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Sánchez-Restrepo, H. & Louçã, J. (2019). Inequality in learning outcomes: Unveiling educational deprivation through complex network analysis. In Cherifi, H., Gaito, S., Mendes, J. F., Moro, E., and Rocha, L. M. (Ed.), Complex Networks and Their Applications VIII. Studies in Computational Intelligence. (pp. 325-336). Lisboa: Springer International Publishing.

H. S. Restrepo and J. M. Louçã,  "Inequality in learning outcomes: Unveiling educational deprivation through complex network analysis", in Complex Networks and Their Applications VIII. Studies in Computational Intelligence, Cherifi, H., Gaito, S., Mendes, J. F., Moro, E., and Rocha, L. M., Ed., Lisboa, Springer International Publishing, 2019, vol. 882, pp. 325-336

@inproceedings{restrepo2019_1775709142379,
	author = "Sánchez-Restrepo, H. and Louçã, J.",
	title = "Inequality in learning outcomes: Unveiling educational deprivation through complex network analysis",
	booktitle = "Complex Networks and Their Applications VIII. Studies in Computational Intelligence",
	year = "2019",
	editor = "Cherifi, H., Gaito, S., Mendes, J. F., Moro, E., and Rocha, L. M.",
	volume = "882",
	number = "",
	series = "",
	doi = "10.1007/978-3-030-36683-4_27",
	pages = "325-336",
	publisher = "Springer International Publishing",
	address = "Lisboa",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-030-36683-4?wt_mc=Internal.Event.1.SEM.ChapterAuthorCongrat"
}

TY  - CPAPER
TI  - Inequality in learning outcomes: Unveiling educational deprivation through complex network analysis
T2  - Complex Networks and Their Applications VIII. Studies in Computational Intelligence
VL  - 882
AU  - Sánchez-Restrepo, H.
AU  - Louçã, J.
PY  - 2019
SP  - 325-336
SN  - 1860-949X
DO  - 10.1007/978-3-030-36683-4_27
CY  - Lisboa
UR  - https://link.springer.com/book/10.1007/978-3-030-36683-4?wt_mc=Internal.Event.1.SEM.ChapterAuthorCongrat
AB  - 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.
ER  -