Identifying Key Factors of Student Dropout through Random Forest: A Data-Driven Approach

DOI

10.2991/978-94-6463-868-4_46How to use a DOI?

Keywords

Random Forest; KDD; Student Dropout; Educational Data Mining

Abstract

The article presents the Random Forest model used to identify the primary factors contributing to student desertion at the Salesian Polytechnic University. This analysis utilizes academic data in conjunction with the demographic data of this institution of higher education. In comparison with other models, Random Forest is used in this article because it is one of the most robust and interpretable models, as it utilizes multiple decision trees for prediction. The methodology employs the KDD method, comprising five stages: selection, preprocessing, transformation, data mining, and evaluation. The results obtained show that factors such as academic performance and level of study are the most predictive of dropout.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Daniel Plúa Morán
AU  - Monica Martinez Goméz
AU  - Víctor Yeste
PY  - 2025
DA  - 2025/10/22
TI  - Identifying Key Factors of Student Dropout through Random Forest: A Data-Driven Approach
BT  - Proceedings of the International Conference on Communication and Applied Technologies 2025 (ICOMTA 2025)
PB  - Atlantis Press
SP  - 504
EP  - 514
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-868-4_46
DO  - 10.2991/978-94-6463-868-4_46
ID  - Morán2025
ER  -