| Literature DB >> 30717301 |
Matthew H E M Browning1, Alessandro Rigolon2.
Abstract
Background: Scholars and policymakers have criticized public education in developed countries for perpetuating health and income disparities. Several studies have examined the ties between green space and academic performance, hypothesizing that green space can foster performance, and, over time, help reduce such disparities. Although numerous reviews have analyzed the link between nature and child health, none have focused on academic achievement.Entities:
Keywords: academic achievement; academic performance; education; green space; nature; schools; test scores
Mesh:
Year: 2019 PMID: 30717301 PMCID: PMC6388261 DOI: 10.3390/ijerph16030429
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1PRISMA flow diagram depicting results of search, screening and selection processes.
Search expressions by database used in the review.
| Database | Keyword Search |
|---|---|
| Web of Science | ALL FIELDS: ((“green space” OR “greenness” OR “greenspace” OR “tree cover*” OR “natural environment*” OR “nearby nature”) AND (“academic performance” OR “academic achievement” OR “test score*” OR “standardized test*” OR “semester grade*”)). Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC. a |
| Scopus | TITLE-ABS-KEY ((“green space” OR “greenness” OR “greenspace” OR “tree cover*” OR “natural environment*” OR “nearby nature”) AND (“academic performance” OR “academic achievement” OR “test score*” OR “standardized test*” OR “semester grade*”)) AND DOCTYPE (ar) |
| Education Resources Information Center (ERIC) | (“green space” OR “greenness” OR “greenspace” OR “tree cover*” OR “natural environment*” OR “nearby nature”) AND (“academic performance” OR “academic achievement” OR “test score*” OR “standardized test*” OR “semester grade*”) |
a These constitute the Web of Science Core Collection.
Methodological biases in sampled papers.
| Bias Category | Biases Identified |
|---|---|
| Study design |
Randomized control trial rather than observational data (4 pts.) For observational studies, multiple years of data for outcome variable (1 pt.) For observational studies, individual-level data for outcome variable (1 pt.) |
| Confounding |
Adequate control for confounding variables, specifically socioeconomic status (SES) (2 pt.) Rationale for selection and inclusion of control variables (little or no rationale = 0 pt., empirical or theoretical rationale = 1 pt., both empirical and theoretical rationale = 2 pt.) |
| Statistics |
Used appropriate statistical analyses for given dataset(s) and research question(s), such as a detailed description of the statistical technique used, explanation why this technique was chosen, and discussion of caveats regarding the conclusions drawn from analyses using this technique [ Performed sensitivity test(s), for instance, differential effects by urbanization, gender, SES, or distances in which green space was measured (1 pt.) Tested for potential non-linear relationships between green space and outcome, for instance, splitting green space into deciles or tertiles (1 pt.) Corrected for correlation between variables using a reasonable cut-off value (VIF < 3.0) (1 pt.) Did not consider pairwise error rates when reporting a large number of analyses, which affect Type I (false positive) error rates [ For geospatial analyses, did NOT control for spatial autocorrelation, which results in correlated residuals and unreliable model results [ For multi-year studies, did NOT control for temporal autocorrelation, which also lead to correlated residuals and unreliable model results [ |
| Exposure assessment (for geospatial studies that rely on large datasets to measure green space) |
Multiple seasons of green space data to control for seasonal fluctuations in measurement [ Multiple years of green space data to control for annual fluctuations in climate affecting measurement [ High resolution green space data (more than 50 m = 0 pt., 20 m to 50 m = 1 pt., 1 m or less, 2 pt.) to limit under- or over-estimating green space quantity across urban-rural gradients [ Green space data not aligned in time with educational outcomes, for example green space data from 2004 and educational outcomes from 2012 [ |
Figure 2Counts of findings by outcome and green space measure with symbols representing the following associations between academic performance and green space: + = statistically significant positive association, 0 = non-statistically significant association, and - = statistically significant negative association. Metric distances (e.g., “Up to 250 m”) represent the radius of circular buffers or diameter of polygons centered on a school. Statistical significance requires p < 0.05 for 50% or more of analyses summarized in articles that included a measure or proxy for socioeconomic status (i.e., the percentage of students eligible free-or-reduced lunch) because SES often predicts green space cover and vice-versa [57,70,71]. As such, bivariate correlation coefficients were not included in this summary.
