Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools

Maglioni Arana; Maria Camborda; Maribel Arana; Bryan Huaricapcha; Severo Calderón; Miguel Camarena

doi:10.1109/ICACIT66767.2025.11369723

Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools

Maglioni Arana
, Maria Camborda
, Maribel Arana
, Bryan Huaricapcha
, Severo Calderón
, Miguel Camarena

EAP Ingeniería de sistemas e informática

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

Resumen

In today’s digital age, the training of university teachers is crucial for maintaining educational quality, especially in pedagogical development. The present research seeks to make predictions in order to determine the real impact of these skills on pedagogical development. This study aimed to determine how teacher training, using machine learning tools, influences the improvement of pedagogical development in the Systems Engineering Faculty of the Universidad Nacional del Centro de Perú (FIS-UNCP). The research adopted a quantitative approach, applied type and longitudinal non-experimental design, with a sample of 26 FIS-UNCP teachers. The results revealed that teacher training significantly influences pedagogical improvement, with a predictive model that reached 91.26% accuracy and an error margin of 8.74%. The dimensions of training, planning, thematic content, methodology, and evaluation also had a significant influence on pedagogical development.

Idioma original	Inglés estadounidense
Título de la publicación alojada	ICACIT 2025 - Proceedings
Subtítulo de la publicación alojada	11th International Symposium on Accreditation of Engineering and Computing Education
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9798331557973
DOI	https://doi.org/10.1109/ICACIT66767.2025.11369723
Estado	Indizado - 2025
Evento	11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025 - Chiclayo, Perú Duración: 15 oct. 2025 → 17 oct. 2025

Serie de la publicación

Nombre	ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education

Conferencia

Conferencia	11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025
País/Territorio	Perú
Ciudad	Chiclayo
Período	15/10/25 → 17/10/25

Nota bibliográfica

Publisher Copyright:
©2025 IEEE.

Acceder al documento

10.1109/ICACIT66767.2025.11369723

Citar esto

Arana, M., Camborda, M., Arana, M., Huaricapcha, B., Calderón, S., & Camarena, M. (2025). Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools. En ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education (ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACIT66767.2025.11369723

Arana, Maglioni ; Camborda, Maria ; Arana, Maribel et al. / Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools. ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education. Institute of Electrical and Electronics Engineers Inc., 2025. (ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education).

@inproceedings{c17ed891ad4e408083a2048b54bbbf95,

title = "Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools",

abstract = "In today{\textquoteright}s digital age, the training of university teachers is crucial for maintaining educational quality, especially in pedagogical development. The present research seeks to make predictions in order to determine the real impact of these skills on pedagogical development. This study aimed to determine how teacher training, using machine learning tools, influences the improvement of pedagogical development in the Systems Engineering Faculty of the Universidad Nacional del Centro de Per{\'u} (FIS-UNCP). The research adopted a quantitative approach, applied type and longitudinal non-experimental design, with a sample of 26 FIS-UNCP teachers. The results revealed that teacher training significantly influences pedagogical improvement, with a predictive model that reached 91.26\% accuracy and an error margin of 8.74\%. The dimensions of training, planning, thematic content, methodology, and evaluation also had a significant influence on pedagogical development.",

keywords = "Machine learning, Pedagogical development, Predictive model, University teacher training",

author = "Maglioni Arana and Maria Camborda and Maribel Arana and Bryan Huaricapcha and Severo Calder{\'o}n and Miguel Camarena",

note = "Publisher Copyright: {\textcopyright}2025 IEEE.; 11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025 ; Conference date: 15-10-2025 Through 17-10-2025",

year = "2025",

doi = "10.1109/ICACIT66767.2025.11369723",

language = "American English",

series = "ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICACIT 2025 - Proceedings",

address = "United States",

}

Arana, M , Camborda, M, Arana, M, Huaricapcha, B, Calderón, S & Camarena, M 2025, Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools. En ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education. ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education, Institute of Electrical and Electronics Engineers Inc., 11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025, Chiclayo, Perú, 15/10/25. https://doi.org/10.1109/ICACIT66767.2025.11369723

Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools. / Arana, Maglioni ; Camborda, Maria; Arana, Maribel et al.
ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education. Institute of Electrical and Electronics Engineers Inc., 2025. (ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education).

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools

AU - Arana, Maglioni

AU - Camborda, Maria

AU - Arana, Maribel

AU - Huaricapcha, Bryan

AU - Calderón, Severo

AU - Camarena, Miguel

PY - 2025

Y1 - 2025

N2 - In today’s digital age, the training of university teachers is crucial for maintaining educational quality, especially in pedagogical development. The present research seeks to make predictions in order to determine the real impact of these skills on pedagogical development. This study aimed to determine how teacher training, using machine learning tools, influences the improvement of pedagogical development in the Systems Engineering Faculty of the Universidad Nacional del Centro de Perú (FIS-UNCP). The research adopted a quantitative approach, applied type and longitudinal non-experimental design, with a sample of 26 FIS-UNCP teachers. The results revealed that teacher training significantly influences pedagogical improvement, with a predictive model that reached 91.26% accuracy and an error margin of 8.74%. The dimensions of training, planning, thematic content, methodology, and evaluation also had a significant influence on pedagogical development.

AB - In today’s digital age, the training of university teachers is crucial for maintaining educational quality, especially in pedagogical development. The present research seeks to make predictions in order to determine the real impact of these skills on pedagogical development. This study aimed to determine how teacher training, using machine learning tools, influences the improvement of pedagogical development in the Systems Engineering Faculty of the Universidad Nacional del Centro de Perú (FIS-UNCP). The research adopted a quantitative approach, applied type and longitudinal non-experimental design, with a sample of 26 FIS-UNCP teachers. The results revealed that teacher training significantly influences pedagogical improvement, with a predictive model that reached 91.26% accuracy and an error margin of 8.74%. The dimensions of training, planning, thematic content, methodology, and evaluation also had a significant influence on pedagogical development.

KW - Machine learning

KW - Pedagogical development

KW - Predictive model

KW - University teacher training

UR - https://www.scopus.com/pages/publications/105034482777

U2 - 10.1109/ICACIT66767.2025.11369723

DO - 10.1109/ICACIT66767.2025.11369723

M3 - Conference contribution

AN - SCOPUS:105034482777

T3 - ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education

BT - ICACIT 2025 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025

Y2 - 15 October 2025 through 17 October 2025

ER -

Arana M , Camborda M, Arana M, Huaricapcha B, Calderón S, Camarena M. Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools. En ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education. Institute of Electrical and Electronics Engineers Inc. 2025. (ICACIT 2025 - Proceedings: 11th International Symposium on Accreditation of Engineering and Computing Education). doi: 10.1109/ICACIT66767.2025.11369723

Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

Acceder al documento

Huella

Citar esto