PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto
Introducción: Este artículo presenta el sistema PREMUCC, una propuesta basada en microservicios para predecir la deserción escolar consciente del contexto en educación superior. La deserción estudiantil es una preocupante problemática que se aborda con esta innovadora solución, permitiendo un seguim...
- Autores:
-
Arenales, Laury
Gonzalez, Joseph
Saldana-Barrios, Juan Jose
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13905
- Palabra clave:
- school dropout
microservices
context-awareness
prediction
intelligent system
deserción escolar
microservicios
consciencia de contecto
predicción
sistema inteligente
- Rights
- openAccess
- License
- Inge CuC - 2024
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PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
| dc.title.translated.eng.fl_str_mv |
PREMUCC: Design proposal for a microservices architecture for the development of a context-aware dropout prediction system |
| title |
PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
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PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto school dropout microservices context-awareness prediction intelligent system deserción escolar microservicios consciencia de contecto predicción sistema inteligente |
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PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
| title_full |
PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
| title_fullStr |
PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
| title_full_unstemmed |
PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
| title_sort |
PREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contexto |
| dc.creator.fl_str_mv |
Arenales, Laury Gonzalez, Joseph Saldana-Barrios, Juan Jose |
| dc.contributor.author.spa.fl_str_mv |
Arenales, Laury Gonzalez, Joseph Saldana-Barrios, Juan Jose |
| dc.subject.eng.fl_str_mv |
school dropout microservices context-awareness prediction intelligent system |
| topic |
school dropout microservices context-awareness prediction intelligent system deserción escolar microservicios consciencia de contecto predicción sistema inteligente |
| dc.subject.spa.fl_str_mv |
deserción escolar microservicios consciencia de contecto predicción sistema inteligente |
| description |
Introducción: Este artículo presenta el sistema PREMUCC, una propuesta basada en microservicios para predecir la deserción escolar consciente del contexto en educación superior. La deserción estudiantil es una preocupante problemática que se aborda con esta innovadora solución, permitiendo un seguimiento personalizado y toma de decisiones informada. Objetivo: El objetivo del sistema PREMUCC es anticipar la deserción escolar universitaria mediante el uso de técnicas de aprendizaje de máquina y una arquitectura de microservicios, brindando apoyo personalizado a los estudiantes y facilitando la gestión educativa. Metodología: Se fundamentó la propuesta PREMUCC a partir de un análisis de sistemas de predicción de deserción y arquitecturas de microservicios. Se identificaron tecnologías y procesos de análisis de datos para garantizar la eficiencia y precisión del sistema. Resultados: El sistema PREMUCC integra seis microservicios que abarcan autenticación de usuarios, análisis de estudios previos, pruebas psicológicas, seguimiento de calificaciones, evaluación económica y el sistema de predicción. Los estudiantes acceden a información personalizada para mejorar su rendimiento académico. Conclusiones: La propuesta PREMUCC representa un avance significativo para mitigar la deserción escolar universitaria. Su enfoque en microservicios y tecnologías modernas permite un seguimiento más cercano al contexto de cada estudiante, respaldando la toma de decisiones informada. Su implementación puede mejorar el sistema educativo y ahorrar recursos, tiempo y esfuerzo. Se propone fortalecer la arquitectura y evaluar su efectividad en entornos reales de educación superior. |
