The Effectiveness of STEAM-Based Learning in Enhancing Early Childhood Creativity in the Deep Learning Era
DOI:
https://doi.org/10.31958/jies.v6i2.17381Keywords:
Early Childhood, Deep Learning, Creativity, STEAMAbstract
The digital era, marked by rapid advances in artificial intelligence and deep learning technologies, has transformed the paradigm of early childhood education, creating a need for learning models that effectively foster creativity as a key twenty-first-century competency. This study aimed to analyze the effectiveness of the Science, Technology, Engineering, Art, and Mathematics (STEAM) learning model in enhancing early childhood creativity through project-based activities integrating science, technology, art, and simple engineering concepts. A quantitative approach with a quasi-experimental non-equivalent control group design was employed involving 30 children aged 5–6 years, consisting of 15 children in the experimental group and 15 children in the control group from a kindergarten. The experimental group participated in STEAM-based learning, while the control group received conventional instruction. The findings revealed that the STEAM learning model was effective in improving children's creativity. MANCOVA analysis indicated that pretest scores had a significant effect on posttest outcomes (p < 0.001), whereas parents’ educational background showed no significant influence on creativity development or intervention outcomes. These results demonstrate that interactive, collaborative, and project-based STEAM learning encourages children to become more active, enthusiastic, innovative, and creative in expressing their ideas. Therefore, the STEAM learning model is recommended as an effective strategy for implementing the Merdeka Curriculum and strengthening creativity and critical thinking skills in early childhood education
References
Ahmed, I., & Yadav, P. K. (2023). Plant disease detection using machine learning approaches. Expert Systems, 40(5), e13136. https://doi.org/10.1111/exsy.13136
Alkhatib, R., Sahwan, W., Alkhatieb, A., & Schütt, B. (2023). A Brief Review of Machine Learning Algorithms in Forest Fires Science. Applied Sciences, 13(14), 8275. https://doi.org/10.3390/app13148275
Bañuelos, J. L., Borguet, E., Brown, G. E., Cygan, R. T., DeYoreo, J. J., Dove, P. M., Gaigeot, M.-P., Geiger, F. M., Gibbs, J. M., Grassian, V. H., Ilgen, A. G., Jun, Y.-S., Kabengi, N., Katz, L., Kubicki, J. D., Lützenkirchen, J., Putnis, C. V., Remsing, R. C., Rosso, K. M., … Zhang, H. (2023). Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment. Chemical Reviews, 123(10), 6413–6544. https://doi.org/10.1021/acs.chemrev.2c00130
Biswas, S. S. (2023). Potential Use of Chat GPT in Global Warming. Annals of Biomedical Engineering, 51(6), 1126–1127. https://doi.org/10.1007/s10439-023-03171-8
Cao, B., Wang, Z., Zhang, L., Feng, D., Peng, M., Zhang, L., & Han, Z. (2023). Blockchain Systems, Technologies, and Applications: A Methodology Perspective. IEEE Communications Surveys & Tutorials, 25(1), 353–385. https://doi.org/10.1109/COMST.2022.3204702
Carrera, E., & Zozulya, V. V. (2024a). Carrera Unified Formulation (CUF) for the composite shells of revolution. Equivalent single layer models. Mechanics of Advanced Materials and Structures, 31(1), 22–44. https://doi.org/10.1080/15376494.2023.2218380
Carrera, E., & Zozulya, V. V. (2024b). Carrera unified formulation (CUF) for the shells of revolution. Numerical evaluation. Mechanics of Advanced Materials and Structures, 31(7), 1597–1619. https://doi.org/10.1080/15376494.2022.2140234
Chakraborty, T., Reddy K S, U., Naik, S. M., Panja, M., & Manvitha, B. (2024). Ten years of generative adversarial nets (GANs): A survey of the state-of-the-art. Machine Learning: Science and Technology, 5(1), 011001. https://doi.org/10.1088/2632-2153/ad1f77
