Note: SES refers to socioeconomic status. The gaps are standard deviation scores for high-SES children relative to low-SES children after adjusting for all family and child characteristics, pre-K schooling, and enrichment activities with parents, and parental expectations for children’s educational attainment. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Tables 3 and 4, Model 4.
Gap between top and bottom quintiles in 1998 | Change in gap from 1998 to 2010 | |
---|---|---|
Reading | 1.29 | |
Math | 1.46 | -0.15 |
Self-control (by teachers) | 0.32 | -0.10 |
Approaches to learning (by teachers) | 0.64 | -0.24 |
Self-control (by parents) | 0.47 | -0.14 |
Approaches to learning (by parents) | 0.66 |
Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have mothers in the top quintile of the education distribution and low-SES children have mothers in bottom quintile of the education distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 7, Model 1.
Gap between top and bottom quintiles in 1998 | Change in gap from 1998 to 2010 | |
---|---|---|
Reading | 1.09 | -0.13 |
Math | 1.31 | -0.23 |
Self-control (by teachers) | 0.42 | |
Approaches to learning (by teachers) | 0.60 | -0.13 |
Self-control (by parents) | 0.44 | |
Approaches to learning (by parents) | 0.44 |
Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children are in households with incomes in the top quintile of the income distribution and low-SES children are in households with incomes in bottom quintile of the income distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 8, Model 1.
Gap between top and bottom quintiles in 1998 | Change in gap from 1998 to 2010 | |
---|---|---|
Reading | 0.74 | 0.08 |
Math | 0.97 | |
Self-control (by teachers) | 0.32 | |
Approaches to learning (by teachers) | 0.46 | |
Self-control (by parents) | 0.28 | |
Approaches to learning (by parents) | 0.58 | 0.09 |
Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have a number of books in the home in the top quintile of the books-in-the-home distribution and low-SES children have a number of books in the home in the bottom quintile of the books-in-the-home distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 9, Model 1.
Reading | Mathematics | Self-control (by teachers) | Approaches to learning (by teachers) | Self-control (by parents) | Approaches to learning (by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | 1 (unadjusted) | 2 (clustered) | |
Gap in 2010–2011 | 1.169*** | 0.944*** | 1.250*** | 0.911*** | 0.386*** | 0.363*** | 0.513*** | 0.562*** | 0.391*** | 0.326*** | 0.563*** | 0.460*** |
(0.024) | (0.036) | (0.024) | (0.034) | (0.029) | (0.041) | (0.027) | (0.041) | (0.028) | (0.041) | (0.028) | (0.044) | |
Controls | ||||||||||||
Demographics | No | No | No | No | No | No | No | No | No | No | No | No |
Education and engagement | No | No | No | No | No | No | No | No | No | No | No | No |
Parental expectations | No | No | No | No | No | No | No | No | No | No | No | No |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 14,090 | 14,090 | 14,040 | 14,040 | 12,180 | 12,180 | 13,280 | 13,280 | 12,890 | 12,890 | 12,900 | 12,900 |
Adjusted R2 | 0.165 | 0.281 | 0.190 | 0.276 | 0.021 | 0.114 | 0.034 | 0.105 | 0.018 | 0.028 | 0.037 | 0.118 |
Note: Using the full sample. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10. Sizes may differ from those inferred from Tables 3–6, and from those in García 2015, due to differences in the sample sizes or to rounding.
