Support System to Predict Student Dropout in Universities

D. Rivero-Albarrán; L. Guerra Torrealba; S. Arciniegas Aguirre; Ortiz Alexander

doi:10.1007/978-981-19-6347-6_1

Support System to Predict Student Dropout in Universities

D. Rivero-Albarrán^*, L. Guerra Torrealba, S. Arciniegas Aguirre, Ortiz Alexander

^*Corresponding author for this work

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

1 Scopus citations

Abstract

The objective of this research was to provide a prediction system for the possibility of student dropout at the Pontificia Universidad Católica del Ecuador Sede Ibarra. It is applied research with a mixed approach. It was developed in two phases. In the first phase, the KDD methodology and the Scikit-Learn tool were applied to select the best prediction algorithm (KNN, Decision Tree, Random Forest, SVM, and Neural Network). In the second phase, the information system was built to make use of the model obtained in the first phase, where users will be able to consult the possibility of risks of academic dropout of students. Technologies such as Django, Python, HTML, JavaScript, and MySQL, among others, were used in this study. The results show an information system that allows consultation by the student, by the level of schooling, or by subject, based on neural networks that provide an accuracy of 92%.

Original language	English
Title of host publication	Communication and Applied Technologies - Proceedings of ICOMTA 2022
Editors	Paulo Carlos López-López, Ángel Torres-Toukoumidis, Andrea De-Santis, Óscar Avilés, Daniel Barredo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-12
Number of pages	10
ISBN (Print)	9789811963469
DOIs	https://doi.org/10.1007/978-981-19-6347-6_1
State	Published - 2023
Event	International Conference on Communication and Applied Technologies, ICOMTA 2022 - Cuenca, Ecuador Duration: 7 Sep 2022 → 9 Sep 2022

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	318
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	International Conference on Communication and Applied Technologies, ICOMTA 2022
Country/Territory	Ecuador
City	Cuenca
Period	7/09/22 → 9/09/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Data analytics
Data mining
Learning analytics
Predictive models
Student dropout

Access to Document

10.1007/978-981-19-6347-6_1

Cite this

Rivero-Albarrán, D., Guerra Torrealba, L., Arciniegas Aguirre, S., & Alexander, O. (2023). Support System to Predict Student Dropout in Universities. In P. C. López-López, Á. Torres-Toukoumidis, A. De-Santis, Ó. Avilés, & D. Barredo (Eds.), Communication and Applied Technologies - Proceedings of ICOMTA 2022 (pp. 3-12). (Smart Innovation, Systems and Technologies; Vol. 318). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-6347-6_1

Rivero-Albarrán, D. ; Guerra Torrealba, L. ; Arciniegas Aguirre, S. et al. / Support System to Predict Student Dropout in Universities. Communication and Applied Technologies - Proceedings of ICOMTA 2022. editor / Paulo Carlos López-López ; Ángel Torres-Toukoumidis ; Andrea De-Santis ; Óscar Avilés ; Daniel Barredo. Springer Science and Business Media Deutschland GmbH, 2023. pp. 3-12 (Smart Innovation, Systems and Technologies).

@inproceedings{a7bf8c9021824369a70f026d9cde114a,

title = "Support System to Predict Student Dropout in Universities",

abstract = "The objective of this research was to provide a prediction system for the possibility of student dropout at the Pontificia Universidad Cat{\'o}lica del Ecuador Sede Ibarra. It is applied research with a mixed approach. It was developed in two phases. In the first phase, the KDD methodology and the Scikit-Learn tool were applied to select the best prediction algorithm (KNN, Decision Tree, Random Forest, SVM, and Neural Network). In the second phase, the information system was built to make use of the model obtained in the first phase, where users will be able to consult the possibility of risks of academic dropout of students. Technologies such as Django, Python, HTML, JavaScript, and MySQL, among others, were used in this study. The results show an information system that allows consultation by the student, by the level of schooling, or by subject, based on neural networks that provide an accuracy of 92%.",

keywords = "Data analytics, Data mining, Learning analytics, Predictive models, Student dropout",

author = "D. Rivero-Albarr{\'a}n and {Guerra Torrealba}, L. and {Arciniegas Aguirre}, S. and Ortiz Alexander",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Communication and Applied Technologies, ICOMTA 2022 ; Conference date: 07-09-2022 Through 09-09-2022",

year = "2023",

doi = "10.1007/978-981-19-6347-6_1",

language = "English",

isbn = "9789811963469",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "3--12",

editor = "L{\'o}pez-L{\'o}pez, {Paulo Carlos} and {\'A}ngel Torres-Toukoumidis and Andrea De-Santis and {\'O}scar Avil{\'e}s and Daniel Barredo",

booktitle = "Communication and Applied Technologies - Proceedings of ICOMTA 2022",

