Inteligencia artificial para determinar las estrategias de aprendizaje de los estudiantes universitarios.

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dc.contributor.authorSabando García , Ángel Ramón
dc.contributor.authorOlguín Martínez , Cynthia Michel
dc.contributor.authorBenavides Lara, Raul Marcelo
dc.contributor.authorSalazar Echeagaray, Teresa Irina
dc.contributor.authorHuerta Mora, Eduardo Alfonso
dc.contributor.authorBumbila García, Bibian Bibeca
dc.contributor.authorCedeño Barcia , Lizandro Agustín
dc.contributor.authorMoreira Choez , Jenniffer Sobeida
dc.contributor.correspondingSabando García , Ángel Ramón
dc.date.accessioned2026-06-24T19:51:34Z
dc.date.available2026-06-24T19:51:34Z
dc.date.issued2026-06-24
dc.date.issued24/6/2025
dc.description.abstractBackground: University students employ various learning strategies that influence their academic success and retention in the educational system. However, those who fail to use these strategies effectively may be at risk of dropping out. In this context, the objective of this study was to determine the learning strategies of students at the Pontifical Catholic University of Ecuador, Santo Domingo campus (PUCESD) using artificial intelligence.Methods: The research followed a quantitative, correlational, and predictive approach, with a probabilistic sample of 162 students aged 17–24, of whom 29% were male and 71% female, from public, private religious, private secular, and semi-private institutions. Through the ACRA questionnaire, three dimensions were evaluated: cognitive strategies, study habits, and learning support.Results: The results revealed a structure with adequate internal consistency and structural validity, high-lighting a significant relationship between cognitive strategies and study habits, suggesting a positive interaction between the two to optimize learning.Conclusions: Artificial intelligence proved effective in identifying patterns in learning strategies. However, it is recommended to adjust certain questionnaire items to enhance its precision and applicability in diverse contexts, thereby facilitating targeted interventions.
dc.id.author1309219416
dc.id.author0602173080
dc.id.author1306176247
dc.id.author1305203976
dc.id.author1311987836
dc.identifier.citation"Sabando-García ´AR, Olguín-Martínez CM,Benavides-Lara RM, Salazar-Echeagaray TI, Huerta-Mora EA, Bumbila-García BB,Cede˜no-Barcia LA and Moreira-Choez JS (2025) Artificial intelligence for determininglearning strategies in university students. Front. Educ. 10:1611189.doi: 10.3389/feduc.2025.1611189"
dc.identifier.doihttps://doi.org/10.3389/feduc.2025.1611189
dc.identifier.issn1664-1078
dc.identifier.urihttps://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1611189/full
dc.identifier.urihttps://repositorio.puce.edu.ec/handle/123456789/49160
dc.indexed.databaseLatin index
dc.language.isoes
dc.magazine.pageRange1-12
dc.magazine.titleCódigo Científico Revista de Investigación
dc.magazine.volumeChapterV10
dc.subjectFactor análisis
dc.subjectUniversity student
dc.subjectEducation evaluation
dc.subjectArtificial intelligence
dc.subjectLearning method
dc.titleInteligencia artificial para determinar las estrategias de aprendizaje de los estudiantes universitarios.
dc.title.alternativeArtificial intelligence for determining learning strategies in university students
dc.typeArtículo científico
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