Source: EPI analysis of ECLS-K, kindergarten class of 2010–2011 (National Center for Education Statistics)
1998–1999 | Low-SES (quintile 1) | Low-middle SES (quintile 2) | Middle SES (quintile 3) | High-middle SES (quintile 4) | High-SES (quintile 5) | All quintiles | |
---|---|---|---|---|---|---|---|
Child and family characteristics and main developmental activities | |||||||
Race/ethnicity | White | 26.40% | 53.70% | 61.20% | 68.10% | 78.80% | 57.70% |
Black | 26.20% | 17.80% | 15.50% | 12.00% | 6.40% | 15.60% | |
Hispanic | 39.80% | 21.20% | 15.80% | 12.70% | 6.80% | 19.20% | |
Hispanic English language learner (ELL) | 28.40% | 9.50% | 4.80% | 3.10% | 1.40% | 9.40% | |
Hispanic English speaker | 11.50% | 11.70% | 10.90% | 9.60% | 5.40% | 9.80% | |
Asian | 2.30% | 1.70% | 2.30% | 2.70% | 4.70% | 2.70% | |
Other | 5.30% | 5.60% | 5.30% | 4.40% | 3.40% | 4.80% | |
Poverty status | Lives in poverty | 71.30% | 22.30% | 10.60% | 4.20% | 1.10% | 21.80% |
Language | Child’s language at home is not English | 31.20% | 12.00% | 7.00% | 6.10% | 5.30% | 12.30% |
Family composition | Not living with two parents | 45.60% | 30.50% | 23.80% | 15.80% | 11.10% | 25.10% |
Number of family members | 4.84 | 4.55 | 4.42 | 4.36 | 4.40 | 4.51 | |
First- or second-generation immigrant | 30.30% | 15.10% | 12.80% | 13.10% | 15.40% | 17.30% | |
Pre-K care arrangements | Pre-K care | 64.20% | 70.90% | 76.50% | 81.00% | 87.80% | 76.20% |
Pre-K care, center-based | 43.70% | 45.00% | 50.20% | 55.40% | 65.80% | 52.20% | |
Parental care | 30.50% | 22.60% | 17.20% | 15.40% | 9.90% | 18.90% | |
Care by relative | 15.90% | 18.30% | 16.20% | 11.80% | 6.60% | 13.70% | |
Care by nonrelative | 5.30% | 8.20% | 10.90% | 11.60% | 13.70% | 10.00% | |
Care by multiple sources | 4.60% | 5.90% | 5.50% | 5.80% | 3.90% | 5.20% | |
Activities indices | Literacy/reading | -0.221 | -0.059 | -0.010 | 0.070 | 0.193 | -0.003 |
Other educational and engagement activities | -0.114 | -0.011 | 0.014 | 0.042 | 0.071 | 0.002 | |
Number of books | Average number | 32.4 | 58.1 | 74.3 | 87.9 | 107.3 | 72.5 |
Number of books, grouped by least to most | 0–25 | 61.70% | 31.60% | 20.20% | 11.30% | 5.00% | 25.50% |
26–50 | 23.10% | 34.80% | 30.80% | 30.60% | 21.40% | 28.20% | |
51–100 | 11.30% | 23.40% | 32.90% | 36.00% | 41.00% | 29.10% | |
101–199 | 1.80% | 4.00% | 5.70% | 6.60% | 9.50% | 5.60% | |
More than 200 | 2.10% | 6.20% | 10.30% | 15.50% | 23.00% | 11.50% | |
Parents’ expectations for their children’s educational attainment | |||||||
Highest education level expected | High school or less | 24.10% | 15.20% | 7.70% | 3.70% | 1.20% | 10.20% |
Two or more years of college, vocational school | 16.40% | 21.80% | 21.40% | 11.60% | 3.80% | 14.90% | |
Bachelor’s degree | 33.20% | 38.70% | 46.70% | 58.80% | 57.20% | 47.10% | |
Master’s degree | 9.20% | 9.40% | 10.30% | 13.60% | 22.80% | 13.10% | |
Ph.D. or M.D. | 17.10% | 15.00% | 13.90% | 12.30% | 15.00% | 14.60% | |
2010–2011 | Low-SES (quintile 1) | Low-middle SES (quintile 2) | Middle SES (quintile 3) | High-middle SES (quintile 4) | High-SES (quintile 5) | All quintiles | |
Child and family characteristics, and main developmental activities | |||||||
Race/ethnicity | White | 23.10% | 45.50% | 56.80% | 69.00% | 71.30% | 52.90% |
Black | 19.60% | 17.00% | 13.40% | 9.40% | 5.80% | 13.20% | |
Hispanic | 50.40% | 28.30% | 19.70% | 12.20% | 8.60% | 24.10% | |
Hispanic English language learner (ELL) | 36.10% | 11.90% | 5.20% | 2.10% | 0.90% | 11.40% | |
