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.