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Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches

This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrum...

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Autores principales: Ibarra-Vazquez, Gerardo, Ramí­rez-Montoya, María Soledad, Terashima, Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261829/
https://www.ncbi.nlm.nih.gov/pubmed/37361781
http://dx.doi.org/10.1007/s10639-023-11831-4
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author Ibarra-Vazquez, Gerardo
Ramí­rez-Montoya, María Soledad
Terashima, Hugo
author_facet Ibarra-Vazquez, Gerardo
Ramí­rez-Montoya, María Soledad
Terashima, Hugo
author_sort Ibarra-Vazquez, Gerardo
collection PubMed
description This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following data analyses: 1) predict students’ gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models’ performance during training and testing stages, and 3) study the models’ prediction bias through a confusion matrix analysis. Our results confirm the hypothesis that the four machine learning models (Random Forest, Support Vector Machines, Multi-layer Perception, and One-Dimensional Convolutional Neural Network) can find sufficient differences in the eComplexity data to classify correctly up to 96.94% and 82.14% of the students’ gender in the training and testing stage, respectively. The confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. It showed that the most frequent error was to predict Male students as Female class. This paper provides empirical support for analyzing perception data through machine learning models in survey research. This work proposed a novel educational practice based on developing complex thinking competency and machine learning models to facilitate educational itineraries adapted to the training needs of each group to reduce social gaps existing due to gender.
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spelling pubmed-102618292023-06-14 Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches Ibarra-Vazquez, Gerardo Ramí­rez-Montoya, María Soledad Terashima, Hugo Educ Inf Technol (Dordr) Article This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following data analyses: 1) predict students’ gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models’ performance during training and testing stages, and 3) study the models’ prediction bias through a confusion matrix analysis. Our results confirm the hypothesis that the four machine learning models (Random Forest, Support Vector Machines, Multi-layer Perception, and One-Dimensional Convolutional Neural Network) can find sufficient differences in the eComplexity data to classify correctly up to 96.94% and 82.14% of the students’ gender in the training and testing stage, respectively. The confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. It showed that the most frequent error was to predict Male students as Female class. This paper provides empirical support for analyzing perception data through machine learning models in survey research. This work proposed a novel educational practice based on developing complex thinking competency and machine learning models to facilitate educational itineraries adapted to the training needs of each group to reduce social gaps existing due to gender. Springer US 2023-06-13 /pmc/articles/PMC10261829/ /pubmed/37361781 http://dx.doi.org/10.1007/s10639-023-11831-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ibarra-Vazquez, Gerardo
Ramí­rez-Montoya, María Soledad
Terashima, Hugo
Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
title Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
title_full Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
title_fullStr Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
title_full_unstemmed Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
title_short Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches
title_sort gender prediction based on university students’ complex thinking competency: an analysis from machine learning approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261829/
https://www.ncbi.nlm.nih.gov/pubmed/37361781
http://dx.doi.org/10.1007/s10639-023-11831-4
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