Hispanic English speaker | 14.30% | 16.30% | 14.40% | 10.10% | 7.70% | 12.60% | |
Asian | 2.50% | 2.80% | 3.20% | 4.40% | 8.70% | 4.20% | |
Others | 4.40% | 6.40% | 7.00% | 4.90% | 5.60% | 5.70% | |
Poverty status | Lives in poverty | 84.60% | 35.70% | 10.90% | 3.10% | 0.60% | 25.50% |
Language | Child’s language at home is not English | 40.30% | 15.60% | 8.00% | 5.00% | 7.00% | 15.30% |
Family composition | Not living with two parents | 54.90% | 41.70% | 34.10% | 19.30% | 9.60% | 31.80% |
Number of family members | 4.81 | 4.62 | 4.53 | 4.44 | 4.46 | 4.57 | |
First- or second-generation immigrant | 49.80% | 25.70% | 18.90% | 17.20% | 21.60% | 26.10% | |
Pre-K care arrangements | Pre-K care | 66.60% | 75.60% | 81.60% | 85.00% | 88.30% | 79.30% |
Pre-K care, center-based | 44.30% | 47.00% | 53.10% | 61.60% | 69.90% | 55.10% | |
Parental care | 34.90% | 25.40% | 19.10% | 15.40% | 12.00% | 21.40% | |
Care by relative | 16.00% | 19.70% | 17.40% | 12.70% | 8.60% | 14.90% | |
Care by nonrelative | 3.30% | 5.50% | 7.40% | 7.30% | 6.90% | 6.10% | |
Care by multiple sources | 1.50% | 2.40% | 3.10% | 2.90% | 2.70% | 2.50% | |
Activities indices | Literacy/reading | -0.231 | -0.038 | 0.033 | 0.094 | 0.171 | 0.008 |
Other educational and engagement activities | -0.049 | 0.022 | 0.029 | 0.026 | 0.001 | 0.006 | |
Number of books | Average number | 35.2 | 57.6 | 74.1 | 90.8 | 106.3 | 73.1 |
Number of books, grouped by least to most | 0–25 | 59.30% | 33.60% | 19.40% | 11.50% | 5.00% | 25.50% |
26–50 | 24.70% | 31.70% | 32.50% | 26.90% | 22.40% | 27.70% | |
51–100 | 11.20% | 24.80% | 32.30% | 39.00% | 41.70% | 30.00% | |
101–199 | 1.70% | 3.10% | 5.50% | 6.50% | 7.70% | 4.90% | |
More than 200 | 3.10% | 6.80% | 10.30% | 16.20% | 23.20% | 12.00% | |
Parents’ expectations for their children’s educational attainment | |||||||
Highest education level expected | High school or less | 11.40% | 6.20% | 5.00% | 2.40% | 1.00% | 5.20% |
Two or more years of college, vocational school | 16.70% | 25.00% | 17.20% | 9.80% | 3.20% | 14.40% | |
Bachelor’s degree | 34.80% | 39.10% | 47.00% | 57.10% | 53.10% | 46.30% | |
Master’s degree | 10.70% | 12.30% | 14.60% | 16.80% | 26.60% | 16.20% | |
Ph.D. or M.D. | 26.40% | 17.30% | 16.20% | 13.90% | 16.10% | 17.90% |
Note: SES refers to socioeconomic status.
Reading models | Mathematics models | |||||||
---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | 1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | |
Gap in 1998 | 1.071*** | 0.846*** | 0.641*** | 0.596*** | 1.258*** | 0.932*** | 0.668*** | 0.610*** |
(0.024) | (0.032) | (0.031) | (0.031) | (0.022) | (0.033) | (0.030) | (0.031) | |
Change in gap by 2010 | 0.098*** | 0.122*** | 0.096* | 0.080 | -0.008 | 0.025 | 0.053 | 0.051 |
(0.033) | (0.046) | (0.051) | (0.052) | (0.032) | (0.045) | (0.047) | (0.048) | |
Controls | ||||||||
Demographics | No | No | Yes | Yes | No | No | Yes | Yes |
Education and engagement | No | No | Yes | Yes | No | No | Yes | Yes |
Parental expectations | No | No | No | Yes | No | No | No | Yes |
School fixed effects | No | Yes | Yes | Yes | No | Yes | Yes | Yes |
Observations | 30,950 | 30,950 | 26,050 | 26,050 | 31,850 | 31,850 | 26,890 | 26,890 |
Adjusted R2 | 0.152 | 0.243 | 0.289 | 0.293 | 0.189 | 0.265 | 0.331 | 0.336 |
Notes: Models 1 and 2 use the full sample; Models 3 and 4 use the complete cases sample. Robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10. SES refers to socioeconomic status.
