Storytelling in Optimizing the Learning of the History of Noah As
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History, Learning, Storytelling
Abstract
Background. As Muslims, we are obliged to study knowledge to be a provision in life, one of which is history. History is an event that happened in the past that we can learn and take lessons from for future life.
Purpose. Derived from Arabic, syajarah, which means tree. Like a small seed that can grow as a big tree. History is considered to bring change, that's why history must be studied. Especially the history of the Prophets and Messengers.
Method. . It is very important for children to learn in order to get the wisdom and advice contained therein.
Results. Apart from gaining knowledge from formal education, non-formal education is also important for children. Non-formal education is present as a complement to formal education, namely to fulfill certain aspects that are not given in formal education.
Conclusion. To fill the school void in certain aspects, it is good that parents can look for alternatives from non-formal education, one of which is by enrolling children in the Al-Qur'an Education Park. Taman Pendidikan Al-Qur'an or commonly abbreviated as TPA.
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Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3), 292. https://doi.org/10.3390/electronics8030292
Aparicio-Martinez, P., Perea-Moreno, A.-J., Martinez-Jimenez, M. P., Redel-Macías, M. D., Vaquero-Abellan, M., & Pagliari, C. (2019). A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People. International Journal of Environmental Research and Public Health, 16(20), 4024. https://doi.org/10.3390/ijerph16204024
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., Kapa, S., & Friedman, P. A. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. The Lancet, 394(10201), 861–867. https://doi.org/10.1016/S0140-6736(19)31721-0
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A., Pereira, J. H., Rodrigues, A. V., Van Dijk, A. A., Ebrecht, A. C., … Baker, D. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876. https://doi.org/10.1126/science.abj8754
Bylemans, T., Heleven, E., Baetens, K., Deroost, N., Baeken, C., & Van Overwalle, F. (2022). A narrative sequencing and mentalizing training for adults with autism: A pilot study. Frontiers in Behavioral Neuroscience, 16, 941272. https://doi.org/10.3389/fnbeh.2022.941272
Chang, M., Wang, M., Wang, M., Shu, M., Ding, B., Li, C., Pang, M., Cui, S., Hou, Z., & Lin, J. (2019). A Multifunctional Cascade Bioreactor Based on Hollow?Structured Cu 2 MoS 4 for Synergetic Cancer Chemo?Dynamic Therapy/Starvation Therapy/Phototherapy/Immunotherapy with Remarkably Enhanced Efficacy. Advanced Materials, 31(51), 1905271. https://doi.org/10.1002/adma.201905271
Chen, Y., Wang, T., Tian, H., Su, D., Zhang, Q., & Wang, G. (2021). Advances in Lithium–Sulfur Batteries: From Academic Research to Commercial Viability. Advanced Materials, 33(29), 2003666. https://doi.org/10.1002/adma.202003666
Font, A., Guiseppin, L., Blangiardo, M., Ghersi, V., & Fuller, G. W. (2019). A tale of two cities: Is air pollution improving in Paris and London? Environmental Pollution, 249, 1–12. https://doi.org/10.1016/j.envpol.2019.01.040
Gal, D., & Rucker, D. D. (2021). Act boldly: Important life decisions, courage, and the motivated pursuit of risk. Journal of Personality and Social Psychology, 120(6), 1607–1620. https://doi.org/10.1037/pspi0000329
Gerber, I. C., & Serp, P. (2020). A Theory/Experience Description of Support Effects in Carbon-Supported Catalysts. Chemical Reviews, 120(2), 1250–1349. https://doi.org/10.1021/acs.chemrev.9b00209
Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A. (2020). A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host & Microbe, 27(4), 671-680.e2. https://doi.org/10.1016/j.chom.2020.03.002
Hendry, & Chen, R.-C. (2019). Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning. Image and Vision Computing, 87, 47–56. https://doi.org/10.1016/j.imavis.2019.04.007
Hindricks, G., Potpara, T., Dagres, N., Arbelo, E., Bax, J. J., Blomström-Lundqvist, C., Boriani, G., Castella, M., Dan, G.-A., Dilaveris, P. E., Fauchier, L., Filippatos, G., Kalman, J. M., La Meir, M., Lane, D. A., Lebeau, J.-P., Lettino, M., Lip, G. Y. H., Pinto, F. J., … Zakirov, N. U. (2021). 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). European Heart Journal, 42(5), 373–498. https://doi.org/10.1093/eurheartj/ehaa612
Huerta-Cepas, J., Szklarczyk, D., Heller, D., Hernández-Plaza, A., Forslund, S. K., Cook, H., Mende, D. R., Letunic, I., Rattei, T., Jensen, L. J., von Mering, C., & Bork, P. (2019). eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research, 47(D1), D309–D314. https://doi.org/10.1093/nar/gky1085
