Automatic Mobile Learning System for the Constant Preparation of the Student Community
DOI:
https://doi.org/10.56294/dm2024221Keywords:
Pandemic, Mobile learning., Automatic system, StudentsAbstract
Introduction: the events that occurred with the pandemic caused a drastic change in all activities with direct contact due to the high risk of contagion, with educational centers being affected by the closure measures and the imposition of virtual classes to continue with student preparation, leading many students to see the need to have a computer to take their classes, eventually showing boredom due to the lack of desire to be in front of a computer, This to a certain extent weakens their interest in learning and affects their learning because mobile devices have become more important due to the various applications that provide students with information. For this reason, we propose mobile learning that allows students to have more information, as well as interaction with different students so that they have the opportunity to learn on a constant basis.
Objective: the objective is to create an automatic mobile learning system for the constant preparation of the student community.
Method: a methodology based on a client-server model to take advantage of the various educational resources accompanied by the good support it provides the subjects for students with the interaction of a mobile application.
Results: through the operation of the system, it was visualized that the tests carried out with the students were presented with an efficiency of 96,70 %,
Conclusions: this system presents a high efficiency that allows to reinforce the subjects that need more prominence in the student's learning and progress of level through the teacher's evaluations
References
1. Maguiña Vargas C. The management of COVID-19: a dilemma between science and therapeutic art. Acta Medica Peruana [Internet]. 2020 Jul 1; 37(2):228–58. Available from: https://amp.cmp.org.pe/index.php/AMP/article/view/1028
2. Adnan Shereen M, Khan S, Kazmi A, Bashir N, Siddique R. COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. J Adv Res [Internet]. 2020 Jul 1; 24:91–8. Available from: https://pubmed.ncbi.nlm.nih.gov/32257431/
3. Román F, Forés Miravalles A, Calandri I, Gautreaux R, Antúnez A, Ordhei D, et al. Resiliencia de docentes en distanciamiento social preventivo obligatorio durante la pandemia de COVID-19. Journal of Neuroeducation [Internet]. 2020 Jul 15; 1(1):76–87. Available from: https://revistes.ub.edu/index.php/joned/article/view/31727
4. Campos Palma C. De académicas, pandemia, encierro y bitácoras: experiencias de algunas universitarias en el contexto del COVID-19. Revista Reflexiones [Internet]. 2020 Jun 11; 99(2):1–9. Available from: https://revistas.ucr.ac.cr/index.php/reflexiones/article/view/42271/42519
5. Marchiori Buss P, Tobar S. La COVID-19 y las oportunidades de cooperación internacional en salud. Cad Saude Publica [Internet]. 2020 Apr 22; 36(4):1–3. Available from: https://africacdc.org/programme/emergency-
6. González Fernández MO. La capacitación docente para una educación remota de emergencia por la pandemia de la COVID-19. Revista Tecnología, Ciencia y Educación [Internet]. 2021 May 4; 19:81–102. Available from: https://www.tecnologia-ciencia-educacion.com/index.php/TCE/article/view/614
7. Miranda Benavides K, Prendas Aguilar G, Miranda Benavides Y. El Teletrabajo, valoraciones de las personas trabajadoras en relación con las ventajas y desventajas, percepción de estrés y calidad de vida. Revista Nuevo Humanismo 2021 May 11; 9(1):20–20. Available from: https://www.revistas.una.ac.cr/index.php/nuevohumanismo/article/view/15011/21484
8. Guiot Limón I. Uso de las TICS en la educación superior durante la Pandemia COVID-19: Ventajas y desventajas. Interconectando Saberes [Internet]. 2021 Jul 24; 12(6):223–7. Available from: https://is.uv.mx/index.php/IS/article/view/2724
9. Londoño Velasco E, Montoya Cobo E, García A, Bolaños Martínez IA, Osorio Roa DM, Isaza Gómez GD. Percepción de estudiantes frente a procesos de enseñanza- aprendizaje durante pandemia por covid-19. Educación y Educadores [Internet]. 2021 Sep 30; 24(2):199–217. Available from: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0123-12942021000200199&lng=en&nrm=iso&tlng=es
10. Castañeda Rodríguez KD, Vargas Jaimes AM. En tiempos de pandemia: una mirada retrospectiva sobre la educación a distancia, virtual y remota de emergencia, así como sobre las buenas prácticas docentes. Academia y Virtualidad [Internet]. 2021 May 28; 14(1):13–22. Available from: https://www.researchgate.net/publication/352013364_En_tiempos_de_pandemia_una_mirada_retrospectiva_sobre_la_educacion_a_distancia_virtual_y_remota_de_emergencia_asi_como_sobre_las_buenas_practicas_docentes
11. Ballesteros Ballesteros VA, Rodríguez Cardoso ÓI, Lozano Forero S, Nisperuza Toledo JL. El Aprendizaje Móvil en Educación Superior: Una Experiencia desde la Formación de Ingenieros. Revista Científica [Internet]. 2020 May 10; 38(2):243–57. Available from: https://revistas.udistrital.edu.co/index.php/revcie/article/view/15214
12. Shen J, Shafiq MO. Learning mobile application usage - A deep learning approach. In: Proceedings - 18th IEEE International Conference on Machine Learning and Applications [Internet]. Florida - EEUU: Institute of Electrical and Electronics Engineers Inc.; 2019. p. 287–92. Available from: https://ieeexplore.ieee.org/abstract/document/8999259
13. Zhu K, Zhang T. Deep Reinforcement Learning Based Mobile Robot Navigation: A Review. Tsinghua Sci Technol [Internet]. 2021 Oct 1; 26(5):674–91. Available from: https://www.sciopen.com/article/10.26599/TST.2021.9010012
14. Daoudi M, Lebkiri N, Oumaira I. Determining the learner’s profile and context profile in order to propose adaptive mobile interfaces based on machine learning. In: 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) [Internet]. Kenitra - Morocco: Institute of Electrical and Electronics Engineers Inc.; 2020. p. 1–6. Available from: https://ieeexplore.ieee.org/abstract/document/9314518
15. Wu J. A Mobile Platform-Oriented English Vocabulary Deep Search Learning Algorithm. In: Proceedings of the 5th International Conference on I-SMAC [Internet]. Palladam - India: Institute of Electrical and Electronics Engineers Inc.; 2021. p. 486–9. Available from: https://ieeexplore.ieee.org/abstract/document/9640847
Published
Issue
Section
License
Copyright (c) 2024 Lucía Asencios-Trujillo, Djamila Gallegos-Espinoza, Lida Asencios-Trujillo, Livia Piñas-Rivera, Carlos LaRosa-Longobardi, Rosa Perez-Siguas (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
