[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117037-en":3,"doc-seo-117037-105":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},117037,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Predicting Open Education Competency Level - A Machine Learning Approach","The article studies open education competency data using machine learning to assess whether decision-rule models can be built from students’ perceptions and used to classify competency levels. Convenience-sample data from 326 students across 26 countries is collected through the eOpen instrument. Using a quantitative approach, it trains Decision Trees and Random Forests, evaluates prediction errors to identify bias, and interprets decision trees to explain model choices. Results support the hypothesis that perceived knowledge, skills, and attitudes/values—covering open education sub-competencies—enable satisfactory competency prediction.","Heliyon 9 (2023) e20597  \nContents lists available at ScienceDirect  \nHeliyon  \n[journal homepage: www.cell.com/heliyon](journal homepage: www.cell.com/heliyon)  \n| Research article\u003Cbr>Predicting open education competency level: A machine learning approach |  |  |  |\n| --- | --- | --- | --- |\n| Gerardo Ibarra-Vazquez a,∗ , María Soledad Ramírez-Montoya b, Mariana Buenestado-Fernández c, Gustavo Olague d\u003Cbr>a School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico b Institute for the Future of Education, Tecnologico de Monterrey, Monterrey, Mexico c Department of Education, Universidad de Cantabria, Santander, Spain\u003Cbr>d CICESE Research Center, EvoVision Laboratory, Department of Computer Science, Ensenada, Mexico |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Open education Competency level Machine learning Educational innovation Higher education |  | This article aims to study open education competency data through machine learning models to determine whether models can be built on decision rules using the features from the students’perceptions and classify them by the level of competency. Data was collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. Based on a quantitative research approach, we analyzed the eOpen data using two machine learning models considering these findings: 1) derivation of decision rules from students’ perceptions of knowledge, skills, and attitudes or values related to open education to predict their competence level using Decision Trees and Random Forests models, 2) analysis of the prediction errors in the machine learning models to find bias, and 3) description of decision trees from the machine learning models to understand the choices that both models made to predict the competency levels. The results confirmed our hypothesis that the students’ perceptions oftheir knowledge, skills, and attitudes or values related to open education and its sub-competencies produced satisfactory data for building machine learning models to predict the participants’ competency levels. |  |\n\n1. Introduction  \nOpen Education allows people to access knowledge easily, provides means for collaboration, fosters innovation, and unites international communities of students and teachers. The exchange of knowledge, ideas, and information has always been a fundamental part of education, so free sharing is familiar in this field. Open Education seeks to expand educational opportunities by taking advantage of the possibilities offered by the Internet, allowing rapid and almost unrestricted diffusion, allowing people from allover the world to access knowledge, connect and collaborate [11]. The key concept in this idea is openness, which allows not only access to materials but also the freedom to modify and use them, the creation of communities and networks to share information and work, allowing education to be personalized to the individual needs and interests or adapt to different audiences in innovative ways [53]. Open Education is founded on sharing freely and with unrestricted access. The gratuity of the materials, the freedom of  \n* Corresponding author.  \nE-mail address: [gerardo.ibarra.v@tec.mx](gerardo.ibarra.v@tec.mx) (G. Ibarra-Vazquez).  \n[https://doi.org/10.1016/j.heliyon.2023.e20597](https://doi.org/10.1016/j.heliyon.2023.e20597)  \nReceived 16 November 2022; Received in revised form 25 September 2023; Accepted 29 September 2023  \nAvailable online 17 October 2023  \n2405-8440/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).  \nG. Ibarra-Vazquez, M.S. Ramírez-Montoya, M. Buenestado-Fernández et al. Heliyon 9 (2023) e20597  \nuse, and legal tools (such as open licenses) allow educational resources to be reused and modified by anyone [45]. By promoting free and open exch","cbCaiaQpx4HPsjzG","https://ap.wps.com/l/cbCaiaQpx4HPsjzG","pdf",1918981,1,15,"English","en",105,"# Introduction\n# Methodology and Data\n## Machine Learning Models\n## Error Analysis and Bias Detection\n## Decision Tree Interpretation","[{\"question\":\"What is the goal of the study on open education competency?\",\"answer\":\"To determine whether machine learning models can be built from students’ perceptions and used to classify open education competency levels.\"},{\"question\":\"What dataset and instrument are used to train and evaluate the models?\",\"answer\":\"The study uses eOpen data collected from a convenience sample of 326 students from 26 countries.\"},{\"question\":\"Which machine learning approaches are used and how are their results interpreted?\",\"answer\":\"Decision Trees and Random Forests are trained; prediction errors are analyzed to find bias, and decision trees are described to understand the choices behind competency predictions.\"}]","Predicting Open Education Competency Level - A Machine Learning Approach | PDF",1785673284,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-open-education-competency-level-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-open-education-competency-level-a-machine-learning-approach/117037/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the study on open education competency?","Question",{"text":75,"@type":76},"To determine whether machine learning models can be built from students’ perceptions and used to classify open education competency levels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and instrument are used to train and evaluate the models?",{"text":80,"@type":76},"The study uses eOpen data collected from a convenience sample of 326 students from 26 countries.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are used and how are their results interpreted?",{"text":84,"@type":76},"Decision Trees and Random Forests are trained; prediction errors are analyzed to find bias, and decision trees are described to understand the choices behind competency predictions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]