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

School of Systems and informatics engineering

Research output: Chapter in Book/Report › Conference contribution › peer-review

Abstract

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.

Original language	American English
Title of host publication	ICACIT 2025 - Proceedings
Subtitle of host publication	11th International Symposium on Accreditation of Engineering and Computing Education
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331557973
DOIs	https://doi.org/10.1109/ICACIT66767.2025.11369723
State	Indexed - 2025
Event	11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025 - Chiclayo, Peru Duration: 15 Oct 2025 → 17 Oct 2025

Publication series

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

Conference

Conference	11th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2025
Country/Territory	Peru
City	Chiclayo
Period	15/10/25 → 17/10/25

Bibliographical note

Publisher Copyright:
©2025 IEEE.

Keywords

Machine learning
Pedagogical development
Predictive model
University teacher training

Access to Document

10.1109/ICACIT66767.2025.11369723

Cite this

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. In 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).

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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",

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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",

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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. in 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, Peru, 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).

Research output: Chapter in Book/Report › Conference contribution › peer-review

TY - GEN

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

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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.

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M3 - Conference contribution

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Arana M , Camborda M, Arana M, Huaricapcha B, Calderón S, Camarena M. Prediction of Pedagogical Improvement through University Teacher Training with Machine Learning Tools. In 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

Abstract

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Conference

Bibliographical note

Keywords

Access to Document

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Cite this