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2024-10-21 19:36:40 2024-12-13T08:30:12Z |
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2024-10-21 |
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Artículo de revista |
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eng |
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Inge CuC |
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G. E. Reyes, D. V. Sosa, M. G. Quispe, and I. Iraola-Real, “Family, economic and pedagogical factors involved in dropping out of school and the consequences for students of a private institution in Lima - Peru,” in Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/SHIRCON53068.2021.9652381. [2] Y. V. F. Vizcaino, O. A. Z. Duran, R. O. D. Garcia, and M. Gonzalez Duenas, “Architecture of an Automated System for the Monitoring and PreventiÃn of School DropOut,” in Applications in Software Engineering - Proceedings of the 9th International Conference on Software Process Improvement, CIMPS 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 71–77. doi: 10.1109/CIMPS52057.2020.9390097. [3] V. Tinto, “Dropout from Higher Education: A Aeoretical Synthesis of Recent Research. Review of Educational Research,” vol. 43(1), pp. 89–125. [4] M. MacEdo et al., “Investigation of college dropout with the fuzzy c-means algorithm,” in Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, Institute of Electrical and Electronics Engineers Inc., Jul. 2019, pp. 187–189. doi: 10.1109/ICALT.2019.00055. [5] E. Vanegas, Y. Salazar, P. Lerma, and I. Plaza, “Impact of the tutoring program on engineering student dropout,” 2022. [6] T. Morrow, A Context-Aware Ontology forPersonalized Learning: Pervasive Computing forEducational Technology. [7] Á. Rabelo Lopes, R. De Sousa, D. Carvalho, and R. Vaccare, “Context-aware Ubiquitous Learning Literature Systematic Mapping on Ubiquitous Learning Environments,” International Symposium on Computers in Education (SIIE), pp. 1–6, 2017, doi: 10.1109/SIIE.2017.8259662. [8] N. Harrati, I. Bouchrika, A. Tari, and A. Ladjaila, “Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis,” Comput Human Behav, vol. 61, pp. 463–471, 2016. [9] C. Bellei, Situación Educativa de América Latina y el Caribe: Hacia la educación de calidad para todos al 2015. Santiago de Chile: Oficina Regional de Educación para América Latina y el Caribe (OREALC/UNESCO Santiago)), 2013. [10] Á. Hernández Prados, J. Santiago, Á. Muñoz, and A. A. Martínez, “The Issue of school dropout in scientific production activity,” International Journal of Social Sciences and Humanities, vol. XXVI, no. 1, pp. 89–112, 2017. [11] “Introducción a la Universidad Tecnológica de Panamá | Universidad Tecnológica de Panamá.” https://utp.ac.pa/introduccion-la-universidad-tecnologica-de-panama (accessed Oct. 11, 2022). [12] “Instituto Nacional de Estadística y Censo.” https://www.inec.gob.pa/buscador/Default.aspx (accessed Oct. 11, 2022). [13] Banco Interamericano de Desarrollo, “DIAGNÓSTICO DE LA EDUCACIÓN SUPERIOR EN PANAMÁ: RETOS Y OPORTUNIDADES,” 2021. [14] P. Díaz and A. Tejedor, “Factores asociados al abandono. Tipos y perfiles de abandono,” 2018. [15] World Bank, “Acting Now to Protect the Human Capital of Our Children; Actuemos ya para Proteger el Capital Humano de Nuestros Niños; Agir aujourd’hui pour protéger le capital humain de nos enfants : The Costs of and Response to COVID-19 Pandemic’s Impact on the Education Sector in Latin America and the Caribbean,” 2021. [Online]. Available: www.worldbank.org [16] Y. Velazco Flórez, A. Abuchar Porras, I. Castilla, and K. Rivera, “E-Learning: Rompiendo Fronteras.” [Online]. Available: http://revistas.udistrital.edu.co/ojs/index.php/REDES/index [17] A. Naik, J. Choudhari, V. Pawar, and S. Shitole, “Building an EdTech Platform Using Microservices and Docker,” in 2021 IEEE Pune Section International Conference, PuneCon 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/PuneCon52575.2021.9686535. [18] H. Calderon-Gomez et