Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

Wilson Chango; Rebeca Cerezo; Miguel Sanchez-Santillan; Roger Azevedo; Cristóbal Romero

doi:10.1007/s12528-021-09298-8

Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

Wilson Chango
, Rebeca Cerezo^*
, Miguel Sanchez-Santillan
, Roger Azevedo
, Cristóbal Romero

^*Corresponding author for this work

Faculty of Habitat, Infrastructure, and Creativity

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Original language	English
Pages (from-to)	614-634
Number of pages	21
Journal	Journal of Computing in Higher Education
Volume	33
Issue number	3
DOIs	https://doi.org/10.1007/s12528-021-09298-8
State	Published - 28 Oct 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Funding

The authors acknowledge the financial subsidy provided by the Spanish Ministry of Science and Innovation “Deteccion Temprana e Intervencion en Dificultades del Aprendizaje Específicas desde el Modelo RtI” (PID2019-107201GB-I009), and the Spanish Ministry of Science and Innovation in the project “Improving Data Science User’s Experience with Computational Intelligence (INTENSE)” (PID2020-115832GB-I00).

Funders	Funder number
Ministerio de Ciencia e Innovación	PID2020-115832GB-I00, PID2019-107201GB-I009

UN SDGs

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

SDG 4 Quality Education

Keywords

Data fusion
Intelligent tutoring systems
Multimodal learning
Multisource data
Predicting academic performance

Access to Document

10.1007/s12528-021-09298-8

Cite this

@article{ebc6bef410ea4224a6d49f2887916396,

title = "Improving prediction of students{\textquoteright} performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources",

keywords = "Data fusion, Intelligent tutoring systems, Multimodal learning, Multisource data, Predicting academic performance",

author = "Wilson Chango and Rebeca Cerezo and Miguel Sanchez-Santillan and Roger Azevedo and Crist{\'o}bal Romero",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = oct,

day = "28",

doi = "10.1007/s12528-021-09298-8",

language = "English",

volume = "33",

pages = "614--634",

journal = "Journal of Computing in Higher Education",

issn = "1042-1726",

publisher = "Springer US",

number = "3",

}

Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources. / Chango, Wilson; Cerezo, Rebeca; Sanchez-Santillan, Miguel et al.
In: Journal of Computing in Higher Education, Vol. 33, No. 3, 28.10.2021, p. 614-634.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

AU - Chango, Wilson

AU - Cerezo, Rebeca

AU - Sanchez-Santillan, Miguel

AU - Azevedo, Roger

AU - Romero, Cristóbal

PY - 2021/10/28

Y1 - 2021/10/28

KW - Data fusion

KW - Intelligent tutoring systems

KW - Multimodal learning

KW - Multisource data

KW - Predicting academic performance

UR - https://link.springer.com/article/10.1007%2Fs12528-021-09298-8

U2 - 10.1007/s12528-021-09298-8

DO - 10.1007/s12528-021-09298-8

M3 - Article

SN - 1042-1726

VL - 33

SP - 614

EP - 634

JO - Journal of Computing in Higher Education

JF - Journal of Computing in Higher Education

IS - 3

ER -

Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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Cite this