Description of study sample, outcome, and green space measure stratified by research design.
| Citation | Sample Size | Geographic Context | Grade Level and Age | Green Space Measure(s) | Academic Outcome |
|---|---|---|---|---|---|
| Observational ( | |||||
| Beere & Kingham (2017) [ | 838 public schools | Cities in New Zealand | 1–6th (6–12 years old 1) | Tree cover from a local land cover database (resolution not reported) from one year on school parcel and in attendance zone | Math, reading, and writing standardized test scores |
| Browning et al., 2018 [ | 404 public schools | Chicago, Illinois, United States | 3rd (8 or 9 years old) | NDVI-derived greenness from MODIS (250 m resolution) over six years in spring, summer and fall (March, July, October) at 250, 500, 1000, and 3000 m radial buffers | Math and reading standardized test scores |
| Hodson & Sander (2017) [ | 222 public schools | Twin Cities, Minnesota, United States | 3rd (8 or 9 years old) | Grass, shrub, and tree cover from NLCD (30 m resolution) in one year on school parcel and in attendance zone | Math and reading standardized test scores |
| Kuo et al., 2018 [ | 318 public schools | Chicago, Illinois, United States | 3rd (8 or 9 years old) | Grass/shrub and tree cover from UTC (0.6 m resolution) in one year on school parcel and in attendance zone | Math and reading standardized test scores |
| Kweon et al., 2017 [ | 219 public schools | Washington, D.C., United States | 2–10th (7 to 16 years old) | Grass/shrub and tree cover from UTC (0.6 m resolution) in one year on school parcel | Math and reading standardized test scores |
| Leung et al., 2019 [ | 3054 public schools | Massachusetts, United States | 3–10th (ages 8–16) | NDVI-derived greenness from MODIS (250 m resolution) over eight years in spring and fall at 250, 500, 1000, and 2000 m radial buffers; Green land cover from a local database (0.5 m resolution) in one year at 250, 500, 1000, 2000 m radial buffers | Math and reading standardized test scores |
| Li et al., 2019 [ | 624 public schools | Illinois, United States | 9–12th (ages 14–18) | Tree canopy cover from NLCD (30 m resolution) in one year at 400, 800, 1600, 3200, and 4800 m radial buffers | American College Test (ACT), a standardized test administered at the end of high school to evaluate preparation for college, which includes math, reading, and science; End-of-semester grades as determined by percent of students on-track for college with no more than one “F” letter grade after at least ten semesters of high school |
| Markevych et al., 2018 [ | 2429 students | Munich and Wesel areas, Germany | NR (age 10 and age 15) | NDVI-derived greenness from MODIS (250 m resolution) over eight years in summer months (May to August) at 500 and 1000 m radial buffers; Tree cover from Copernicus (20 m resolution) [ | Math and reading standardized test scores |
| Matsuoka, 2010 [ | 101 public schools | Southeast Michigan, United States | 9–12th (ages 14–18) | Green view from cafeteria window; Grass cover on school parcel from aerial imagery | Michigan college preparatory exam for high school students; End-of-semester grades as determined by graduation rates, which require minimum letter grade average [ |
| Sivajarah et al., 2018 [ | 387 public schools | Toronto, Ontario, Canada | 3th and 6th (ages 8–9 and 11–12) | Tree canopy cover from UTC (0.6 m resolution) in one year on the school parcel; Number tree species and biodiversity from tree inventory | Math, reading, and writing standardized test scores |
| Tallis et al., 2018 [ | 495 public schools | California, United States | 5th (ages 10–11) | NDVI-derived greenness and agricultural cover from NAIP (1 m resolution) in one year in summer at 50, 100, 300, 500, 750, and 1000 m radial buffers | Composite index of math, reading, and writing standardized test scores |
| Wu et al., 2014 [ | 6333 public schools | Massachusetts, United States | 3rd (8 or 9 years old) | NDVI-derived greenness from MODIS (250 m resolution) over six years in spring, summer and fall (March, July, October) at 250, 500, 1000, and 3000 m radial buffers | Math and reading standardized test scores |
|
| |||||
| Benfield et al. (2015) [ | 567 students | University in Pennsylvania, United States | College (age M = 18.9, SD = 1.57) | Green view vs. fogged view (no view but daylight present) from classroom windows | End-of-semester grades |
1 ages reported are those typically associated with these grade levels in the United States [62], NR = not reported, MODIS = [63], NAIP = U.S. Department of Agriculture National Agriculture Imagery Program [64], UTC = Urban Tree Canopy Assessment [65,66], NLCD = National Land Cover Database [67], Note: For more details on green space measures, see [68,69].
