Number Board Media to Stimulate the Symbolic Thinking Ability of Children Aged 5-6 Years

Ipah Saripah (1), Imam Tabroni (2), Yuanyuan Wang (3), Guijiao Zou (4)
(1) Universitas Islam Bunga Bangsa Cirebon, Indonesia,
(2) Universitas Islam Bunga Bangsa Cirebon, Indonesia,
(3) Yangon University, Myanmar,
(4) Public Universities and Colleges, Taiwan, Province of China

Abstract

Children will experience a golden period that is very important and should not be missed until they are 3 years old. Because during these times, children's cognitive abilities are still growing and developing rapidly. In fact, about 80% of children's cognitive skills are optimized in the first 3 years of life, and up to 90% of their abilities will continue to develop until they reach the age of 5. Media is a tool that can be used as an intermediary in stimulating all aspects of development in early childhood both aspects of moral and religious values, physical motor aspects, language aspects, social emotional aspects and cognitive aspects. To stimulate all aspects of early childhood development cannot be separated from learning media because for early childhood learning is done through play using learning media both real media, audio media, visual media, environmental media and audio-visual media, so that learning activities in early childhood run effectively. This article will discuss how the influence of number board media in stimulating the symbolic thinking ability of children aged 5-6 years in Sumurugul Village.


 

Full text article

Generated from XML file

References

Albrecht, E., & Chin, K. J. (2020). Advances in regional anaesthesia and acute pain management: A narrative review. Anaesthesia, 75(S1). https://doi.org/10.1111/anae.14868

Arora, S., Singh, H., Sharma, M., Sharma, S., & Anand, P. (2019). A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection. IEEE Access, 7, 26343–26361. https://doi.org/10.1109/ACCESS.2019.2897325

Bai, B., Guo, Z., Zhou, C., Zhang, W., & Zhang, J. (2021). Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering. Information Sciences, 546, 42–59. https://doi.org/10.1016/j.ins.2020.07.069

Caniëls, M. C. J., Chiocchio, F., & Van Loon, N. P. A. A. (2019). Collaboration in project teams: The role of mastery and performance climates. International Journal of Project Management, 37(1), 1–13. https://doi.org/10.1016/j.ijproman.2018.09.006

Chen, Y., Zhong, H., Wang, J., Wan, X., Li, Y., Pan, W., Li, N., & Tang, B. (2019). Catalase-like metal–organic framework nanoparticles to enhance radiotherapy in hypoxic cancer and prevent cancer recurrence. Chemical Science, 10(22), 5773–5778. https://doi.org/10.1039/C9SC00747D

Gao, Z., Dang, W., Wang, X., Hong, X., Hou, L., Ma, K., & Perc, M. (2021). Complex networks and deep learning for EEG signal analysis. Cognitive Neurodynamics, 15(3), 369–388. https://doi.org/10.1007/s11571-020-09626-1

Golden, T. D., & Gajendran, R. S. (2019). Unpacking the Role of a Telecommuter’s Job in Their Performance: Examining Job Complexity, Problem Solving, Interdependence, and Social Support. Journal of Business and Psychology, 34(1), 55–69. https://doi.org/10.1007/s10869-018-9530-4

Hassan, M. H., Houssein, E. H., Mahdy, M. A., & Kamel, S. (2021). An improved Manta ray foraging optimizer for cost-effective emission dispatch problems. Engineering Applications of Artificial Intelligence, 100, 104155. https://doi.org/10.1016/j.engappai.2021.104155

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0

Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657–673. https://doi.org/10.1016/j.landusepol.2018.12.030

Low, E. S., Ong, P., & Cheah, K. C. (2019). Solving the optimal path planning of a mobile robot using improved Q-learning. Robotics and Autonomous Systems, 115, 143–161. https://doi.org/10.1016/j.robot.2019.02.013

Penconek, T., Tate, K., Bernardes, A., Lee, S., Micaroni, S. P. M., Balsanelli, A. P., De Moura, A. A., & Cummings, G. G. (2021). Determinants of nurse manager job satisfaction: A systematic review. International Journal of Nursing Studies, 118, 103906. l

Peng, H., Wang, H., Du, B., Bhuiyan, M. Z. A., Ma, H., Liu, J., Wang, L., Yang, Z., Du, L., Wang, S., & Yu, P. S. (2020). Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences, 521, 277–290. https://doi.org/10.1016/j.ins.2020.01.043

Pfattheicher, S., Nielsen, Y. A., & Thielmann, I. (2022). Prosocial behavior and altruism: A review of concepts and definitions. Current Opinion in Psychology, 44, 124–129. https://doi.org/10.1016/j.copsyc.2021.08.021

Song, J., She, J., Chen, D., & Pan, F. (2020). Latest research advances on magnesium and magnesium alloys worldwide. Journal of Magnesium and Alloys, 8(1), 1–41. https://doi.org/10.1016/j.jma.2020.02.003

Van Doren, J., Arns, M., Heinrich, H., Vollebregt, M. A., Strehl, U., & K. Loo, S. (2019). Sustained effects of neurofeedback in ADHD: A systematic review and meta-analysis. European Child & Adolescent Psychiatry, 28(3), 293–305. https://doi.org/10.1007/s00787-018-1121-4

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

Wang, F., Wang, H., Wang, H., Li, G., & Situ, G. (2019). Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging. Optics Express, 27(18), 25560. https://doi.org/10.1364/OE.27.025560

Wang, S., Chen, X., & Szolnoki, A. (2019). Exploring optimal institutional incentives for public cooperation. Communications in Nonlinear Science and Numerical Simulation, 79, 104914. https://doi.org/10.1016/j.cnsns.2019.104914

Yang, Z., Yu, W., Liang, P., Guo, H., Xia, L., Zhang, F., Ma, Y., & Ma, J. (2019). Deep transfer learning for military object recognition under small training set condition. Neural Computing and Applications, 31(10), 6469–6478. https://doi.org/10.1007/s00521-018-3468-3

Zhang, Y., & Jin, Z. (2020). Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 148, 113246. https://doi.org/10.1016/j.eswa.2020.113246

Authors

Ipah Saripah
ipahsaripah1183@gmail.com (Primary Contact)
Imam Tabroni
Yuanyuan Wang
Guijiao Zou
Saripah, I., Tabroni, I., Wang, Y., & Zou, G. (2023). Number Board Media to Stimulate the Symbolic Thinking Ability of Children Aged 5-6 Years. Journal of Computer Science Advancements, 1(2), 103–112. https://doi.org/10.55849/jsca.v1i2.456

Article Details

Most read articles by the same author(s)

1 2 > >>