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Comparison of Predictive Models with Balanced Classes for the Forecast of Student Dropout in Higher Education

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection (PAAMS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1472))

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Abstract

Based on the premise that university student dropout is a social problem in the university ecosystem of any country, technological leverage is a way that allows us to build technological proposals to solve a poorly met need in university education systems. Under this scenario, the study presents and analyzes eight predictive models to forecast university dropout, based on data mining methods and techniques, using WEKA for its implementation, with a dataset of 4365 academic records of students from the National University of Moquegua (UNAM), in Peru. The objective is to determine which model presents the best performance indicators to forecast and hence prevent student dropout. The aim of the study is to propose and compare the accuracy of eight predictive models with balanced classes, using the SMOTE method for the generation of synthetic data. The results allow us to confirm that the predictive model based on Random Forest is the one that presents the highest accuracy and robustness.

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Notes

  1. 1.

    https://www.the-modeling-agency.com/crisp-dm.pdf.

  2. 2.

    Attribute selection algorithms used: OneR Attribute Evaluation, Relief Factor Attribute Evaluation, Info Gain Attribute Evaluation, Gain Ratio Attribute Evaluation, and Symmetrical Uncertainty Attribute Evaluation.

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Acknowledgements

This work is partially supported by the Spanish Government project TIN2017-89156-R, and the Valencian Government project PROMETEO/2018/002. The research was developed thanks to the support of the National University of Moquegua, which provided the information for the creation of the dataset.

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Authors and Affiliations

  1. National University of Moquegua (UNAM), Moquegua, Peru

    Vaneza Flores

  2. Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politécnica de Valéncia (UPV), Valencia, Spain

    Stella Heras & Vicente Julián

Authors
  1. Vaneza Flores
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  2. Stella Heras
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  3. Vicente Julián
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Corresponding author

Correspondence to Stella Heras .

Editor information

Editors and Affiliations

  1. University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. German University in Cairo, New Cairo City, Egypt

    Alia El Bolock

  3. University of Minho, Braga, Portugal

    Dalila Durães

  4. Polytechnic Institute of Porto, ISEP, Porto, Portugal

    João Carneiro

  5. National Laboratory of Energy and Geology, Lisbon, Portugal

    Fernando Lopes

  6. Polytechnic University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

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Flores, V., Heras, S., Julián, V. (2021). Comparison of Predictive Models with Balanced Classes for the Forecast of Student Dropout in Higher Education. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Communications in Computer and Information Science, vol 1472. Springer, Cham. https://doi.org/10.1007/978-3-030-85710-3_12

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  • DOI: https://doi.org/10.1007/978-3-030-85710-3_12

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