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Modelling the effect of deprived physical urban environments on academic performance in the Philippines

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Abstract

This study investigates how physical urban environments affect academic performance of urban public elementary schools in the Philippines by analysing the physical environment of school facilities and slum areas. Global, local, and semi-parametric regression analyses indicate that there is disproportionate provision of resources among the government schools and that lower academic performance is associated with the provision of fewer clinics rather than the proximity to poverty hotspots. Semiparametric, geographically weighted regression modelling outperformed global and local modelling, and estimated up to 30 % of the variation in math scores where the semi-parametric regression model is based on each school’s number of teachers and rooms, building conditions, availability of health clinics, and the location of slum areas near the school. On the basis of the research findings, it is concluded that the current state of school buildings is adequate and is a lower priority than the provision of health care support and smaller pupil–teacher ratios. Hence, government programs that aim to enhance the academic performance of children from the deprived physical urban environments should prioritize the provision of health clinics as well as maintaining few large schools with small pupil–teacher ratios.

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Notes

  1. The population of Quezon City at the 2010 Census was 2.7 million.

  2. In the Philippines, children start primary school at age 6.

  3. Only 20 % of the elementary schools in Quezon City gave an updated report of their school facility data in 2011.

  4. Size (area) of informal settlements, intensity of poverty (kernel density estimation of informal settlements), school location within poverty hotspot/coldspot, school’s proximity to the central business district, town’s commercial land value and population size.

  5. School’s mean building condition score.

  6. Presence of a school library, health clinic and canteen.

  7. Total school buildings, total classrooms by type, ratio of total pupils to total classrooms, total teachers, total toilets by type and ratio of pupils to toilets by gender.

  8. Exploratory regression in ArcMap 10.3.

  9. In regression models, residuals are often used to measure the differences between the actual data and the models. In this study, the residual represents the difference between the actual math score of school i and the estimated math score from Eq. 1.

  10. Most dwellings in informal settlements are 3 m apart but some can be 15 m apart if the lots are unusually large and if the alleys (which do not follow rigid rules and is spontaneously allocated as the need arises over a period of time), streets, and shared open spaces for laundry or gardening are accounted for (Cabalfin 2014).

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Acknowledgments

The authors would like to thank the School Mapping Unit of the Department of Education, Philippines as well as Dr. Wilmina Lara of Geodata Systems Technologies for allowing the use of their data. This research was supported by the Engineering Research and Development for Technology—Human Resource Development Program (ERDT-HRD) of the Department of Science and Technology, Philippines.

Funding

This study was funded by the Engineering Research and Development for Technology—Human Resource Development Program (ERDT-HRD) of the Department of Science and Technology, Philippines.

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Authors and Affiliations

  1. School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia

    Ligaya Leah Lara Figueroa & Samsung Lim

  2. School of Education, University of New South Wales, Sydney, Australia

    Jihyun Lee

  3. Department of Computer Science, University of the Philippines, Diliman, Philippines

    Ligaya Leah Lara Figueroa

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Ligaya Leah Figueroa has received research grants from the funding organization. Samsung Lim and Jihyun Lee has no relationship with the funding organization.

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Figueroa, L.L.L., Lim, S. & Lee, J. Modelling the effect of deprived physical urban environments on academic performance in the Philippines. GeoJournal 83, 13–30 (2018). https://doi.org/10.1007/s10708-016-9751-x

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