Self-control (reported by teachers) models | Approaches to learning (reported by teachers) models | |||||||
---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | 1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | |
Gap in 1998 | 0.394*** | 0.304*** | 0.217*** | 0.182*** | 0.630*** | 0.630*** | 0.493*** | 0.435*** |
(0.025) | (0.037) | (0.037) | (0.038) | (0.024) | (0.035) | (0.036) | (0.037) | |
Change in gap by 2010 | -0.009 | 0.065 | 0.078 | 0.085 | -0.117*** | -0.066 | -0.042 | -0.043 |
(0.037) | (0.054) | (0.060) | (0.061) | (0.035) | (0.053) | (0.057) | (0.057) | |
Controls | ||||||||
Demographics | No | No | Yes | Yes | No | No | Yes | Yes |
Education and engagement | No | No | Yes | Yes | No | No | Yes | Yes |
Parental expectations | No | No | No | Yes | No | No | No | Yes |
School fixed effects | No | Yes | Yes | Yes | No | Yes | Yes | Yes |
Observations | 29,500 | 29,500 | 25,080 | 25,080 | 31,260 | 31,260 | 26,460 | 26,460 |
Adjusted R2 | 0.019 | 0.117 | 0.173 | 0.175 | 0.040 | 0.117 | 0.199 | 0.204 |
Self-control (reported by parents) models | Approaches to learning (reported by parents) models | |||||||
---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | 1 (unadjusted) | 2 | 3 | 4 (fully adjusted) | |
Gap in 1998 | 0.467*** | 0.424*** | 0.357*** | 0.291*** | 0.539*** | 0.479*** | 0.215*** | 0.132*** |
(0.025) | (0.036) | (0.039) | (0.040) | (0.025) | (0.032) | (0.033) | (0.033) | |
Change in gap by 2010 | -0.076** | -0.084 | -0.032 | 0.001 | 0.024 | -0.024 | 0.096* | 0.112** |
(0.037) | (0.054) | (0.060) | (0.061) | (0.036) | (0.053) | (0.055) | (0.056) | |
Controls | ||||||||
Demographics | No | No | Yes | Yes | No | No | Yes | Yes |
Education and engagement | No | No | Yes | Yes | No | No | Yes | Yes |
Parental expectations | No | No | No | Yes | No | No | No | Yes |
School fixed effects | No | Yes | Yes | Yes | No | Yes | Yes | Yes |
Observations | 30,400 | 30,400 | 27,220 | 27,220 | 30,420 | 30,420 | 27,240 | 27,240 |
Adjusted R2 | 0.022 | 0.037 | 0.075 | 0.079 | 0.035 | 0.057 | 0.218 | 0.228 |
Year | Reduction | Change in reduction from 1998 to 2010 (in percentage points) | |
---|---|---|---|
Reading | 1998 | 45.5% | |
2010 | 42.9% | -2.6 | |
Math | 1998 | 52.6% | |
2010 | 48.6% | -4.1 | |
Self-control (reported by teachers) | 1998 | 50.8% | |
2010 | 32.6% | -18.1 | |
Approaches to learning (reported by teachers) | 1998 | 28.3% | |
2010 | 20.3% | -8 | |
Self-control (reported by parents) | 1998 | 35.3% | |
2010 | 34.3% | -1.1 | |
Approaches to learning (reported by parents) | 1998 | 73.5% | |
2010 | 56.0% | -17.5 |
Note: SES refers to socioeconomic status. Declining values from 1998 to 2010 indicate that factors such as early literacy activities and other controls were not as effective at shrinking SES-based gaps in 2010 as they were in 1998.
