Caracterización del nivel de estrés de alumnos de ingeniería mediante herramientas de Data Mining

Jonatán Rojas Polo, Cesar Corrales Riveros, Wilmer Atoche Diaz, Alexia Cáceres Cansaya, Miguel Rodriguez Anticona

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

This research addresses the analysis of the level of stress faced by university students of industrial engineering located in metropolitan Lima through data mining tools. In normal situations, the daily load of the student from the eighth to the tenth cycle of a university was divided between university studies and the work of professional practices required in the curriculum, which meant an average load of 25 hours of classes, 30 hours of work in a company and 33 hours of study in the execution of academic tasks per week. This load has been affected since March 15, 2020, when the Ministry of Education established distance education - virtual and the Ministry of Health established confinement due to COVID 19, which impacted on a higher level of stress. The first phase of the research began with data collection, for this phase the SISCO Academic Stress Inventory proposed by Rosanna [1] was used; in the second phase the data preprocessing was carried out; In the third phase, it was identified which are the significant variables that influence a high level of stress measurement of the students, the main methods being the use of logistic regression and the classification tree; In the third phase, the level of precision of the proposed methods were validated, in the logistic regression method a model with a p_value of 95.7%, and a value of the Akaike criterion; In the classification tree method, a precision level of 78% was obtained; Finally, it was determined which are the significant variables that affect the level of stress of the students, such as the ergonomic conditions for studying and carrying out activities at home, which are on average 20 hours a week. The research concludes with the measurement and characterization of the level of stress, recommendations to teachers to be able to motivate students, and look for complementary tools to strengthen learning.

Título traducido de la contribuciónCharacterization of the level of stress of engineering students using data mining tools
Idioma originalEspañol
Título de la publicación alojada19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojada"Prospective and Trends in Technology and Skills for Sustainable Social Development" and "Leveraging Emerging Technologies to Construct the Future", LACCEI 2021 - Proceedings
EditoresMaria M. Larrondo Petrie, Luis Felipe Zapata Rivera, Catalina Aranzazu-Suescun
EditorialLatin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)9789585207189
DOI
EstadoPublicada - 2021
Evento19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Prospective and Trends in Technology and Skills for Sustainable Social Development" and "Leveraging Emerging Technologies to Construct the Future", LACCEI 2021 - Virtual, Online
Duración: 19 jul. 202123 jul. 2021

Serie de la publicación

NombreProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen2021-July
ISSN (versión digital)2414-6390

Conferencia

Conferencia19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Prospective and Trends in Technology and Skills for Sustainable Social Development" and "Leveraging Emerging Technologies to Construct the Future", LACCEI 2021
CiudadVirtual, Online
Período19/07/2123/07/21

Palabras clave

  • Classification tree
  • Data mining and stress level
  • Logistic regression
  • Stress and COVID 19
  • Stress in college students

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