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

Faculty of Habitat, Infrastructure, and Creativity

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450372848
DOIs	https://doi.org/10.1145/3368691.3368694
State	Published - 2 Dec 2019
Event	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 - Dubai, United Arab Emirates Duration: 2 Dec 2019 → 5 Dec 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
Country/Territory	United Arab Emirates
City	Dubai
Period	2/12/19 → 5/12/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery. ACM

Funding

This work was funded by the Department of Science and Innovation (Spain) under the National Program for Research, Development and Innovation: project TIN2017-83445-P. We have also received funds from the European Union, through the European Regional Development Funds (ERDF); and the Principality of Asturias, through its Science, Technology and Innovation Plan FC-GRUPIN-IDI/2018/000199.

Funders	Funder number
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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Keywords

Data fusion
Educational data mining
Multi-source data
Predicting student performance

Access to Document

10.1145/3368691.3368694

Cite this

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

@inproceedings{18a6b55ea4404366870c7787b954fbd3,

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",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

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

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Chango, W, Cerezo, R & Romero, C 2019, Predicting academic performance of university students from multi-sources data in blended learning. in 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, United Arab Emirates, 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).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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. In 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

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

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Cite this