Algoritmos de aprendizaje automático para predecir las competencias digitales del personal docente universitario

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dc.contributor.authorSabando García , Ángel Ramón
dc.contributor.authorMoreira-Choez , Jenniffer Sobeida
dc.contributor.authorNúñez Naranjo , Aracelly Fernanda
dc.contributor.authorCarrasco Valenzuela , Asia Cecilia
dc.contributor.authorLópez López , Héctor Luis
dc.contributor.authorVázquez Meza , Jesús Alejandro
dc.contributor.correspondingSabando García , Ángel Ramón
dc.date.accessioned2026-06-24T16:58:22Z
dc.date.available2026-06-24T16:58:22Z
dc.date.issued2026-06-24
dc.date.issued10/6/2025
dc.description.abstract"The 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. Methods A 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: logistic 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 Results Gradient 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. Conclusions The 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"
dc.id.author1309219416
dc.id.author1311987836
dc.id.author1803187739
dc.identifier.citation" Moreira-Choez JS, Núñez-Naranjo AF, Carrasco-Valenzuela AC et al. Machine Learning Algorithms to Predict Digital Competencies in University Faculty [version 1; peer review: 1 approved, 1 approved with reservations] F1000Research 2025, 14:573 https://doi.org/10.12688/f1000research.165342.1"
dc.identifier.doihttps://doi.org/10.12688/f1000research.165342.1
dc.identifier.issn2046 1402
dc.identifier.urihttps://f1000research.com/articles/14-573
dc.identifier.urihttps://repositorio.puce.edu.ec/handle/123456789/49154
dc.indexed.databaseScopus
dc.language.isoen
dc.magazine.pageRange1-30
dc.magazine.titleCódigo Científico Revista de Investigación
dc.magazine.volumeChapterNo
dc.statePublished
dc.subjectMachine learning
dc.subjectDigital competence
dc.subjectHigher education
dc.subjectArtificial intelligence
dc.subjectTeacher training
dc.subjectEducational assessment
dc.subjectPedagogical innovation
dc.subjectEducational technology
dc.titleAlgoritmos de aprendizaje automático para predecir las competencias digitales del personal docente universitario
dc.title.alternativeMachine learning algorithms to predict digital competencies in university faculty
dc.typeArtículo científico
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