Personalización de programas de capacitación y desarrollo mediante algoritmos de aprendizaje automático
Customization of training and development programs through machine learning algorithmsBarra lateral del artículo
Contenido principal del artículo
La personalización de programas de capacitación y desarrollo mediante algoritmos de aprendizaje automático en la actualidad ha sido utilizada en distintas disciplinas, pero el entendimiento de estos programas y sus algoritmos para su aprendizaje automático siguen siendo un área de suma importancia. El objetivo del estudio fue el análisis de programas y algoritmos de aprendizaje automático. La revisión sistemática a la cual fue llevada es el método PRISMA. Las fuentes de información fueron las bases de datos de Scopus y Scielo. Los operadores booleanos fueron AND y OR. El número total de estudios hallados fueron 2223, de los cuales se lograron seleccionar 55 artículos de India, Iraq, Países bajos, Brasil, China, Canadá, Reino Unido, Tailandia, Indonesia, Inglaterra, Corea del Sur, Estados Unidos, Reino Unido, Grecia, Túnez, Países bajos, Rusia, Australia, Suiza, Turquía, Dinamarca, Sudáfrica, Grecia, Australia, Pakistán, Finlandia, España e Irán. Según los resultados, la personalización de programas de capacitación y desarrollo mediante algoritmos de aprendizaje tienen un impacto positivo en la actualidad ya que favorece el aprendizaje constante, optimiza nuestro tiempo de trabajo y nos adapta a los cambios y tendencias.
The customization of training and development programs using machine learning algorithms has been used in different disciplines, but the understanding of these programs and their machine learning algorithms is still an area of great importance. The objective of the study was to analyze the various existing programs, their operation and their purpose in training. The systematic review was carried out using the PICO method. The sources of information were the Scopus and Scielo databases. The Boolean operators were AND and OR. The total number of studies found was 80, of which 40 articles were selected from the United States, Thailand, United Kingdom, India, Netherlands, England, South Korea, Indonesia, Iraq, China and Russia. According to the results, the customization of training and development programs through learning algorithms has a positive impact nowadays as it favors constant learning, optimizes our working time and adapts us to changes and trends.
Descargas
Detalles del artículo
Abood, M., y Abdul-Majeed, G. H. (2025). Enhancing Multi-Class DDoS Attack Classification using Machine Learning Techniques. Journal of Advanced Research in Applied Sciences and Engineering Technology, 43(2), 75–92. https://doi.org/10.37934/araset.43.2.7592
Andi, A., Ainun, A., Armin, L y Edy, R. (2024). Ensemble Transfer Learning for Hand-sign Digit Image Classification. Journal of Advanced Research in Applied Sciences and Engineering Technology, 43(1), 95–111. https://doi.org/10.37934/araset.43.1.95111
Ataullah, I., y Livesey, A. (2024). Maintaining High Professional Standards, morally, ethically and fairly: what doctors need to know right now. https://doi.org/10.1136/postgradmedj-2020
Banarase, S. J., y Shirbahadurkar, S. (2024). Orchard Guard: Deep Learning powered apple leaf disease detection with MobileNetV2 model. In Pg 1 J. Integr. Sci. Technol. 2024 (4). http://pubs.thesciencein.org/jist
Carini, C., y Seyhan, A. A. (2024). Tribulations and future opportunities for artificial intelligence in precision medicine. In Journal of Translational Medicine; 22, (1). BioMed Central Ltd. https://doi.org/10.1186/s12967-024-05067-0
Cascón-Katchadourian, J., Rodríguez-Domínguez, C., Carranza-García, F., y Torres-Salinas, D. (2023). GeoAcademy: web platform and algorithm for automatic detection and location of geographic coordinates and toponyms in scientific articles. Revista Espanola de Documentacion Cientifica, 46(4). https://doi.org/10.3989/redc.2023.4.1393
Choi, I., Kwon, S., Rojewski, J. W., Hill, J. R., Kim, E. S., Fisher, E., Thomas, R. S., y McCauley, L. (2024). Conceptualization, development, and early dissemination of eMPACTTM: A competency-based career navigation system for translational research professionals. Journal of Clinical and Translational Science, 8(1). https://doi.org/10.1017/cts.2023.693
Daza, A., Miranda, J., Cornelio, J. B., López, A. R., y Ponce, C. F. (2023). Predicting the depression in university students using stacking ensemble techniques over oversampling method. Informatics in Medicine Unlocked, 41. https://doi.org/10.1016/j.imu.2023.101295
Deng, M., Liu, Y., y Chen, L. (2023). AI-driven innovation in ethnic clothing design: an intersection of machine learning and cultural heritage. Electronic Research Archive, 31(9), 5793–5814. https://doi.org/10.3934/era.2023295
du Gay, P y Lopdrup Hjorth, T. (2024). Organizing States: The continuing relevance of formal organization within political administration. Organization Theory, 5(1). https://doi.org/10.1177/26317877241235944
Guo, Z., Xu, X y Chen, X. (2024). Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank. Computer Systems Science and Engineering, 35(7). https://doi.org/10.32604/csse.2023.043932
Gupta, N., Hayder, Z., Norris, R. P., Huynh, M., y Petersson, L. (2024). RadioGalaxyNET: Dataset and novel computer vision algorithms for the detection of extended radio galaxies and infrared hosts. Publications of the Astronomical Society of Australia, 41. https://doi.org/10.1017/pasa.2023.64
Gupta, R., Rajkumar, A., y Beemkumar, N. (2023). Predicting software defects with swarm-intelligence-based machine learning algorithm for improved process quality. Multidisciplinary Science Journal, 5. https://doi.org/10.31893/multiscience.2023ss0311
Huo, C., Husnain, M., y Guo, F. (2023). Ethical leadership and workplace behavior in the education sector: The implications of employees’ ethical work behavior.
