Abstract
The purpose of this research is to develop educational game tools for improve children's cognitive. The application of APE (Educative Game Tool) was carried out on village children to be exact at Harapan Pertiwi 2 Kindergarten. The researcher used the R&D research method (Research & Development) which aims to determine the application of game tools educative windmill counting in developing the development of cognitive aspects of children. This study used a child subject, namely 4 children between the ages of 4-5 years. Technique data collection using observation, interviews, trials, expert validation, and documentation. Based on the results of the research on the application of the Counting Rabbit Educative Game Tool it is proven feasible to use and able to improve children's cognitive abilities. Children are very enthusiastic in trying the game. The results of the data obtained from the Material Expert found that the total rating score obtained was 63 out of the expected 80, after being converted in the presentation the result was 143.2% in the 'Very Good' category. In the assessment rubric it is known that there is a significant difference in the ability to recognize the concept of numbers in children aged 5-6 years in playing the Counting Rabbit Windmill. In the pre-test activity, an average score of 30 out of 80 was obtained. Meanwhile, in the post-test activity using developed media, an average score of 50 was obtained from the score of 85. The pre-test and post-test activities experienced an increase in the average score of 125.1. So that with the Counting Rabbit Wheel media it can improve children's cognitive abilities.
Full text article
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
Huseien, G. F., & Shah, K. W. (2020). Durability and life cycle evaluation of self-compacting concrete containing fly ash as GBFS replacement with alkali activation. Construction and Building Materials, 235, 117458. https://doi.org/10.1016/j.conbuildmat.2019.117458
Jiang, L., Zhang, L. J., & May, S. (2019). Implementing English-medium instruction (EMI) in China: Teachers’ practices and perceptions, and students’ learning motivation and needs. International Journal of Bilingual Education and Bilingualism, 22(2), 107–119. https://doi.org/10.1080/13670050.2016.1231166
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. https://doi.org/10.1016/j.ijnurstu.2021.103906
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
Salminen, J., Hopf, M., Chowdhury, S. A., Jung, S., Almerekhi, H., & Jansen, B. J. (2020). Developing an online hate classifier for multiple social media platforms. Human-Centric Computing and Information Sciences, 10(1), 1. https://doi.org/10.1186/s13673-019-0205-6
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
Wu, M., Chen, Y., Lin, H., Zhao, L., Shen, L., Li, R., Xu, Y., Hong, H., & He, Y. (2020). Membrane fouling caused by biological foams in a submerged membrane bioreactor: Mechanism insights. Water Research, 181, 115932. https://doi.org/10.1016/j.watres.2020.115932
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
Copyright (c) 2023 Sinta Sri Rahayu, Imam Tabroni, Jayshree Martin, Wang Fang

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.