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Forecasting gender in open education competencies: a machine learning approach

Abstract: This article aims to study the performance ofmachine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from26 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 subcompetencies 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 learningmodels, and 4) an analysis fromexplainable 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.

 Autoría: Ibarra-Vazquez G., Ramirez-Montoya M.S., Buenestado-Fernández M.,

 Fuente: IEEE Transactions on Learning Technologies, 2024, 17, 1236-1247

 Año de publicación: 2024

 Nº de páginas: 12

 Tipo de publicación: Artículo de Revista

 DOI: 10.1109/TLT.2023.3336541

 ISSN: 1939-1382,2372-0050

 Url de la publicación: https://doi.org/10.1109/TLT.2023.3336541

Autoría

IBARRA-VÁZQUEZ, GERARDO

RAMÍREZ-MONTOYA, MARÍA SOLEDAD