al., “Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture,” IEEE Access, vol. 8, pp. 118340–118354, 2020, doi: 10.1109/ACCESS.2020.3005638. [19] Á. R. Lopes, D. C. de Oliveira, R. C. de Sousa Aguiar, and R. T. Vaccare Braga, “Context-aware ubiquitous learning: Literature systematic mapping on ubiquitous learning environments,” in 2017 International Symposium on Computers in Education (SIIE), 2017, pp. 1–6. doi: 10.1109/SIIE.2017.8259662. [20] Z. Yu, X. Zhou, and L. Shu, “Towards a Semantic Infrastructure for Context-Aware e-Learning,” Multimedia Tools Appl., vol. 47, no. 1, pp. 71–86, Mar. 2010, doi: 10.1007/s11042-009-0407-4. [21] T. T. Wu, T. C. Yang, G. J. Hwang, and H. N. Chu, “Conducting situated learning in a context-aware ubiquitous learning environment,” in Proceedings - 5th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education, WMUTE 2008, 2008, pp. 82–86. doi: 10.1109/WMUTE.2008.9. [22] A. R. Hurson and S. S. Sarvestani, “PERCEPOLIS: Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support,” Intell. Inf. Manag., vol. 2, pp. 586–596, 2010. [23] S. Dowdy, S. Wearden, and D. Chilko, “STATISTICS FOR RESEARCH THIRD EDITION,” John Wiley & Sons, vol. 3, 2004. [24] J. Y. K. Yau and M. Joy, “A context-aware personalized m-learning application based on m-learning preferences,” in 6th IEEE International Conference on Wireless, Mobile and Ubiquitous Technologies in Education, WMUTE 2010: Mobile Social Media for Learning and Education in Formal and Informal Settings, 2010, pp. 11–18. doi: 10.1109/WMUTE.2010.15. [25] Y. Shulin and H. Jieping, “Design and Implementation of Smart Teaching System Based on Microservice Architecture,” Institute of Electrical and Electronics Engineers (IEEE), Mar. 2022, pp. 279–282. doi: 10.1109/icpeca53709.2022.9718846. [26] W. Peishun and W. Pei, “Design of Integrated Teaching System Based on Microservice Architecture,” in Proceedings - 2021 2nd International Conference on Artificial Intelligence and Education, ICAIE 2021, Institute of Electrical and Electronics Engineers Inc., Jun. 2021, pp. 671–674. doi: 10.1109/ICAIE53562.2021.00147. [27] P. E. De la Cruz Vélez de Villa, M. H. Espinoza Ramirez, and O. Cuba Estrella, “Propuesta de arquitectura de microservicios, metodología Scrum para una aplicación móvil de control académico: Caso Escuela Profesional de Obstetricia de la Universidad Nacional Mayor de san Marcos,” HAMUT’AY, vol. 6, no. 2, Aug. 2019, doi: 10.21503/hamu.v6i2.1781. [28] P. Mussida and P. L. Lanzi, “A computational tool for engineer dropout prediction,” in IEEE Global Engineering Education Conference, EDUCON, IEEE Computer Society, 2022, pp. 1571–1576. doi: 10.1109/EDUCON52537.2022.9766632. [29] E. Cevallos Medina, C. Barahona Chunga, J. Armas-Aguirre, and E. Gradón, “Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees,” 5th Iberian Conference on Information Systems and Technologies (CISTI), vol. 15, pp. 1–7, Jun. 2020, doi: 10.23919/CISTI49556.2020.9141095. [30] D. E. Fuster, “3. Investigación cualitativa: Método fenomenológico hermenéutico,” Propósitos y Representaciones, vol. 7, no. 1, pp. 201–229, 2019, [Online]. Available: http://dx.doi.org/10.20511/pyr2019.v7n1.267 [31] H. Zhao, Y. Jiang, and X. Zhao, “Design and research of university intelligent education cloud platform based on Dubbo microservice framework,” in Proceedings - 2020 5th International Conference on Mechanical, Control and Computer Engineering, ICMCCE 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 870–874. doi: 10.1109/ICMCCE51767.2020.00191. [32] P. M. Gee, D. A. Greenwood, D. A. Paterniti, D. Ward, and L. M. S. Miller, “The eHealth Enhanced Chronic Care Model: a theory derivation approach,” J Med Internet Res, vol. 17, no. 4, p. e86, Apr. 2015, doi: 10.2196/JMIR.4067. [33] M. Driss, “WS-ADVISING: a Reusable and reconfigurable microservices‐based platform for effective academic advising,” J Ambient Intell Humaniz Comput, vol. 13, no. 1, pp. 283–294, Jan. 2022, doi: 10.1007/s12652-021-02901-x. [34] D. Guaman, Lady