Chen, L., Sun, S., Gao, Y., & Ran, X. (2023). Global mortality of diabetic foot ulcer: A systematic review and meta‐analysis of observational studies. Diabetes, Obesity and Metabolism, 25(1), 36–45. https://doi.org/10.1111/dom.14840
Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y
Cossich, V. R. A., Carlgren, D., Holash, R. J., & Katz, L. (2023). Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis. Applied Sciences, 13(23), 12965. https://doi.org/10.3390/app132312965
Darmawansah, D., Hwang, G.-J., Chen, M.-R. A., & Liang, J.-C. (2023). Trends and research foci of robotics-based STEM education: A systematic review from diverse angles based on the technology-based learning model. International Journal of STEM Education, 10(1), 12. https://doi.org/10.1186/s40594-023-00400-3
Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jiang, J., Zhu, Y., Liu, J., Zhang, B., & Wei, G.-W. (2023). Machine Learning Methods for Small Data Challenges in Molecular Science. Chemical Reviews, 123(13), 8736–8780. https://doi.org/10.1021/acs.chemrev.3c00189
Eggmann, F., Weiger, R., Zitzmann, N. U., & Blatz, M. B. (2023). Implications of large language models such as CHATGPT for dental medicine. Journal of Esthetic and Restorative Dentistry, 35(7), 1098–1102. https://doi.org/10.1111/jerd.13046
Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating Academic Answers Generated Using ChatGPT. Journal of Chemical Education, 100(4), 1672–1675. https://doi.org/10.1021/acs.jchemed.3c00087
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16(1), 45–74. https://doi.org/10.1007/s12559-023-10179-8
Janga, B., Asamani, G., Sun, Z., & Cristea, N. (2023). A Review of Practical AI for Remote Sensing in Earth Sciences. Remote Sensing, 15(16), 4112. https://doi.org/10.3390/rs15164112
Kashyap, R. (2023). Histopathological image classification using dilated residual grooming kernel model. International Journal of Biomedical Engineering and Technology, 41(3), 272. https://doi.org/10.1504/IJBET.2023.129819
Korzynski, P., Mazurek, G., Krzypkowska, P., & Kurasinski, A. (2023). Artificial intelligence prompt engineering as a new digital competence: Analysis of generative AI technologies such as ChatGPT. Entrepreneurial Business and Economics Review, 11(3), 25–37. https://doi.org/10.15678/EBER.2023.110302
Lee, H. (2024). The rise of CHATGPT: Exploring its potential in medical education. Anatomical Sciences Education, 17(5), 926–931. https://doi.org/10.1002/ase.2270
Li, C., Wang, P., Martin-Moratinos, M., Bella-Fernández, M., & Blasco-Fontecilla, H. (2024). Traditional bullying and cyberbullying in the digital age and its associated mental health problems in children and adolescents: A meta-analysis. European Child & Adolescent Psychiatry, 33(9), 2895–2909. https://doi.org/10.1007/s00787-022-02128-x
Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31(7), 4270–4296. https://doi.org/10.1080/10494820.2021.1958348
Marcinkevičs, R., & Vogt, J. E. (2023). Interpretable and explainable machine learning: A methods‐centric overview with concrete examples. WIREs Data Mining and Knowledge Discovery, 13(3), e1493. https://doi.org/10.1002/widm.1493
Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., Silva, I., Magalhães, P., Schmidt, S., & Vale, N. (2024). Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare. Pharmaceutics, 16(3), 332. https://doi.org/10.3390/pharmaceutics16030332
Mashala, M. J., Dube, T., Mudereri, B. T., Ayisi, K. K., & Ramudzuli, M. R. (2023). A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments. Remote Sensing, 15(16), 3926. https://doi.org/10.3390/rs15163926
Messeri, L., & Crockett, M. J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627(8002), 49–58. https://doi.org/10.1038/s41586-024-07146-0
Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024a). Empowering student self‐regulated learning and science education through CHATGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024b). Empowering student self‐regulated learning and science education through CHATGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454
