[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118231-en":3,"doc-seo-118231-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118231,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Forecasting Gender in Open Education Competencies - A Machine Learning Approach","This study evaluates machine learning models for forecasting gender using students’ perceptions of open education competencies. Data come from a convenience sample of 326 students across 26 countries collected with the eOpen instrument. The workflow includes modeling based on perceived knowledge, skills, and attitudes/values, performance validation via cross-validation, statistical comparisons across models, and interpretation using explainable machine learning to identify predictive features.","Forecasting Gender in Open Education Competencies: A Machine Learning Approach  \nGerardo Ibarra-Vazquez , María Soledad Ramírez-Montoya , and Mariana Buenestado-Fernández   \nAbstract—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 ofknowledge, 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 learning models, and4)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.  \nIndex Terms—Educational innovation, explainable, forecasting, gender, higher education, machine learning (ML), open education, student perception.  \nACRONYMS  \nOER Open educational resource.  \nSDG Sustainable development goal.  \nML Machine learning.  \nDT Decision tree.  \nRF Random forest.  \nLGBM Light gradient boosting machine.  \nCNN Convolutional neural network.  \n1D-CNN 1-D convolutional neural network.  \nI. INTRODUCTION  \nT HE current international political agenda makes explicit  \nthe importance of generating processes to incorporate social justice in education, including good open education practices for the solvency of contextual education. The United  \nManuscript received 17 October 2022; revised 5 June 2023 and 29 September 2023; accepted 8 November 2023 . Date of publication 29 November 2023; date of current version 21 March 2024 . This work was supported by the Tecnologico de Monterrey through the “Challenge-Based Research Funding Program 2022” under Project ID \\#003-IFE001-C2-T3-T. (Corresponding author: Gerardo Ibarra-Vazquez.)  \nGerardo Ibarra-Vazquez is with the School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico (e-mail: gerardo. [ibarra.v@tec.mx](ibarra.v@tec.mx)) .  \nMaría Soledad Ramírez-Montoya is with the Institute for the Future of Education, Tecnologico de Monterrey, Monterrey 64849, Mexico (e-mail: solrami[rez@tec.mx](rez@tec.mx)) .  \nMariana Buenestado-Fernández is with the Department of Education, Universidad de Cantabria, 39005 Santander, Spain (e-mail: buenestadom@unican.es) .  \nDigital Object Identifier 10.1109/TLT.2023.3336541  \nNations Educational, Scientific, and Cultural Organization(UNESCO) Framework for Action gives a good account of this in recognition of its leading role in the Education 2030 Sustainable Development Agenda [1] . Sustainable Development Goal 4 establishes international scientific communication to guarantee access to inclusive and equitable quality education and promote learning opportunities for all. In addition, it formulates using free-access educational resources and technology without discrimination as a strategic measure. Their potential is recognized as part of the solution in the strategy for gender equality in and through education [2] . More recently, UNESCO reaffirmedits commitment to these goals and strategies by helping all member states build inclusive knowledge societies through the recommendation on open educational resources (OER) [3] . The need to transfer the political orientations of international organizations as sources of innovation is frequently demanded to develop practices to improve the training of professionalsand solve contextual problems related to practice in educational sciences [4] .  \nThere is an essential heterogeneity of open education practices. 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