Yaguachi, C. C. Samanta, J. H. Danilo, and F. Soto, “Performance evaluation in the migration process from a monolithic application to microservices,” Iberian Conference on Information Systems and Technologies, CISTI, vol. 2018-June, pp. 1–8, Jun. 2018, doi: 10.23919/CISTI.2018.8399148. [35] I. Semenov, R. Osenev, S. Gerasimov, G. Kopanitsa, D. Denisov, and Y. Andreychuk, “Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support,” International Journal of Environmental Research and Public Health Article, 2019, doi: 10.3390/ijerph17010073. [36] P. Di Francesco, P. Lago, and I. Malavolta, “Architecting with microservices: A systematic mapping study,” Journal of Systems and Software, vol. 150, pp. 77–97, Apr. 2019, doi: 10.1016/J.JSS.2019.01.001. |
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Arenales, LauryGonzalez, JosephSaldana-Barrios, Juan Jose2024-10-21 19:36:402024-12-13T08:30:12Z2024-10-21 19:36:402024-12-13T08:30:12Z2024-10-210122-6517https://hdl.handle.net/11323/13905https://doi.org/10.17981/ingecuc.20.2.2024.0910.17981/ingecuc.20.2.2024.092382-4700Introducción: Este artículo presenta el sistema PREMUCC, una propuesta basada en microservicios para predecir la deserción escolar consciente del contexto en educación superior. La deserción estudiantil es una preocupante problemática que se aborda con esta innovadora solución, permitiendo un seguimiento personalizado y toma de decisiones informada. Objetivo: El objetivo del sistema PREMUCC es anticipar la deserción escolar universitaria mediante el uso de técnicas de aprendizaje de máquina y una arquitectura de microservicios, brindando apoyo personalizado a los estudiantes y facilitando la gestión educativa. Metodología: Se fundamentó la propuesta PREMUCC a partir de un análisis de sistemas de predicción de deserción y arquitecturas de microservicios. Se identificaron tecnologías y procesos de análisis de datos para garantizar la eficiencia y precisión del sistema. Resultados: El sistema PREMUCC integra seis microservicios que abarcan autenticación de usuarios, análisis de estudios previos, pruebas psicológicas, seguimiento de calificaciones, evaluación económica y el sistema de predicción. Los estudiantes acceden a información personalizada para mejorar su rendimiento académico. Conclusiones: La propuesta PREMUCC representa un avance significativo para mitigar la deserción escolar universitaria. Su enfoque en microservicios y tecnologías modernas permite un seguimiento más cercano al contexto de cada estudiante, respaldando la toma de decisiones informada. Su implementación puede mejorar el sistema educativo y ahorrar recursos, tiempo y esfuerzo. Se propone fortalecer la arquitectura y evaluar su efectividad en entornos reales de educación superior.Introduction: This article presents the PREMUCC system, a proposal based on microservices to predict context-aware college dropout in higher education. Student attrition is a concerning issue addressed by this innovative solution, enabling personalized support and informed decision-making. Objective: The objective of the PREMUCC system is to anticipate university dropout through the use of machine learning techniques and a microservices architecture, providing personalized support to students and facilitating educational management. Method: The proposal for PREMUCC was based on analyzing dropout prediction systems and microservices architectures. Technologies and data analysis processes were identified to ensure system efficiency and accuracy. Results: The PREMUCC system integrates six microservices encompassing user authentication, previous studies analysis, psychological testing, grade tracking, financial evaluation, and the prediction system. Students access personalized information to improve their academic performance. Conclusions: The PREMUCC proposal represents a significant advancement in mitigating college dropout. Its focus on microservices and modern technologies enables closer tracking of each student's context, supporting informed decision-making. Its