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Inequality in Learning Outcomes: Unveiling Educational Deprivation Through Complex Network Analysis

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

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Abstract

Understanding which factors are determinant to guarantee the human right to education entails the study of a large number of non-linear relationships among multiple agents and their impact on the properties of the entire system. Complex network analysis of large-scale assessment results provides a set of unique advantages over classical tools for facing the challenge of measuring inequality gaps in learning outcomes and recognizing those factors associated with educational deprivation, combining the richness of qualitative analysis with quantitative inferences.

This study establishes two milestones in educational research using a census high-quality data from a Latin American country. The first one is to provide a direct method to recognize the structure of inequality and the relationship between social determinants as ethnicity, socioeconomic status of students, rurality of the area and type of school funding and educational deprivation. The second one focus in unveil and hierarchize educational and non-educational factors associated with the conditional distribution of learning outcomes. This contribution provides new tools to current theoretical framework for discovering non-trivial relationships in educational phenomena, helping policymakers to address the challenge of ensuring inclusive and equitable education for those historically marginalized population groups.

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Notes

  1. 1.

    Full dataset is available in http://www.evaluacion.gob.ec/evaluaciones/descarga-de-datos/.

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Author information

Authors and Affiliations

  1. University of Lisbon, Cidade Universitária, 1649-004, Lisbon, Portugal

    Harvey Sánchez-Restrepo

  2. Information Sciences, Technologies and Architecture Research Center, ISCTE-IUL, 1649-026, Lisbon, Portugal

    Jorge Louçã

Authors
  1. Harvey Sánchez-Restrepo
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  2. Jorge Louçã
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Corresponding author

Correspondence to Harvey Sánchez-Restrepo .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Sánchez-Restrepo, H., Louçã, J. (2020). Inequality in Learning Outcomes: Unveiling Educational Deprivation Through Complex Network Analysis. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_27

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