Figure A1Points assigned to bias categories for articles in the review.
Outcomes and green space measures identified in the selected articles.
| First Author | Academic Outcome | Green Space Measure | Distance | Association between Green Space and Outcome |
|---|---|---|---|---|
| Beere | Math | Green land | Schoolyard | Negative |
| Beere | Math | Green land | 3000 m | Negative |
| Beere | Reading | Green land | Schoolyard | Negative |
| Beere | Reading | Green land | 3000 m | Negative |
| Beere | Writing | Green land | Schoolyard | Negative |
| Beere | Writing | Green land | 3000 m | Negative |
| Browning | Math | Greenness | 500 m | Negative |
| Browning | Math | Greenness | 3000 m | Negative |
| Browning | Math | Greenness | 250 m | Negative |
| Browning | Math | Greenness | 1000 m | Negative |
| Hodson | Math | Grass | 3000 m | Null |
| Hodson | Math | Shrub | 3000 m | Null |
| Hodson | Math | Tree | 3000 m | Null |
| Hodson | Reading | Grass | 3000 m | Null |
| Hodson | Reading | Shrub | 3000 m | Null |
| Hodson | Reading | Tree | 3000 m | Positive |
| Kuo | Math | Tree | Schoolyard | Positive |
| Kuo | Math | Tree | 3000 m | Null |
| Kuo | Reading | Tree | Schoolyard | Null |
| Kuo | Reading | Tree | 3000 m | Null |
| Kweon | Math | Grass | Schoolyard | Null |
| Kweon | Math | Tree | Schoolyard | Positive |
| Kweon | Reading | Grass | Schoolyard | Null |
| Kweon | Reading | Tree | Schoolyard | Positive |
| Leung | Math | Green land | 500 m | Positive |
| Leung | Math | Green land | 250 m | Positive |
| Leung | Math | Green land | 2000 m | Positive |
| Leung | Math | Green land | 1000 m | Positive |
| Leung | Math | Greenness | 500 m | Positive |
| Leung | Math | Greenness | 250 m | Positive |
| Leung | Math | Greenness | 2000 m | Positive |
| Leung | Math | Greenness | 1000 m | Positive |
| Leung | Reading | Green land | 500 m | Positive |
| Leung | Reading | Green land | 250 m | Null |
| Leung | Reading | Green land | 2000 m | Positive |
| Leung | Reading | Green land | 1000 m | Positive |
| Leung | Reading | Greenness | 500 m | Positive |
| Leung | Reading | Greenness | 250 m | Positive |
| Leung | Reading | Greenness | 2000 m | Positive |
| Leung | Reading | Greenness | 1000 m | Positive |
| Li | College | Tree | 500 m | Positive |
| Li | College | Tree | 3000 m | Positive |
| Li | College | Tree | 3000 m | Positive |
| Li | College | Tree | 250 m | Positive |
| Li | College | Tree | 2000 m | Positive |
| Li | College | Tree | 2000 m | Positive |
| Li | Grades | Tree | 500 m | Null |
| Li | Grades | Tree | 3000 m | Null |
| Li | Grades | Tree | 3000 m | Positive |
| Li | Grades | Tree | 250 m | Positive |
| Li | Grades | Tree | 2000 m | Null |
| Li | Grades | Tree | 2000 m | Null |
| Markevych | Math | Agriculture | 500 m | Null |
| Markevych | Math | Agriculture | 1000 m | Null |
| Markevych | Math | Green land | 500 m | Null |
| Markevych | Math | Green land | 1000 m | Null |
| Markevych | Math | Greenness | 500 m | Null |
| Markevych | Math | Greenness | 500 m | Null |
| Markevych | Math | Greenness | 1000 m | Null |
| Markevych | Math | Greenness | 1000 m | Null |