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |
---|---|---|---|---|---|---|
Correlations between selected practices and skills measured at kindergarten entry in 1998 | ||||||
Center-based pre-K | 0.106*** | 0.097*** | -0.125*** | -0.001 | -0.006 | 0.018 |
(0.016) | (0.015) | (0.018) | (0.018) | (0.019) | (0.016) | |
Number of books | 0.012*** | 0.016*** | 0.004** | 0.008*** | 0.002 | 0.006*** |
(0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | |
Reading/literacy | 0.166*** | 0.068*** | 0.010 | 0.030* | 0.143*** | 0.315*** |
(0.016) | (0.015) | (0.018) | (0.016) | (0.018) | (0.017) | |
Other activities | -0.115*** | -0.036*** | 0.047*** | 0.033** | 0.046*** | 0.292*** |
(0.015) | (0.014) | (0.017) | (0.016) | (0.017) | (0.016) | |
Correlations between parents’ expectations about their children’s highest level of educational attainment and skills measured at kindergarten entry in 1998 | ||||||
Two or more years of college/vocational school | 0.029 | 0.066** | 0.072* | 0.115*** | 0.180*** | 0.136*** |
(0.025) | (0.026) | (0.042) | (0.037) | (0.038) | (0.033) | |
Bachelor’s degree | 0.114*** | 0.172*** | 0.141*** | 0.211*** | 0.272*** | 0.228*** |
(0.023) | (0.023) | (0.036) | (0.032) | (0.036) | (0.030) | |
Master’s degree or more | 0.160*** | 0.220*** | 0.120*** | 0.219*** | 0.254*** | 0.377*** |
(0.026) | (0.025) | (0.039) | (0.034) | (0.036) | (0.033) | |
Changes from 1998 to 2010 in the correlations between selected practices and skills measured at kindergarten entry | ||||||
Center-based pre-K | -0.005 | -0.036 | 0.060* | -0.010 | -0.020 | 0.010 |
(0.025) | (0.025) | (0.032) | (0.031) | (0.031) | (0.026) | |
Number of books | 0.002 | -0.001 | 0.001 | 0.002 | -0.002 | 0.004 |
(0.003) | (0.002) | (0.003) | (0.003) | (0.003) | (0.002) | |
Reading/literacy | 0.018 | 0.008 | 0.015 | 0.014 | -0.079*** | -0.173*** |
(0.025) | (0.024) | (0.031) | (0.028) | (0.030) | (0.027) | |
Other activities | -0.008 | -0.016 | 0.031 | 0.020 | 0.218*** | 0.265*** |
(0.025) | (0.024) | (0.029) | (0.028) | (0.029) | (0.025) | |
Changes from 1998 to 2010 in the correlations between parents’ expectations about their children’s highest level of educational attainment and skills measured at kindergarten entry | ||||||
Two or more years of college/vocational school | 0.121** | 0.106* | 0.201** | 0.204*** | -0.030 | 0.151** |
(0.055) | (0.059) | (0.081) | (0.072) | (0.084) | (0.066) | |
Bachelor’s degree | 0.139*** | 0.103** | 0.136* | 0.174*** | -0.084 | 0.100 |
(0.048) | (0.051) | (0.070) | (0.063) | (0.078) | (0.061) | |
Master’s degree or more | 0.186*** | 0.117** | 0.140* | 0.189*** | -0.041 | 0.076 |
(0.052) | (0.054) | (0.074) | (0.066) | (0.081) | (0.063) | |
Observations | 26,050 | 26,890 | 25,080 | 26,460 | 27,220 | 27,240 |
Adj.R2 | 0.293 | 0.336 | 0.175 | 0.204 | 0.079 | 0.228 |
Notes: The robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10.
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | |
Gap in 1998 | 1.294*** | 0.696*** | 1.457*** | 0.681*** | 0.317*** | 0.076 | 0.638*** | 0.409*** | 0.471*** | 0.254*** | 0.655*** | 0.221*** |
(0.038) | (0.058) | (0.036) | (0.050) | (0.039) | (0.048) | (0.038) | (0.042) | (0.039) | (0.049) | (0.039) | (0.045) | |
Change in gap by 2010 | -0.020 | -0.075 | -0.154*** | -0.119* | -0.099* | 0.046 | -0.237*** | -0.141* | -0.136** | -0.093 | -0.084 | -0.004 |
(0.051) | (0.082) | (0.049) | (0.070) | (0.055) | (0.081) | (0.053) | (0.074) | (0.053) | (0.080) | (0.053) | (0.070) | |
Controls | ||||||||||||
Demographics | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Education and engagement | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Parental expectations | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 26,660 | 23,880 | 27,570 | 24,710 | 25,790 | 23,170 | 27,200 | 24,380 | 27,280 | 25,040 | 27,290 | 25,050 |
Adjusted R2 | 0.134 | 0.282 | 0.166 | 0.328 | 0.009 | 0.172 | 0.029 | 0.199 | 0.017 | 0.079 | 0.032 | 0.223 |
Notes: Model 1 uses the full sample; Model 4 uses the complete cases sample. Robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10.