Kaffashpoor, A., y Sadeghian, S. (2020). The effect of ethical leadership on subjective wellbeing, given the moderator job satisfaction (a case study of private hospitals in Mashhad). BMC Nursing, 19(1). https://doi.org/10.1186/s12912-020-00496-w
Karali, N., Mastrokoukou, S., y Livas, C. (2023). Mindful minds and entrepreneurial spirits in higher education: a scoping review Introduction: Mindfulness at Higher Education Institutions (HEIs) may enhance personal development, learning, and entrepreneurial thinking. Thus, this scoping review investigates the effects of mindfulness on HEI entrepreneurship education, focusing on teaching, learning, and entrepreneurial intention. https://doi.org/10.17605/OSF.IO/YJTA
Khammarnia, M., Golestani, Z. S., Ranjbar, A. A., Peyvand, M., Khorram, A., y Setoodehzadeh, F. (2022). Relationship of Information Literacy and Professional Ethics with Career Development. Shiraz E Medical Journal, 23(9). https://doi.org/10.5812/semj-111166
Khan, H. S., Siddiqui, S. H., Zhiqiang, M., Weijun, H., y Mingxing, L. (2021). “Who Champions or Mentors Others”? The Role of Personal Resources in the Perceived Organizational Politics and Job Attitudes Relationship. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.609842
Khan, M., Mahmood, A., y Shoaib, M. (2022). Role of Ethical Leadership in Improving Employee Outcomes through the Work Environment, Work-Life Quality and ICT Skills: A Setting of China-Pakistan Economic Corridor. In Sustainability (Switzerland); 14, (17). MDPI. https://doi.org/10.3390/su141711055
Kim, M. J., y Kim, B. J. (2020). The performance implications of job insecurity: The sequential mediating effect of job stress and organizational commitment, and the buffering role of ethical leadership. International Journal of Environmental Research and Public Health, 17(21), 1–16. https://doi.org/10.3390/ijerph17217837
Kooptiwoot, S., Kooptiwoot, S., y Javadi, B. (2024). Application of regression decision tree and machine learning algorithms to examine students’ online learning preferences during COVID-19 pandemic. International Journal of Education and Practice, 12(1), 82–94. https://doi.org/10.18488/61.v12i1.3619
Lee, H., Kim, J., y Jung, H. S. (2024). Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-61106-2
Maki, P.L., Shea, P., Alexander, B. (2023). Advance praise for Transforming Digital Learning and Assessment.
