Decision Trees and Gender Stereotypes in University Academic Desertion

Andrade-Zurita, Sylvia; Armas-Arias, Sonia; Núñez-López, Rocío; Arévalo-Peralta, Josué

doi:10.1007/978-3-031-12413-6_25

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The analysis of specific sociodemographic characteristics in machine learning studies in education is still in its infancy [48], [49]. Gender prediction using machine learning models mainly focused on gender prediction of educational leadership [50]; female models and reinforcement in STEM [51]; exploring gender differences in learning computational thinking [52]; the intersection of the academic gender gap [53]; and gender stereotyping in academic dropout [54]. These studies shed light on the potential of machine learning to provide valuable insights and guide efforts toward closing the gender gap in education.…”

Section: Gender In Educationmentioning

confidence: 99%

Forecasting Gender in Open Education Competencies: A Machine Learning Approach

Ibarra-Vazquez,

Ramírez-Montoya,

Buenestado-Fernández

2024

IEEE Trans. Learning Technol.

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This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and attitudes or values related to open education and its sub-competencies from a 30-item questionnaire using machine learning models to forecast participants' gender, 2) validation of performance through cross-validation methods, 3) statistical analysis to find significant differences between machine learning models, and 4) an analysis from explainable machine learning models to find relevant features to forecast gender. The results confirm our hypothesis that the performance of machine learning models can effectively forecast gender based on the student's perceptions of knowledge, skills, and attitudes or values related to open education competency.

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