Chamorro Atalaya, OmarGuerrero Carranza, RosemaryPoma García, ClaudiaSobrino Chunga, LisleVargas Díaz, Ademar2024-02-292024-02-292024http://hdl.handle.net/20.500.11955/1207Indexado en Scopus.El 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.International Journal of Evaluation and Research in Education; Vol. 13, Abril 2024; pp. 831-841With 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.enginfo:eu-repo/semantics/restrictedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/Satisfacción del clienteAprendizaje en líneainfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#5.01.00https://doi.org/10.11591/ijere.v13i2.26717Advanced Engineering and Sciencehttps://www.scopus.com/record/display.uri?eid=2-s2.0-85183938191&doi=10.11591%2fijere.v13i2.26717&origin=inward&txGid=1db2592ac1c8fbbf0a043c80f812a0ee