Moscol-Albañil, I., Solórzano-Requejo, W., Rodriguez, C., Ojeda, C., y Díaz, A. (2024a). Innovative AI-driven design of patient-specific short femoral stems in primary hip arthroplasty. Materials and Design, 240. https://doi.org/10.1016/j.matdes.2024.112868
Mumtaz, S., Tariq, M., y Abbas, M. (2023). ¿Do ethical values buffer against workplace stressors? Interactive effects of challenge-hindrance stressors and Islamic work ethics on individualized change outcomes. Estudios de Psicologia, 44(2–3), 321–352. https://doi.org/10.1080/02109395.2023.2252253
Ninno, F., Tsui, J., Balabani, S., y Díaz-Zuccarini, V. (2023). A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries. In JVS-Vascular Science; 4. Elsevier Inc. https://doi.org/10.1016/j.jvssci.2023.100128
Oh, J., Yeom, J., Madika, B., Kim, K. M., Liow, C. H., Agar, J. C., y Hong, S. (2024). Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images. Npj Computational Materials, 10(1). https://doi.org/10.1038/s41524-024-01279-6
Pham, T., Ghafoor, M., Grañana-Castillo, S., Marzolini, C., Gibbons, S., Khoo, S., Chiong, J., Wang, D., y Siccardi, M. (2024). DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy. Npj Systems Biology and Applications, 10(1). https://doi.org/10.1038/s41540-024-00374-0
Pinto-Molina, M., Caballero-Mariscal, D., y García-Marco, F. J. (2021). Assessment of the implementation of the mobile apps in Spanish Universities. Revista Espanola de Documentacion Cientifica, 44(1), 1–19. https://doi.org/10.3989/redc.2021.1.1755
Qu, P., Yuan, Q., Du, F., y Gao, Q. (2024). An improved manta ray foraging optimization algorithm. Qu, P., Yuan, Q., Du, F. et al. An Improved Manta Ray Foraging Optimization Algorithm. Sci Rep 14, 10301, 14(1). https://doi.org/10.1038/s41598-024-59960-1
Radjab, E., Tjambolang, T. A., Tang, M., Meiniza, Y., Amiruddin, A., Mandasari, N. F., y Sabbar, S. D. (2024). Beyond ethics: How perceived organizational support amplifies the impact of ethical HRM on employee commitment and performance. Journal of Infrastructure, Policy and Development, 8(4). https://doi.org/10.24294/jipd.v8i4.3352
Rao, D. P., Savoy, F. M., Tan, J., Fung, B., Bopitiya, C. M., Sivaraman, A., y Vinekar, A. (2023). Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population. Frontiers in Pediatrics, 11. https://doi.org/10.3389/fped.2023.1197237
Ruzicki, J., Holden, M., Cheon, S., Ungi, T., Egan, R., y Law, C. (2023). Use of Machine Learning to Assess Cataract Surgery Skill Level With Tool Detection. Ophthalmology Science, 3(1). https://doi.org/10.1016/j.xops.2022.100235
Shiva, V., y Kathiravan, M. (2024). Virtual Reality Technology and Artificial Intelligence for Television and Film Animation. Journal of Advanced Research in Applied Sciences and Engineering Technology, 43(1), 263–273. https://doi.org/10.37934/araset.43.1.263273
Silva, A. D., da, Gomes, M. F. da C., Gregianini, T. S., Martins, L. G., y Veiga, A. B. G. da. (2024). Machine learning in predicting severe acute respiratory infection outbreaks. Cadernos de Saude Publica, 40(1). https://doi.org/10.1590/0102-311XEN122823
Sirivella, Y., e Jain, A. (2023). A Hybrid Metaheuristic Algorithm for Diseases Classification Using UAV Images. Journal of Computer Science, 19(10), 1231–1242. https://doi.org/10.3844/jcssp.2023.1231.1241
Sivapurnima, S., y Manjula, D. (2023). Adaptive Deep Learning Model for Software Bug Detection and Classification. Computer Systems Science and Engineering, 45(2), 1233–1248. https://doi.org/10.32604/csse.2023.025991
Syahrizal, S., Yasmi, F., y Mary, T. (2024). AI-Enhanced Teaching Materials for Education: A Shift Towards Digitalization. International Journal of Religion, 5(1), 203–217. https://doi.org/10.61707/j6sa1w36
van Kooten, M. J., Tan, C. O., Hofmeijer, E. I. S., van Ooijen, P. M. A., Noordzij, W., Lamers, M. J., Kwee, T. C., Vliegenthart, R., y Yakar, D. (2024). A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist. Insights into Imaging, 15(1). https://doi.org/10.1186/s13244-023-01595-3
Varzakas, T., y Antoniadou, M. (2024). A Holistic Approach for Ethics and Sustainability in the Food Chain: The Gateway to Oral and Systemic Health. In Foods (Vol. 13, Issue 8). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/foods13081224
Wang, Y., Zhang, H., Xu, Z., Zhang, S., y Guo, R. (2023). TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots. Mathematical Biosciences and Engineering, 20(11), 19320–19340. https://doi.org/10.3934/mbe.2023854
Wang, Z., Ren, S., Chadee, D., y Chen, Y. (2024). Employee Ethical Silence Under Exploitative Leadership: The Roles of Work Meaningfulness and Moral Potency. Journal of Business Ethics, 190(1), 59–76. https://doi.org/10.1007/s10551-023-05405-0
Wu, H., Xing, T., Li, W., Chen, H., Kang, Y., y Li, J. (2023). Robotic Scheduling Strategies based on Machine Learning Algorithm. Journal of Physics: Conference Series, 2562(1). https://doi.org/10.1088/1742-6596/2562/1/012025
Zhang, Q., Cheng, J., Zhou, C., Jiang, X., Zhang, Y., Zeng, J., y Liu, L. (2023). PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation. Frontiers in Physiology, 14. https://doi.org/10.3389/fphys.2023.1259877
Zviyita, I., y Mare, A. (2024). Ethical Issues Confronting Namibian Hybrid Media Organizations in the Digital Age. Journalism and Mass Communication Quarterly. https://doi.org/10.1177/10776990241240118