}

Rivero-Albarrán, D, Guerra Torrealba, L , Arciniegas Aguirre, S & Alexander, O 2023, Support System to Predict Student Dropout in Universities. in PC López-López, Á Torres-Toukoumidis, A De-Santis, Ó Avilés & D Barredo (eds), Communication and Applied Technologies - Proceedings of ICOMTA 2022. Smart Innovation, Systems and Technologies, vol. 318, Springer Science and Business Media Deutschland GmbH, pp. 3-12, International Conference on Communication and Applied Technologies, ICOMTA 2022, Cuenca, Ecuador, 7/09/22. https://doi.org/10.1007/978-981-19-6347-6_1

Support System to Predict Student Dropout in Universities. / Rivero-Albarrán, D.; Guerra Torrealba, L.; Arciniegas Aguirre, S. et al.
Communication and Applied Technologies - Proceedings of ICOMTA 2022. ed. / Paulo Carlos López-López; Ángel Torres-Toukoumidis; Andrea De-Santis; Óscar Avilés; Daniel Barredo. Springer Science and Business Media Deutschland GmbH, 2023. p. 3-12 (Smart Innovation, Systems and Technologies; Vol. 318).

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

TY - GEN

T1 - Support System to Predict Student Dropout in Universities

AU - Rivero-Albarrán, D.

AU - Guerra Torrealba, L.

AU - Arciniegas Aguirre, S.

AU - Alexander, Ortiz

PY - 2023

Y1 - 2023

N2 - The objective of this research was to provide a prediction system for the possibility of student dropout at the Pontificia Universidad Católica del Ecuador Sede Ibarra. It is applied research with a mixed approach. It was developed in two phases. In the first phase, the KDD methodology and the Scikit-Learn tool were applied to select the best prediction algorithm (KNN, Decision Tree, Random Forest, SVM, and Neural Network). In the second phase, the information system was built to make use of the model obtained in the first phase, where users will be able to consult the possibility of risks of academic dropout of students. Technologies such as Django, Python, HTML, JavaScript, and MySQL, among others, were used in this study. The results show an information system that allows consultation by the student, by the level of schooling, or by subject, based on neural networks that provide an accuracy of 92%.

AB - The objective of this research was to provide a prediction system for the possibility of student dropout at the Pontificia Universidad Católica del Ecuador Sede Ibarra. It is applied research with a mixed approach. It was developed in two phases. In the first phase, the KDD methodology and the Scikit-Learn tool were applied to select the best prediction algorithm (KNN, Decision Tree, Random Forest, SVM, and Neural Network). In the second phase, the information system was built to make use of the model obtained in the first phase, where users will be able to consult the possibility of risks of academic dropout of students. Technologies such as Django, Python, HTML, JavaScript, and MySQL, among others, were used in this study. The results show an information system that allows consultation by the student, by the level of schooling, or by subject, based on neural networks that provide an accuracy of 92%.

KW - Data analytics

KW - Data mining

KW - Learning analytics

KW - Predictive models

KW - Student dropout

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DO - 10.1007/978-981-19-6347-6_1

M3 - Conference contribution

AN - SCOPUS:85144175736

SN - 9789811963469

T3 - Smart Innovation, Systems and Technologies

SP - 3

EP - 12

BT - Communication and Applied Technologies - Proceedings of ICOMTA 2022

A2 - López-López, Paulo Carlos

A2 - Torres-Toukoumidis, Ángel

A2 - De-Santis, Andrea

A2 - Avilés, Óscar

A2 - Barredo, Daniel

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Communication and Applied Technologies, ICOMTA 2022

Y2 - 7 September 2022 through 9 September 2022

ER -

Rivero-Albarrán D, Guerra Torrealba L , Arciniegas Aguirre S, Alexander O. Support System to Predict Student Dropout in Universities. In López-López PC, Torres-Toukoumidis Á, De-Santis A, Avilés Ó, Barredo D, editors, Communication and Applied Technologies - Proceedings of ICOMTA 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 3-12. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-19-6347-6_1

Support System to Predict Student Dropout in Universities

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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