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Topological Properties of Inequality and Deprivation in an Educational System: Unveiling the Key-Drivers Through Complex Network Analysis

  • Harvey Sánchez-RestrepoEmail author
  • Jorge LouçãEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)

Abstract

This research conceives an educational system as a complex network to incorporate a rich framework for analyzing topological and statistical properties of inequality and learning deprivation at different levels, as well as to simulate the structure, stability and fragility of the educational system. The model provides a natural way to represent educational phenomena, allowing to test public policies by computation before being implemented, bringing the opportunity of calibrating control parameters for assessing order parameters over time in multiple territorial scales. This approach provides a set of unique advantages over classical analysis tools because it allows the use of large-scale assessments and other evidences for combining the richness of qualitative analysis with quantitative inferences for measuring inequality gaps. An additional advantage, as shown in our results using real data from a Latin American country, is to provide a solution to concerns about the limitations of case studies or isolated statistical approaches.

Keywords

Complex network Educational deprivation Inequality Large-scale assessments Policy informatics 

Notes

Acknowledgments

We would like to thank ISCTE’s Programme in Complexity Sciences, the University of Lisbon and the Latin American Agency for Evaluation and Public Policy, for their support to the project as well as the authors of the articles mentioned in the references.

References

  1. 1.
    UN DESA. The Sustainable Development Goals Report 2018, UN, New York (2018)Google Scholar
  2. 2.
    Shee, N.K.: Karen Education Department’s multilingual education for language maintenance, Kasetsart J. Soc. Sci. (2018)Google Scholar
  3. 3.
    Johnson, J.: Hypernetworks in the Science of Complex Systems. Imperial College Press, London (2015)zbMATHGoogle Scholar
  4. 4.
    Sánchez, H.: Equity: the focal point of educational quality. Natl. Educ. Eval. Policy Gaz. 4(10), 42–44 (2018)Google Scholar
  5. 5.
    Kivela, M., Arenas, A., et al.: Multilayer networks. J. Complex Netw. 2(3), 203–271 (2014)CrossRefGoogle Scholar
  6. 6.
    Mizuno, K., Tanaka, M., Fukuda, S., Imai-Matsumura, K., Watanabe, Y.: Relationship between cognitive functions and prevalence of fatigue in elementary and junior high school students. Brain Dev. 33(6), 470–479 (2011)CrossRefGoogle Scholar
  7. 7.
    Price, H.E., Moolenaar, N.M.: Principal-teacher relationships: foregrounding the international importance of principals’ social relationships for school learning climates. J. Educ. Adm. 53(1), 8–39 (2015)CrossRefGoogle Scholar
  8. 8.
    Catalan Najera, H.E.: Multiple deprivation, severity and latent sub-groups: advantages of factor mixture modelling for analysing material deprivation. Soc. Ind. Res. 131(2), 681–700 (2016)CrossRefGoogle Scholar
  9. 9.
    OECD. Equity in Education: Breaking Down Barriers to Social Mobility, PISA, OECD Publishing, Paris (2018)Google Scholar
  10. 10.
    Gray, J., Kruse, S., Tarter, C.J.: Enabling school structures, collegial trust and academic emphasis: antecedents of professional learning communities. Educ. Manage. Adm. Leadersh. 44(6), 875–891 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Information Sciences, Technologies and Architecture Research Center (ISTA), ISCTE-IULLisbonPortugal

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