An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure

Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical...

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Detalles Bibliográficos
Autores: Baneres, David, Rodríguez-González, M. Elena, Serra, Montse
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/152620
Acceso en línea:https://openaccess.uoc.edu/handle/10609/152620
http://doi.org/10.1109/TLT.2019.2912167
Access Level:acceso abierto
Palabra clave:predictive models
at-risk student
first-year student
personalized feedback
online learning
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spelling An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education CoureBaneres, DavidRodríguez-González, M. ElenaSerra, Montsepredictive modelsat-risk studentfirst-year studentpersonalized feedbackonline learningIdentifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management Systems store a large amount of data that could help to generate predictive models to early identification of students in online and blended learning. The contribution of this paper is twofold: First, a new adaptive predictive model is presented based only on students’ grades specifically trained for each course. A deep analysis is performed in the whole institution to evaluate its performance accuracy. Second, an early warning system is developed, focusing on dashboards visualization for stakeholders (i.e., students and teachers) and an early feedback prediction system to intervene in the case of at-risk identification. The early warning system has been evaluated in a case study on a first-year undergraduate course in computer science. We show the accuracy of the correct identification of at-risk students, the students’ appraisal, and the most common factors that lead to at-risk level.IEEE Computer Society / IEEE Education Society202520252019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://openaccess.uoc.edu/handle/10609/152620http://doi.org/10.1109/TLT.2019.2912167reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésIEEE Transactions on Learning Technologies, 2029, 12(2)2019 IEEEAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1526202026-05-28T12:42:01Z
dc.title.none.fl_str_mv An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
title An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
spellingShingle An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
Baneres, David
predictive models
at-risk student
first-year student
personalized feedback
online learning
title_short An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
title_full An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
title_fullStr An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
title_full_unstemmed An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
title_sort An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Coure
dc.creator.none.fl_str_mv Baneres, David
Rodríguez-González, M. Elena
Serra, Montse
author Baneres, David
author_facet Baneres, David
Rodríguez-González, M. Elena
Serra, Montse
author_role author
author2 Rodríguez-González, M. Elena
Serra, Montse
author2_role author
author
dc.subject.none.fl_str_mv predictive models
at-risk student
first-year student
personalized feedback
online learning
topic predictive models
at-risk student
first-year student
personalized feedback
online learning
description Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management Systems store a large amount of data that could help to generate predictive models to early identification of students in online and blended learning. The contribution of this paper is twofold: First, a new adaptive predictive model is presented based only on students’ grades specifically trained for each course. A deep analysis is performed in the whole institution to evaluate its performance accuracy. Second, an early warning system is developed, focusing on dashboards visualization for stakeholders (i.e., students and teachers) and an early feedback prediction system to intervene in the case of at-risk identification. The early warning system has been evaluated in a case study on a first-year undergraduate course in computer science. We show the accuracy of the correct identification of at-risk students, the students’ appraisal, and the most common factors that lead to at-risk level.
publishDate 2019
dc.date.none.fl_str_mv 2019
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://openaccess.uoc.edu/handle/10609/152620
http://doi.org/10.1109/TLT.2019.2912167
url https://openaccess.uoc.edu/handle/10609/152620
http://doi.org/10.1109/TLT.2019.2912167
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Learning Technologies, 2029, 12(2)
dc.rights.none.fl_str_mv 2019 IEEE
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv 2019 IEEE
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Computer Society / IEEE Education Society
publisher.none.fl_str_mv IEEE Computer Society / IEEE Education Society
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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