implementation could enhance the educational system, saving resources, time, and effort. It is proposed to strengthen the architecture and evaluate its effectiveness in real higher education settings.application/pdfengUniversidad de la CostaInge CuC - 2024http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/5277school dropoutmicroservicescontext-awarenesspredictionintelligent systemdeserción escolarmicroserviciosconsciencia de contectopredicciónsistema inteligentePREMUCC: Propuesta de diseño de una arquitectura de microservicios para el desarrollo de un sistema de predicción de deserción escolar consciente del contextoPREMUCC: Design proposal for a microservices architecture for the development of a context-aware dropout prediction systemArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge CuCG. E. Reyes, D. V. Sosa, M. G. Quispe, and I. Iraola-Real, “Family, economic and pedagogical factors involved in dropping out of school and the consequences for students of a private institution in Lima - Peru,” in Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/SHIRCON53068.2021.9652381. [2] Y. V. F. Vizcaino, O. A. Z. Duran, R. O. D. Garcia, and M. Gonzalez Duenas, “Architecture of an Automated System for the Monitoring and PreventiÃn of School DropOut,” in Applications in Software Engineering - Proceedings of the 9th International Conference on Software Process Improvement, CIMPS 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 71–77. doi: 10.1109/CIMPS52057.2020.9390097. [3] V. Tinto, “Dropout from Higher Education: A Aeoretical Synthesis of Recent Research. Review of Educational Research,” vol. 43(1), pp. 89–125. [4] M. MacEdo et al., “Investigation of college dropout with the fuzzy c-means algorithm,” in Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, Institute of Electrical and Electronics Engineers Inc., Jul. 2019, pp. 187–189. doi: 10.1109/ICALT.2019.00055. [5] E. Vanegas, Y. Salazar, P. Lerma, and I. Plaza, “Impact of the tutoring program on engineering student dropout,” 2022. [6] T. Morrow, A Context-Aware Ontology forPersonalized Learning: Pervasive Computing forEducational Technology. [7] Á. Rabelo Lopes, R. De Sousa, D. Carvalho, and R. Vaccare, “Context-aware Ubiquitous Learning Literature Systematic Mapping on Ubiquitous Learning Environments,” International Symposium on Computers in Education (SIIE), pp. 1–6, 2017, doi: 10.1109/SIIE.2017.8259662. [8] N. Harrati, I. Bouchrika, A. Tari, and A. Ladjaila, “Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis,” Comput Human Behav, vol. 61, pp. 463–471, 2016. [9] C. Bellei, Situación Educativa de América Latina y el Caribe: Hacia la educación de calidad para todos al 2015. Santiago de Chile: Oficina Regional de Educación para América Latina y el Caribe (OREALC/UNESCO Santiago)), 2013. [10] Á. Hernández Prados, J. Santiago, Á. Muñoz, and A. A. Martínez, “The Issue of school dropout in scientific production activity,” International Journal of Social Sciences and Humanities, vol. XXVI, no. 1, pp. 89–112, 2017. [11] “Introducción a la Universidad Tecnológica de Panamá | Universidad Tecnológica de Panamá.” https://utp.ac.pa/introduccion-la-universidad-tecnologica-de-panama (accessed Oct. 11, 2022). [12] “Instituto Nacional de Estadística y Censo.” https://www.inec.gob.pa/buscador/Default.aspx (accessed Oct. 11, 2022). 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Malavolta, “Architecting with microservices: A systematic mapping study,” Journal of Systems and Software, vol. 150, pp. 77–97, Apr. 2019, doi: 10.1016/J.JSS.2019.01.001.220https://revistascientificas.cuc.edu.co/ingecuc/article/download/5277/5481Núm. 2 , Año 2024 : (Julio-Diciembre)OREORE.xmltext/xml2773https://repositorio.cuc.edu.co/bitstreams/b41c9dc2-2137-4f7a-a5aa-db641895e444/downloadf1c5f8be4ea054893dcc2587cf3940ebMD5111323/13905oai:repositorio.cuc.edu.co:11323/139052024-12-13 03:30:12.751http://creativecommons.org/licenses/by-nc-nd/4.0Inge CuC - 2024metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co |