Hull, D. M., Lowry, P. B., Gaskin, J. E., & Mirkovski, K. (2019). A storyteller’s guide to problem?based learning for information systems management education. Information Systems Journal, 29(5), 1040–1057. https://doi.org/10.1111/isj.12234
Janssen, M., & Van Der Voort, H. (2020). Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic. International Journal of Information Management, 55, 102180. https://doi.org/10.1016/j.ijinfomgt.2020.102180
Kaeophanuek, S., Na-Songkhla, J., & Nilsook, P. (2019). A Learning Process Model to Enhance Digital Literacy using Critical Inquiry through Digital Storytelling (CIDST). International Journal of Emerging Technologies in Learning (iJET), 14(03), 22. https://doi.org/10.3991/ijet.v14i03.8326
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827
Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, 101162. https://doi.org/10.1016/j.learninstruc.2018.08.002
Mantere, T., Kersten, S., & Hoischen, A. (2019). Long-Read Sequencing Emerging in Medical Genetics. Frontiers in Genetics, 10, 426. https://doi.org/10.3389/fgene.2019.00426
Mazza, C., Ricci, E., Biondi, S., Colasanti, M., Ferracuti, S., Napoli, C., & Roma, P. (2020). A Nationwide Survey of Psychological Distress among Italian People during the COVID-19 Pandemic: Immediate Psychological Responses and Associated Factors. International Journal of Environmental Research and Public Health, 17(9), 3165. https://doi.org/10.3390/ijerph17093165
Meyer, J., McDowell, C., Lansing, J., Brower, C., Smith, L., Tully, M., & Herring, M. (2020). Changes in Physical Activity and Sedentary Behavior in Response to COVID-19 and Their Associations with Mental Health in 3052 US Adults. International Journal of Environmental Research and Public Health, 17(18), 6469. https://doi.org/10.3390/ijerph17186469
Montag, C., & Elhai, J. D. (2019). A new agenda for personality psychology in the digital age? Personality and Individual Differences, 147, 128–134. https://doi.org/10.1016/j.paid.2019.03.045
Moriguchi, T., Harii, N., Goto, J., Harada, D., Sugawara, H., Takamino, J., Ueno, M., Sakata, H., Kondo, K., Myose, N., Nakao, A., Takeda, M., Haro, H., Inoue, O., Suzuki-Inoue, K., Kubokawa, K., Ogihara, S., Sasaki, T., Kinouchi, H., … Shimada, S. (2020). A first case of meningitis/encephalitis associated with SARS-Coronavirus-2. International Journal of Infectious Diseases, 94, 55–58. https://doi.org/10.1016/j.ijid.2020.03.062
Qin, C., Zhou, L., Hu, Z., Zhang, S., Yang, S., Tao, Y., Xie, C., Ma, K., Shang, K., Wang, W., & Tian, D.-S. (2020). Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan, China. Clinical Infectious Diseases, 71(15), 762–768. https://doi.org/10.1093/cid/ciaa248
Ruder, S., Vuli?, I., & Søgaard, A. (2019). A Survey of Cross-lingual Word Embedding Models. Journal of Artificial Intelligence Research, 65, 569–631. https://doi.org/10.1613/jair.1.11640
Sharma, K. (2019). Cholinesterase inhibitors as Alzheimer’s therapeutics (Review). Molecular Medicine Reports. https://doi.org/10.3892/mmr.2019.10374
Shen, B., Choi, T.-M., & Minner, S. (2019). A review on supply chain contracting with information considerations: Information updating and information asymmetry. International Journal of Production Research, 57(15–16), 4898–4936. https://doi.org/10.1080/00207543.2018.1467062
Sollai, G., Tomassini Barbarossa, I., Usai, P., Hummel, T., & Crnjar, R. (2020). Association between human olfactory performance and ability to detect single compounds in complex chemical mixtures. Physiology & Behavior, 217, 112820. https://doi.org/10.1016/j.physbeh.2020.112820
Stutt, R. O. J. H., Retkute, R., Bradley, M., Gilligan, C. A., & Colvin, J. (2020). A modelling framework to assess the likely effectiveness of facemasks in combination with ‘lock-down’ in managing the COVID-19 pandemic. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2238), 20200376. https://doi.org/10.1098/rspa.2020.0376
Wang, B., Liu, Y., Qian, J., & Parker, S. K. (2021). Achieving Effective Remote Working During the COVID?19 Pandemic: A Work Design Perspective. Applied Psychology, 70(1), 16–59. https://doi.org/10.1111/apps.12290
Wang, B., Tang, C., Wang, H., Chen, X., Cao, R., & Zhang, Q. (2019). A Nanosized CoNi Hydroxide@Hydroxysulfide Core–Shell Heterostructure for Enhanced Oxygen Evolution. Advanced Materials, 31(4), 1805658. https://doi.org/10.1002/adma.201805658
Xiao, F. (2020). A new divergence measure for belief functions in D–S evidence theory for multisensor data fusion. Information Sciences, 514, 462–483. https://doi.org/10.1016/j.ins.2019.11.022
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7), 1235–1270. https://doi.org/10.1162/neco_a_01199
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Copyright (c) 2023 Fildza Qonita Raihana, Imam Tabroni, Kailie Maharjan, Eladdadi Mark
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