IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES

Daniel Plúa Morán; Mónica Martínez Gómez; Víctor Yeste

IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES

Daniel Plúa Morán
, Mónica Martínez Gómez
, Víctor Yeste

Research Group in Artificial Intelligence and Facial Recognition (GIIAR)

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

Abstract

This document presents the development of a classification model to analyze the factors that influence a student at the Universidad Politécnica Salesiana to drop out of their degree program. This analysis is based on data provided by the university. The approach is based on classifications using decision trees. The methodology follows the Knowledge Discovery in Databases (KDD) process and consists of five steps: selection, processing, transformation, data mining, and evaluation. Using Python's Classification and Regression Tree (CART) algorithm, a tree with five levels and seventeen rules was created to identify potential dropouts. It concludes that factors such as the level of studies, academic performance, and the number of subjects taken by the student in a term are decisive in the decision to drop out.

Original language	English
Title of host publication	Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024
Editors	Demetrios G. Sampson, Dirk Ifenthaler, Dirk Ifenthaler, Pedro Isaias, Pedro Isaias, Luis Rodrigues
Publisher	IADIS Press
Pages	345-348
Number of pages	4
ISBN (Electronic)	9789898704610
State	Published - 2024
Event	21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024 - Zagreb, Croatia Duration: 26 Oct 2024 → 28 Oct 2024

Publication series

Name	Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

Conference

Conference	21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024
Country/Territory	Croatia
City	Zagreb
Period	26/10/24 → 28/10/24

Bibliographical note

Publisher Copyright:
© 2024 Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. All rights reserved.

Keywords

Decision Tree
KDD
Student Dropout

CACES Knowledge Areas

316A Software and Applications Development and Analysis

Cite this

Morán, D. P., Gómez, M. M., & Yeste, V. (2024). IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES. In D. G. Sampson, D. Ifenthaler, D. Ifenthaler, P. Isaias, P. Isaias, & L. Rodrigues (Eds.), Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024 (pp. 345-348). (Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024). IADIS Press.

Morán, Daniel Plúa ; Gómez, Mónica Martínez ; Yeste, Víctor. / IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES. Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. editor / Demetrios G. Sampson ; Dirk Ifenthaler ; Dirk Ifenthaler ; Pedro Isaias ; Pedro Isaias ; Luis Rodrigues. IADIS Press, 2024. pp. 345-348 (Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024).

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abstract = "This document presents the development of a classification model to analyze the factors that influence a student at the Universidad Polit{\'e}cnica Salesiana to drop out of their degree program. This analysis is based on data provided by the university. The approach is based on classifications using decision trees. The methodology follows the Knowledge Discovery in Databases (KDD) process and consists of five steps: selection, processing, transformation, data mining, and evaluation. Using Python's Classification and Regression Tree (CART) algorithm, a tree with five levels and seventeen rules was created to identify potential dropouts. It concludes that factors such as the level of studies, academic performance, and the number of subjects taken by the student in a term are decisive in the decision to drop out.",

keywords = "Decision Tree, KDD, Student Dropout",

author = "Mor{\'a}n, \{Daniel Pl{\'u}a\} and G{\'o}mez, \{M{\'o}nica Mart{\'i}nez\} and V{\'i}ctor Yeste",

note = "Publisher Copyright: {\textcopyright} 2024 Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. All rights reserved.; 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024 ; Conference date: 26-10-2024 Through 28-10-2024",

year = "2024",

language = "English",

series = "Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024",

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pages = "345--348",

editor = "Sampson, \{Demetrios G.\} and Dirk Ifenthaler and Dirk Ifenthaler and Pedro Isaias and Pedro Isaias and Luis Rodrigues",

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}

Morán, DP, Gómez, MM & Yeste, V 2024, IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES. in DG Sampson, D Ifenthaler, D Ifenthaler, P Isaias, P Isaias & L Rodrigues (eds), Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024, IADIS Press, pp. 345-348, 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024, Zagreb, Croatia, 26/10/24.

IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES. / Morán, Daniel Plúa; Gómez, Mónica Martínez; Yeste, Víctor.
Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. ed. / Demetrios G. Sampson; Dirk Ifenthaler; Dirk Ifenthaler; Pedro Isaias; Pedro Isaias; Luis Rodrigues. IADIS Press, 2024. p. 345-348 (Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024).

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

TY - GEN

T1 - IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES

AU - Morán, Daniel Plúa

AU - Gómez, Mónica Martínez

AU - Yeste, Víctor

PY - 2024

Y1 - 2024

N2 - This document presents the development of a classification model to analyze the factors that influence a student at the Universidad Politécnica Salesiana to drop out of their degree program. This analysis is based on data provided by the university. The approach is based on classifications using decision trees. The methodology follows the Knowledge Discovery in Databases (KDD) process and consists of five steps: selection, processing, transformation, data mining, and evaluation. Using Python's Classification and Regression Tree (CART) algorithm, a tree with five levels and seventeen rules was created to identify potential dropouts. It concludes that factors such as the level of studies, academic performance, and the number of subjects taken by the student in a term are decisive in the decision to drop out.

AB - This document presents the development of a classification model to analyze the factors that influence a student at the Universidad Politécnica Salesiana to drop out of their degree program. This analysis is based on data provided by the university. The approach is based on classifications using decision trees. The methodology follows the Knowledge Discovery in Databases (KDD) process and consists of five steps: selection, processing, transformation, data mining, and evaluation. Using Python's Classification and Regression Tree (CART) algorithm, a tree with five levels and seventeen rules was created to identify potential dropouts. It concludes that factors such as the level of studies, academic performance, and the number of subjects taken by the student in a term are decisive in the decision to drop out.

KW - Decision Tree

KW - KDD

KW - Student Dropout

UR - https://www.scopus.com/pages/publications/85214030908

M3 - Conference contribution

AN - SCOPUS:85214030908

T3 - Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

SP - 345

EP - 348

BT - Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

A2 - Sampson, Demetrios G.

A2 - Ifenthaler, Dirk

A2 - Isaias, Pedro

A2 - Rodrigues, Luis

PB - IADIS Press

T2 - 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

Y2 - 26 October 2024 through 28 October 2024

ER -

Morán DP, Gómez MM, Yeste V. IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES. In Sampson DG, Ifenthaler D, Ifenthaler D, Isaias P, Isaias P, Rodrigues L, editors, Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. IADIS Press. 2024. p. 345-348. (Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024).

IDENTIFYING INFLUENTIAL FACTORS IN STUDENT DROPOUT USING DECISION TREES

Abstract

Publication series

Conference

Bibliographical note

Keywords

CACES Knowledge Areas

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Cite this