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This study evaluates the predictive effectiveness of nine supervised machine learning algorithms for forecasting faculty digital competence levels using socio-educational variables. A quantitative cross-sectional correlational design applies the DigCompEdu Check-In instrument across six core dimensions and trains models on 4,154 observations. Model performance is assessed with standard classification metrics including ROC-AUC, accuracy, and F1-score.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-algorithms-to-predict-digital-competencies-in-university-faculty-research-version-1-peer-review/290664/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-algorithms-to-predict-digital-competencies-in-university-faculty-research-version-1-peer-review/290664.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-10","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What was the study’s main objective regarding university faculty digital competencies?","Question",{"text":112,"@type":113},"To analyze how effective different machine learning algorithms are at predicting faculty digital competence levels based on socio-educational variables, and to develop an advanced predictive model.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were digital competencies measured in the study?",{"text":117,"@type":113},"Digital competencies were measured with the internationally validated DigCompEdu Check-In instrument, structured across six core dimensions.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning models performed best in predicting higher competence levels?",{"text":121,"@type":113},"Gradient boosting, random forest, and neural network models showed superior predictive performance, especially for advanced competence levels (B2 and C1).",{"name":123,"@type":110,"acceptedAnswer":124},"What limitations were observed for logistic regression and Naive Bayes?",{"text":125,"@type":113},"Logistic regression and Naive Bayes had limitations in identifying low competence levels (A1), and intermediate levels were often overestimated across several models.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},290664,1791470431,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":41,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":147,"read_time":148,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","RESEARCH ARTICLE  \nMachine Learning Algorithms to Predict Digital Competencies  \nin University Faculty  \n[version 1; peer review: 1 approved, 1 approved with reservations]  \nJenniffer Sobeida Moreira-Choez 1, Aracelly Fernanda Núñez-Naranjo 2  \n,  \nAsia Cecilia Carrasco-Valenzuela3, Héctor Luis López-López3, Jesús Alejandro Vázquez Meza3, Angel Ramón Sabando-García4  \n1 Facultad de Posgrado, Universidad Estatal de Milagro, Milagro, Guayas, 091050, Ecuador  \n2Centro de Investigación en Ciencias Humanas y de la Educación-CICHE. Facultad de Ciencias de la Educación, Universidad Tecnologica Indoamerica, Ambato, Tungurahua, 180103, Ecuador  \n3Universidad Autonoma de Sinaloa, Culiacán, Sinaloa, Mexico  \n4 Departamento de Matemáticas y Estadística, Pontificia Universidad Catolica del Ecuador, Santo Domingo, Santo Domingo, Ecuador  \nv1  \nFirst published: 10 Jun 2025, 14:573  \n[https://doi.org/10.12688/f1000research.165342.1](https://doi.org/10.12688/f1000research.165342.1)  \n[Latest published:](Latest published: 10 Jun 2025)[ 10 Jun 2025](Latest published: 10 Jun 2025), 14:573  \n[https://doi.org/10.12688/f1000research.165342.1](https://doi.org/10.12688/f1000research.165342.1)  \nAbstract  \nBackground  \nThe digital transformation of higher education has intensified the need to assess and enhance the digital competencies of university faculty. This study analyzed the effectiveness of various machine learning algorithms in predicting levels of faculty digital competence based on socio-educational variables. The objective was to develop an advanced predictive model, applied to faculty members from the State University of Milagro and the Technical University of Manabí .  \nMethods  \nA quantitative approach was adopted, with a cross-sectional correlational design. Digital competencies were measured using the internationally validated DigCompEdu Check-In instrument, structured across six core dimensions. In the predictive phase, nine supervised machine learning algorithms were trained and evaluated:  \nlogistic regression, decision trees, random forest, gradient boosting, k-nearest neighbors, support vector machines, stochastic gradient descent, artificial neural networks, and Naive Bayes. The models were trained using a dataset comprising 4,154 observations, and their performance was assessed using standard classification metrics: area under the ROC curve (AUC), accuracy, F1-score, sensitivity, and  \nOpen Peer Review  \nApproval Status   \n1 2  \n1. Giorgia Montanucci , University for Foreigners of Perugia, Perugia, Italy Talia Sbardella, University for Foreigners of Perugia, Perugia, Italy  \n2. William J Marin-Rodriguez , Universidad Nacional José Faustino Sánchez Carrión, Huacho, Peru  \nAny reports and responses or comments on the  \narticle can be found at the end of the article.  \nMatthew’s correlation coefficient (MCC) .  \nResults  \nGradient boosting, random forest, and neural network models demonstrated superior predictive performance, particularly at advanced competence levels (B2 and C1) . Significant associations were identified between academic level, age, gender, and digital competencies. Logistic regression and Naive Bayes showed limitations in identifying low competence levels (A1), while intermediate levels were often overestimated across several models.  \nConclusions  \nThe findings confirm that machine learning algorithms can accurately predict university faculty digital competencies. Advanced models outperformed traditional ones, especially at higher competence levels. It is recommended to incorporate contextual variables and validate the models in diverse educational settings.  \nKeywords  \nMachine learning, digital competence, higher education, artificial intelligence, teacher training, educational assessment, pedagogical innovation, educational technology.  \nThis article is included in the Artificial  \nIntelligence and Machine Learning gateway.  \nCorresponding author: Jenniffer Sobeida Moreira-Choez ([jmoreirac10@unemi.edu.ec](jm","cbCaipJFMrQizJvz","https://ap.wps.com/l/cbCaipJFMrQizJvz","pdf",7736681,"English","# Abstract\n# Background\n# Methods\n# Results\n# Conclusions\n# Keywords\n# Introduction","[{\"question\":\"What was the study’s main objective regarding university faculty digital competencies?\",\"answer\":\"To analyze how effective different machine learning algorithms are at predicting faculty digital competence levels based on socio-educational variables, and to develop an advanced predictive model.\"},{\"question\":\"How were digital competencies measured in the study?\",\"answer\":\"Digital competencies were measured with the internationally validated DigCompEdu Check-In instrument, structured across six core dimensions.\"},{\"question\":\"Which machine learning models performed best in predicting higher competence levels?\",\"answer\":\"Gradient boosting, random forest, and neural network models showed superior predictive performance, especially for advanced competence levels (B2 and C1).\"},{\"question\":\"What limitations were observed for logistic regression and Naive Bayes?\",\"answer\":\"Logistic regression and Naive Bayes had limitations in identifying low competence levels (A1), and intermediate levels were often overestimated across several models.\"}]","Machine Learning Algorithms to Predict Digital Competencies in University Faculty - Research Version 1 - Peer Review | PDF",1789642314,76]