School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach

Author:

Colak Oz HazalORCID,Güven ÇiçekORCID,Nápoles GonzaloORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Transportation

Reference92 articles.

1. UNESCO Institute for Statistics (2019). Out-of-school children, adolescents and youth: Global status and trends. Fact Sheet no. 56. UIS/2019/ED/FS/56. Retrieved September 7, 2022 from: http://uis.unesco.org/sites/default/files/documents/new-methodology-shows-258-million-children-adolescents-and-youth-are-out-school.pdf

2. Huisman, J., & Smits, J. (2015). Keeping children in school: Effects of household and context characteristics on school dropout in 363 districts of 30 developing countries. SAGE Open. https://doi.org/10.1177/2158244015609666

3. Breton, T. R. (2004). Can institutions or education explain world poverty? An augmented Solow model provides some insights. Journal of Socio-Economics, 33, 45–69. https://doi.org/10.1016/j.socec.2003.12.004

4. World Bank. (2020). The human capital index 2020 update : Human capital in the time of COVID-19. Washington, DC.: World Bank Retrieved September 10, 2021 from https://openknowledge.worldbank.org/handle/10986/34432

5. Backman, O. (2017). High school dropout, resource attainment, and criminal convictions. Journal of Research in Crime and Delinquency, 54(5), 715–749. https://doi.org/10.1177/0022427817697441

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