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dc.contributor.authorChamorro Atalaya, Omar
dc.contributor.authorGuerrero Carranza, Rosemary
dc.contributor.authorPoma García, Claudia
dc.contributor.authorSobrino Chunga, Lisle
dc.contributor.authorVargas Díaz, Ademar
dc.date.accessioned2024-02-29T19:08:52Z
dc.date.available2024-02-29T19:08:52Z
dc.date.issued2024
dc.identifier.urihttp://hdl.handle.net/20.500.11955/1207
dc.descriptionIndexado en Scopus.es_PE
dc.descriptionEl texto completo de este trabajo no está disponible en el Repositorio Institucional UNIFE. Deberá acceder por el DOI ó URL de la casa editorial externa.es_PE
dc.descriptionInternational Journal of Evaluation and Research in Education; Vol. 13, Abril 2024; pp. 831-841es_PE
dc.description.abstractWith the incursion of data science into the academic field and the massification of social networks, it is possible to extract information on student satisfaction that contributes to feedback on teacher teaching strategies and methods. This article aims to determine student satisfaction with teaching performance, through sentiment analysis. Methodologically, the research is of a non-experimental longitudinal design, with a quantitative approach. Data collection was carried out through the social network Twitter, and data analysis was carried out through the sentiment analysis technique. As a result, it was identified that in the first week of class, the highest level of satisfaction was obtained, reaching 96.3% of the total number of students. Meanwhile, in the evaluation weeks, the highest level of dissatisfaction was reaching 29.17%. It is concluded that when going from totally virtual learning to hybrid learning, students express a certain level of dissatisfaction typical of a process of progressive adaptation. Therefore, teachers should take advantage of these findings to redesign assessment rubrics in the context of hybrid teaching. Aspects such as collecting opinions through social networks and extracting a degree of satisfaction through them apply in a crossed way to other professional fields. © 2024, Institute of Advanced Engineering and Science. All rights reserved.es_PE
dc.language.isoenes_PE
dc.publisherInstitute of Advanced Engineering and Sciencees_PE
dc.relation.ispartofurn:issn: 22528822es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.sourceRepositorio Institucional - UNIFEes_PE
dc.subjectSatisfacción del clientees_PE
dc.subjectAprendizaje en líneaes_PE
dc.titleStudent satisfaction in the context of hybrid learning through sentiment analysises_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#5.01.00es_PE
dc.publisher.countryUSes_PE
dc.identifier.doihttps://doi.org/10.11591/ijere.v13i2.26717es_PE
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.identifier.journalAdvanced Engineering and Sciencees_PE
dc.identifier.urlhttps://www.scopus.com/record/display.uri?eid=2-s2.0-85183938191&doi=10.11591%2fijere.v13i2.26717&origin=inward&txGid=1db2592ac1c8fbbf0a043c80f812a0ee


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