| Markevych | Math | Tree | 500 m | Null |
| Markevych | Math | Tree | 500 m | Null |
| Markevych | Math | Tree | 500 m | Null |
| Markevych | Math | Tree | 1000 m | Null |
| Markevych | Math | Tree | 1000 m | Null |
| Markevych | Math | Tree | 1000 m | Null |
| Markevych | Reading | Agriculture | 500 m | Null |
| Markevych | Reading | Agriculture | 1000 m | Null |
| Markevych | Reading | Green land | 500 m | Null |
| Markevych | Reading | Green land | 1000 m | Null |
| Markevych | Reading | Greenness | 500 m | Null |
| Markevych | Reading | Greenness | 500 m | Null |
| Markevych | Reading | Greenness | 1000 m | Null |
| Markevych | Reading | Greenness | 1000 m | Null |
| Markevych | Reading | Tree | 500 m | Null |
| Markevych | Reading | Tree | 500 m | Null |
| Markevych | Reading | Tree | 500 m | Null |
| Markevych | Reading | Tree | 1000 m | Null |
| Markevych | Reading | Tree | 1000 m | Null |
| Markevych | Reading | Tree | 1000 m | Null |
| Matsuoka | College | Grass | Schoolyard | Null |
| Matsuoka | College | Green land | View | Positive |
| Matsuoka | Grades | Grass | Schoolyard | Null |
| Matsuoka | Grades | Green land | View | Positive |
| Sivarajah | Math | Tree | Schoolyard | Null |
| Sivarajah | Reading | Tree | Schoolyard | Null |
| Sivarajah | Writing | Tree | Schoolyard | Positive |
| Tallis | Math | Agriculture | Schoolyard | Null |
| Tallis | Math | Agriculture | 500 m | Null |
| Tallis | Math | Agriculture | 1000 m | Null |
| Tallis | Math | Greenness | Schoolyard | Null |
| Tallis | Math | Greenness | 500 m | Null |
| Tallis | Math | Greenness | 1000 m | Null |
| Tallis | Math | Tree | Schoolyard | Null |
| Tallis | Math | Tree | 500 m | Null |
| Tallis | Math | Tree | 1000 m | Null |
| Tallis | Reading | Agriculture | Schoolyard | Null |
| Tallis | Reading | Agriculture | 500 m | Null |
| Tallis | Reading | Agriculture | 1000 m | Null |
| Tallis | Reading | Greenness | Schoolyard | Null |
| Tallis | Reading | Greenness | 500 m | Null |
| Tallis | Reading | Greenness | 1000 m | Null |
| Tallis | Reading | Tree | Schoolyard | Null |
| Tallis | Reading | Tree | 500 m | Null |
| Tallis | Reading | Tree | 1000 m | Null |
| Tallis | Writing | Agriculture | Schoolyard | Null |
| Tallis | Writing | Agriculture | 500 m | Null |
| Tallis | Writing | Agriculture | 1000 m | Null |
| Tallis | Writing | Greenness | Schoolyard | Null |
| Tallis | Writing | Greenness | 500 m | Null |
| Tallis | Writing | Greenness | 1000 m | Null |
| Tallis | Writing | Tree | Schoolyard | Null |
| Tallis | Writing | Tree | 500 m | Null |
| Tallis | Writing | Tree | 1000 m | Null |
| Wu | Math | Greenness | 500 m | Positive |
| Wu | Math | Greenness | 250 m | Positive |
| Wu | Math | Greenness | 2000 m | Null |
| Wu | Math | Greenness | 1000 m | Positive |
| Wu | Reading | Greenness | 500 m | Null |
| Wu | Reading | Greenness | 250 m | Positive |
| Wu | Reading | Greenness | 2000 m | Null |
| Wu | Reading | Greenness | 1000 m | Null |
| Wu | Reading | Greenness | 1000 m | Null |
Figure 3Conceptual framework adapted from the model proposed by Markevych and colleagues linking green space near schools to academic performance [51].