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

PUCE Ibarra

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

1 Cita (Scopus)

Resumen

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%.

Idioma original	Inglés
Título de la publicación alojada	Communication and Applied Technologies - Proceedings of ICOMTA 2022
Editores	Paulo Carlos López-López, Ángel Torres-Toukoumidis, Andrea De-Santis, Óscar Avilés, Daniel Barredo
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	3-12
Número de páginas	10
ISBN (versión impresa)	9789811963469
DOI	https://doi.org/10.1007/978-981-19-6347-6_1
Estado	Publicada - 2023
Evento	International Conference on Communication and Applied Technologies, ICOMTA 2022 - Cuenca, Ecuador Duración: 7 sep. 2022 → 9 sep. 2022

Serie de la publicación

Nombre	Smart Innovation, Systems and Technologies
Volumen	318
ISSN (versión impresa)	2190-3018
ISSN (versión digital)	2190-3026

Conferencia

Conferencia	International Conference on Communication and Applied Technologies, ICOMTA 2022
País/Territorio	Ecuador
Ciudad	Cuenca
Período	7/09/22 → 9/09/22

Nota bibliográfica

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

ODS de las Naciones Unidas

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

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Rivero-Albarrán, D., Guerra Torrealba, L., Arciniegas Aguirre, S., & Alexander, O. (2023). Support System to Predict Student Dropout in Universities. En 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",

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Rivero-Albarrán, D, Guerra Torrealba, L , Arciniegas Aguirre, S & Alexander, O 2023, Support System to Predict Student Dropout in Universities. En 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).

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

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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U2 - 10.1007/978-981-19-6347-6_1

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

M3 - Conference contribution

AN - SCOPUS:85144175736

SN - 9789811963469

T3 - Smart Innovation, Systems and Technologies

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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. En López-López PC, Torres-Toukoumidis Á, De-Santis A, Avilés Ó, Barredo D, editores, 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

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