Predicting academic performance of university students from multi-sources data in blended learning

Wilson Chango; Rebeca Cerezo; Cristóbal Romero

doi:10.1145/3368691.3368694

Predicting academic performance of university students from multi-sources data in blended learning

Wilson Chango
, Rebeca Cerezo
, Cristóbal Romero

Facultad de Hábitat, Infraestructura y Creatividad

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

12 Citas (Scopus)

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
Editorial	Association for Computing Machinery
ISBN (versión digital)	9781450372848
DOI	https://doi.org/10.1145/3368691.3368694
Estado	Publicada - 2 dic. 2019
Evento	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 - Dubai, Emiratos Árabes Unidos Duración: 2 dic. 2019 → 5 dic. 2019

Serie de la publicación

Nombre	ACM International Conference Proceeding Series

Conferencia

Conferencia	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
País/Territorio	Emiratos Árabes Unidos
Ciudad	Dubai
Período	2/12/19 → 5/12/19

Nota bibliográfica

Publisher Copyright:
© 2019 Association for Computing Machinery. ACM

Financiación

Financiadores	Número del financiador
Department of Science and Innovation
European Regional Development Funds
National Program for Research, Development and Innovation	TIN2017-83445-P
Principality of Asturias
Science, Technology and Innovation Plan FC-GRUPIN-IDI/2018/000199
Gobierno del Principado de Asturias
European Commission
National Plan for Science, Technology and Innovation
European Regional Development Fund

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 4: Educación de calidad

Acceder al documento

10.1145/3368691.3368694

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

Chango, W., Cerezo, R., & Romero, C. (2019). Predicting academic performance of university students from multi-sources data in blended learning. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 Artículo a3 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3368691.3368694

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title = "Predicting academic performance of university students from multi-sources data in blended learning",

keywords = "Data fusion, Educational data mining, Multi-source data, Predicting student performance",

author = "Wilson Chango and Rebeca Cerezo and Crist{\'o}bal Romero",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery. ACM; 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 ; Conference date: 02-12-2019 Through 05-12-2019",

year = "2019",

month = dec,

day = "2",

doi = "10.1145/3368691.3368694",

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Chango, W, Cerezo, R & Romero, C 2019, Predicting academic performance of university students from multi-sources data in blended learning. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019., a3, ACM International Conference Proceeding Series, Association for Computing Machinery, 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019, Dubai, Emiratos Árabes Unidos, 2/12/19. https://doi.org/10.1145/3368691.3368694

Predicting academic performance of university students from multi-sources data in blended learning. / Chango, Wilson; Cerezo, Rebeca; Romero, Cristóbal.
Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019. Association for Computing Machinery, 2019. a3 (ACM International Conference Proceeding Series).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

T1 - Predicting academic performance of university students from multi-sources data in blended learning

AU - Chango, Wilson

AU - Cerezo, Rebeca

AU - Romero, Cristóbal

PY - 2019/12/2

Y1 - 2019/12/2

KW - Data fusion

KW - Educational data mining

KW - Multi-source data

KW - Predicting student performance

UR - https://www.scopus.com/pages/publications/85077127924

U2 - 10.1145/3368691.3368694

DO - 10.1145/3368691.3368694

M3 - Conference contribution

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019

PB - Association for Computing Machinery

T2 - 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019

Y2 - 2 December 2019 through 5 December 2019

ER -

Chango W, Cerezo R, Romero C. Predicting academic performance of university students from multi-sources data in blended learning. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019. Association for Computing Machinery. 2019. a3. (ACM International Conference Proceeding Series). doi: 10.1145/3368691.3368694