Abstract
Gross motor skills are a fundamental resource in early childhood care, which requires planning in the activities to be developed with infants who attend the service units (UDS) belonging to the Colombian Institute of Family Welfare (ICBF) in the municipality of Fusagasugá-Colombia. With the implementation of a custom-designed mobile application in a medium that lacked this resource, it turns out to be invaluable not only as an assistance tool, but also as a support for the service units of the region, helping teachers and community mothers to plan their daily tasks. The design of the application is based on the Mobile-D methodology, characterized by being flexible and adaptable to the changing needs of users, providing rapid development and iteration with them. The results show that the application facilitates the management of pedagogical resources and registration forms of the activities that must be permanently completed. The design, development and execution of the pedagogical resources managed through the application facilitate academic and administrative processes that were previously carried out manually. According to the conclusions, the mobile application met the requirements using tools such as React Native, Sequelize and Express, facilitating its planning and execution. The lessons learned highlight the importance of involving users in the planning and the appropriate choice of tools to increase the accessibility and reach of the application.
References
Barooni, M., Ziarati, K., & Barooni, A. (2023). Frost Prediction Using Machine Learning Methods in Fars Province. 2023 28th International Computer Conference, Computer Society of Iran, CSICC 2023. https://doi.org/10.1109/CSICC58665.2023.10105391
Castillo Méndez, R. (2023). Clasificación automática de defectos de paneles solares aplicando aprendizaje de máquina. Revista Teinnova, 7, 89–97. https://doi.org/10.23850/25007211.5750
Corporación Autónoma Regional de Cundinamarca (CAR). (2020). Informe del registro e impactos de heladas en el territorio CAR durante Enero de 2020
Cunha, R. L. F., Silva, B., & Netto, M. A. S. (2018). A scalable machine learning system for pre-season agriculture yield forecast. Proceedings - IEEE 14th International Conference on EScience, e-Science 2018, 423–430. https://doi.org/10.1109/eScience.2018.00131
García Cañón, H. S. (2019). Implementación de técnicas de machine learning para la predicción de variables meteorológicas y del suelo que afectan la agricultura. In Implementación de técnicas de machine learning para la predicción de variables meteorológicas y del suelo que afectan la agricultura. Universidad de los Andes.
Gonzalez Botero, D. F. (2018). Planteamiento de un modelo de predicción de heladas en cultivos de rosa en la sabana de Bogotá. Universidad Militar Nueva Granada.
Hurwitz, J., & Kirsch, D. (2018). Machine Learning. In Journal of the American Society for Information Science (Vol. 35, Issue 5). John Wiley & Sons, Inc.
Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics (T. M. Press, Ed.). The MIT Press.
Marqués Gozalbo, M. Á. (2020). Modelos predictivos de producción agroindustrial con Machine Learning a partir de fuentes de información pública. Universidad de Córdoba.
Ramírez Gómez, C. A. (2020). Aplicación del Machine Learning en agricultura de precisión. Revista Cintex, 25(2), 14–27.
Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine Learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415
Snyder, R. L., de Melo-Abreu, J. Paulo., & Matulich, Scott. (2010). Protección contra las heladas: fundamentos, práctica y economía (2nd ed.). Organización de las Naciones Unidas para la Agricultura y la Alimentación.

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