Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis

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dc.catalogadorpva
dc.contributor.authorPérez Sanagustín, Mar
dc.contributor.authorPérez Álvarez, Ronald Antonio
dc.contributor.authorMaldonado Mahauad, Jorge Javier
dc.contributor.authorVillalobos, Esteban
dc.contributor.authorHilliger, Isabel
dc.contributor.authorHernández Correa, Josefina María
dc.contributor.authorSapunar Opazo, Diego Andrés
dc.contributor.authorMoreno-Marcos, Pedro Manuel
dc.contributor.authorMuñoz-Merino, Pedro J.
dc.contributor.authorDelgado Kloos, Carlos
dc.contributor.authorImaz, Jon
dc.date.accessioned2025-03-13T20:46:17Z
dc.date.available2025-03-13T20:46:17Z
dc.date.issued2021
dc.description.abstractThis work in progress paper investigates if timely feedback increases learners' passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about: (1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.
dc.format.extent4 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1145/3430895.3460991
dc.identifier.isbn978-145038215-1
dc.identifier.scopusidSCOPUS_ID:85108120489
dc.identifier.urihttps://doi.org/10.1145/3430895.3460991
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-85108120489&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102590
dc.information.autorucEscuela de Ingeniería; Pérez Sanagustín, Mar; 0000-0001-9854-9963; 1015143
dc.information.autorucEscuela de Ingeniería; Pérez Álvarez, Ronald Antonio; 0000-0002-5544-0770; 1031026
dc.information.autorucEscuela de Ingeniería; Maldonado Mahauad, Jorge Javier; 0000-0003-1953-390X; 1020263
dc.information.autorucEscuela de Ingeniería; Villalobos, Esteban; S/I; 232411
dc.information.autorucEscuela de Ingeniería; Hilliger, Isabel; 0000-0001-5270-7655; 141681
dc.information.autorucEscuela de Ingeniería; Hernández Correa, Josefina María; 0000-0002-2422-3634; 170540
dc.information.autorucEscuela de Ingeniería; Sapunar Opazo, Diego Andrés; S/I; 232414
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final342
dc.pagina.inicio339
dc.publisherAssociation for Computing Machinery, Inc.
dc.relation.ispartof8th Annual ACM Conference on Learning at Scale, L@S 2021
dc.revistaL@S 2021 - Proceedings of the 8th ACM Conference on Learning @ Scale
dc.rightsacceso restringido
dc.subjectMOOC
dc.subjectSelf-regulated learning
dc.subjectFeedback
dc.subjectPrediction
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleCan Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis
dc.typecomunicación de congreso
sipa.codpersvinculados1015143
sipa.codpersvinculados1031026
sipa.codpersvinculados1020263
sipa.codpersvinculados232411
sipa.codpersvinculados141681
sipa.codpersvinculados170540
sipa.codpersvinculados232414
sipa.trazabilidadORCID;2025-03-03
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