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Anticipating Student Abandonment and Failure: Predictive Models in High School Settings

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Artificial Intelligence in Education (AIED 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14829))

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Abstract

Addressing the issue of high school non-completion poses a crucial challenge for contemporary education. This research introduces a machine learning-based methodology to identify students at risk of failure and abandonment in a specific Brazilian state, aiming to establish an early warning system utilizing academic, socioeconomic, and performance indicators for proactive interventions. The methodology followed here ensures the explainability of predictions and guards against bias in relation to certain features. The analysis of data from 79,165 students resulted in the creation of six accurate classification models, with accuracy rates ranging from 69.4% to 92.7%. This underscores the methodology’s effectiveness in identifying at-risk students, highlighting its potential to alleviate failure and abandonment. The implementation of this methodology could positively influence proactive educational policies and enhance educational metrics within the state.

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Acknowledgments

This work was supported by Inter American Development Bank and Unibanco Institute. Cristian Cechinel was partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq) (grants 305731/2021-1 and 409633/2022-4).

Author information

Authors and Affiliations

  1. Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Pelotas, Brazil

    Emanuel Marques Queiroga

  2. Inter American Development Bank (IDB), Brasília, Brazil

    Emanuel Marques Queiroga & Martim de Aguiar

  3. Center of Excellence for Social Technologies (NEES), Federal University of Alagoas (UFAL), Maceió, Brazil

    Emanuel Marques Queiroga, Ig Ibert Bittencourt & Cristian Cechinel

  4. Instituto Unibanco, São Paulo, Brazil

    Daniel Santana, Marcelo da Silva & Vinicius dos Santos

  5. Centro de Estudos e Sistemas Avançados do Recife (CESAR), Recife, Brazil

    Rafael Ferreira Mello & Cristian Cechinel

  6. Federal University of Santa Catarina (UFSC), Florianópolis, Brazil

    Cristian Cechinel

Authors
  1. Emanuel Marques Queiroga
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  2. Daniel Santana
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  3. Marcelo da Silva
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  4. Martim de Aguiar
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  5. Vinicius dos Santos
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  6. Rafael Ferreira Mello
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  7. Ig Ibert Bittencourt
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  8. Cristian Cechinel
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Corresponding author

Correspondence to Emanuel Marques Queiroga .

Editor information

Editors and Affiliations

  1. University of Memphis, Memphis, TN, USA

    Andrew M. Olney

  2. University of Duisburg-Essen, Duisburg, Germany

    Irene-Angelica Chounta

  3. Jinan University, Guangzhou, China

    Zitao Liu

  4. UNED, Madrid, Spain

    Olga C. Santos

  5. Universidade Federal de Alagoas, Maceio, Brazil

    Ig Ibert Bittencourt

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Queiroga, E.M. et al. (2024). Anticipating Student Abandonment and Failure: Predictive Models in High School Settings. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science(), vol 14829. Springer, Cham. https://doi.org/10.1007/978-3-031-64302-6_25

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  • DOI: https://doi.org/10.1007/978-3-031-64302-6_25

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  • Online ISBN: 978-3-031-64302-6

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