Omrani, N., Rejeb, N., Maalaoui, A., Dabić, M., & Kraus, S. (2024). Drivers of Digital Transformation in SMEs. IEEE Transactions on Engineering Management, 71, 5030–5043. https://doi.org/10.1109/TEM.2022.3215727
Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V., Sharma, A., Arya, V. M., Kirkham, M. B., Hou, D., Bolan, N., & Chung, Y. S. (2024). Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in Environmental Science, 12, 1336088. https://doi.org/10.3389/fenvs.2024.1336088
Qureshi, R., Irfan, M., Ali, H., Khan, A., Nittala, A. S., Ali, S., Shah, A., Gondal, T. M., Sadak, F., Shah, Z., Hadi, M. U., Khan, S., Al-Tashi, Q., Wu, J., Bermak, A., & Alam, T. (2023). Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review. IEEE Access, 11, 61600–61620. https://doi.org/10.1109/ACCESS.2023.3285596
Ren, Y., Xiao, Y., Zhou, Y., Zhang, Z., & Tian, Z. (2023). CSKG4APT: A Cybersecurity Knowledge Graph for Advanced Persistent Threat Organization Attribution. IEEE Transactions on Knowledge and Data Engineering, 35(6), 5695–5709. https://doi.org/10.1109/TKDE.2022.3175719
Shi, Y., Ma, D., Zhang, J., & Chen, B. (2023a). In the digital age: A systematic literature review of the e-health literacy and influencing factors among Chinese older adults. Journal of Public Health, 31(5), 679–687. https://doi.org/10.1007/s10389-021-01604-z
Shi, Y., Ma, D., Zhang, J., & Chen, B. (2023b). In the digital age: A systematic literature review of the e-health literacy and influencing factors among Chinese older adults. Journal of Public Health, 31(5), 679–687. https://doi.org/10.1007/s10389-021-01604-z
Stade, E. C., Stirman, S. W., Ungar, L. H., Boland, C. L., Schwartz, H. A., Yaden, D. B., Sedoc, J., DeRubeis, R. J., Willer, R., & Eichstaedt, J. C. (2024). Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation. Npj Mental Health Research, 3(1), 12. https://doi.org/10.1038/s44184-024-00056-z
Sun, J., Li, C., Wang, Z., & Wang, Y. (2024). A Memristive Fully Connect Neural Network and Application of Medical Image Encryption Based on Central Diffusion Algorithm. IEEE Transactions on Industrial Informatics, 20(3), 3778–3788. https://doi.org/10.1109/TII.2023.3312405
Syamala, M., C. R., K., Pramila, P. V., Dash, S., Meenakshi, S., & Boopathi, S. (2023). Machine Learning-Integrated IoT-Based Smart Home Energy Management System: In P. Swarnalatha & S. Prabu (Eds.), Advances in Computational Intelligence and Robotics (pp. 219–235). IGI Global. https://doi.org/10.4018/978-1-6684-8098-4.ch013
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023). A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspectives on Psychological Science, 18(5), 1062–1096. https://doi.org/10.1177/17456916221134490
Vaitkus, A., Merkys, A., Sander, T., Quirós, M., Thiessen, P. A., Bolton, E. E., & Gražulis, S. (2023). A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database. Journal of Cheminformatics, 15(1), 123. https://doi.org/10.1186/s13321-023-00780-2
Van Noorden, R., & Perkel, J. M. (2023). AI and science: What 1,600 researchers think. Nature, 621(7980), 672–675. https://doi.org/10.1038/d41586-023-02980-0
Wu, M., Tikhonov, E., Tudi, A., Kruglov, I., Hou, X., Xie, C., Pan, S., & Yang, Z. (2023). Target‐Driven Design of Deep‐UV Nonlinear Optical Materials via Interpretable Machine Learning. Advanced Materials, 35(23), 2300848. https://doi.org/10.1002/adma.202300848
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850. https://doi.org/10.1038/s41562-024-02004-5
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
Zimmermann, R., Mora, D., Cirqueira, D., Helfert, M., Bezbradica, M., Werth, D., Weitzl, W. J., Riedl, R., & Auinger, A. (2023). Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence. Journal of Research in Interactive Marketing, 17(2), 273–298. https://doi.org/10.1108/JRIM-09-2021-0237
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