S., & Roberts, B.W. (2019). Curricula for empathy and compassion training in medical education: A systematic review. PLOS ONE, 14(8), e0221412. https://doi.org/10.1371/journal.pone.0221412
Phillippi, J., & Lauderdale, J. (2018). A Guide to Field Notes for Qualitative Research: Context and Conversation. Qualitative Health Research, 28(3), 381–388. https://doi.org/10.1177/1049732317697102
Preim, B., & Saalfeld, P. (2018). A survey of virtual human anatomy educational systems. Computers & Graphics, 71, 132–153. https://doi.org/10.1016/j.cag.2018.01.005
Puljak, L., ?ivljak, M., Haramina, A., Mališa, S., ?avi?, D., Klinec, D., Aranza, D., Mesari?, J., Skitareli?, N., Zorani?, S., Majstorovi?, D., Neuberg, M., Mikši?, Š., & Ivaniševi?, K. (2020). Attitudes and concerns of undergraduate university health sciences students in Croatia regarding complete switch to e-learning during COVID-19 pandemic: A survey. BMC Medical Education, 20(1), 416. https://doi.org/10.1186/s12909-020-02343-7
Santos, H., Batista, J., & Marques, R.P. (2019). Digital transformation in higher education: The use of communication technologies by students. Procedia Computer Science, 164, 123–130. https://doi.org/10.1016/j.procs.2019.12.163
Schmid, R., & Petko, D. (2019). Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Computers & Education, 136, 75–86. https://doi.org/10.1016/j.compedu.2019.03.006
Shorey, S., Kowitlawakul, Y., Devi, M.K., Chen, H.-C., Soong, SKA, & Ang, E. (2018). Blended learning pedagogy designed for communication module among undergraduate nursing students: A quasi-experimental study. Nurse Education Today, 61, 120–126. https://doi.org/10.1016/j.nedt.2017.11.011
Spengler, M., Damian, R.I., & Roberts, B.W. (2018). How you behave in school predicts life success above and beyond family background, broad traits, and cognitive abilities. Journal of Personality and Social Psychology, 114(4), 620–636. https://doi.org/10.1037/pspp0000185
Stathopoulou, A., Siamagka, N.-T., & Christodoulides, G. (2019). A multi-stakeholder view of social media as a supporting tool in higher education: An educator–student perspective. European Management Journal, 37(4), 421–431. https://doi.org/10.1016/j.emj.2019.01.008
Sun, N., Wei, L., Shi, S., Jiao, D., Song, R., Ma, L., Wang, H., Wang, C., Wang, Z., You, Y., Liu, S., & Wang, H. (2020). A qualitative study on the psychological experience of caregivers of COVID-19 patients. American Journal of Infection Control, 48(6), 592–598. https://doi.org/10.1016/j.ajic.2020.03.018
Swindle, T., Johnson, S.L., Davenport, K., Whiteside-Mansell, L., Thirunavukarasu, T., Sadasavin, G., & Curran, G.M. (2019). A Mixed-Methods Exploration of Barriers and Facilitators to Evidence-Based Practices for Obesity Prevention in Head Start. Journal of Nutrition Education and Behavior, 51(9), 1067-1079.e1. https://doi.org/10.1016/j.jneb.2019.06.019
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of Artificial Intelligence and Machine learning in smart cities. Computer Communications, 154, 313–323. https://doi.org/10.1016/j.comcom.2020.02.069
van Leeuwen, A., & Janssen, J. (2019). A systematic review of teacher guidance during collaborative learning in primary and secondary education. Educational Research Review, 27, 71–89. https://doi.org/10.1016/j.edurev.2019.02.001
Xiao, Y., Wu, J., Lin, Z., & Zhao, X. (2018). A deep learning-based multi-model ensemble method for cancer prediction. Computer Methods and Programs in Biomedicine, 153, 1–9. https://doi.org/10.1016/j.cmpb.2017.09.005
Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86, 580–596. https://doi.org/10.1016/j.trc.2017.11.027
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