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | |
Gap in 1998 | 1.090*** | 0.384*** | 1.308*** | 0.443*** | 0.419*** | 0.119** | 0.603*** | 0.325*** | 0.443*** | 0.272*** | 0.436*** | 0.073 |
(0.042) | (0.058) | (0.041) | (0.060) | (0.045) | (0.050) | (0.044) | (0.049) | (0.045) | (0.051) | (0.044) | (0.052) | |
Change in gap by 2010 | -0.127** | -0.006 | -0.230*** | -0.060 | 0.049 | 0.228*** | -0.128** | 0.008 | 0.044 | 0.106 | 0.032 | 0.051 |
(0.060) | (0.084) | (0.059) | (0.082) | (0.066) | (0.081) | (0.064) | (0.079) | (0.065) | (0.084) | (0.064) | (0.080) | |
Controls | ||||||||||||
Demographics | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Education and engagement | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Parental expectations | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 28,650 | 26,050 | 29,560 | 26,890 | 27,550 | 25,080 | 29,110 | 26,460 | 28,170 | 27,220 | 28,190 | 27,240 |
Adjusted R2 | 0.103 | 0.276 | 0.143 | 0.321 | 0.023 | 0.174 | 0.036 | 0.199 | 0.019 | 0.079 | 0.019 | 0.226 |
Reading | Math | Self-control (reported by teachers) | Approaches to learning (reported by teachers) | Self-control (reported by parents) | Approaches to learning (reported by parents) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | 1 (unadjusted) | 4 (fully adjusted) | |
Gap in 1998 | 0.736*** | 0.347*** | 0.966*** | 0.424*** | 0.324*** | 0.105*** | 0.455*** | 0.241*** | 0.283*** | 0.117*** | 0.583*** | 0.136*** |
(0.028) | (0.034) | (0.027) | (0.031) | (0.029) | (0.035) | (0.028) | (0.033) | (0.029) | (0.037) | (0.028) | (0.033) | |
Change in gap by 2010 | 0.083** | -0.540*** | -0.019 | -0.818*** | -0.068 | -0.126 | -0.058 | -0.244 | -0.044 | -0.248 | 0.085** | -0.026 |
(0.039) | (0.184) | (0.038) | (0.188) | (0.042) | (0.225) | (0.041) | (0.184) | (0.041) | (0.216) | (0.039) | (0.178) | |
Controls | ||||||||||||
Demographics | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Education and engagement | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Parental expectations | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
School fixed effects | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes |
Observations | 29,060 | 26,050 | 29,920 | 26,890 | 27,730 | 25,080 | 29,350 | 26,460 | 30,200 | 27,220 | 30,220 | 27,240 |
Adjusted R2 | 0.080 | 0.270 | 0.120 | 0.314 | 0.012 | 0.172 | 0.024 | 0.194 | 0.009 | 0.075 | 0.047 | 0.226 |
Part of school district | Entire school district | Across multiple school districts |
---|---|---|
Austin, Texas | Joplin, Missouri | Eastern Kentucky* |
Boston, Massachusetts | Kalamazoo, Michigan | |
Durham, North Carolina (East Durham) | Montgomery County, Maryland* | |
Minneapolis, Minnesota (North Minneapolis) | Pea Ridge, Arkansas | |
New York, New York | Vancouver, Washington** | |
Orange County, Florida (Tangelo Park) |
*Indicates that while the initiative covers the entire county or region, a portion of the county or region receives more intensive services. **Indicates that the initiative will cover the entire school district under plans to expand.
Source: Case studies published on the Broader, Bolder Approach to Education website (www.boldapproach.org/case-studies)
1. Values are in 2008 dollars.
2. Early investments in education strongly predict adolescent and adult development (Cunha and Heckman 2007; Heckman 2008; Heckman and Kautz 2012). For instance, students with higher levels of behavioral skills learn more in school than peers whose attitudinal skills are less developed (Jennings and DiPrete 2010). In general, as Heckman asserted, “skills beget skills,” meaning that creating basic, foundational knowledge makes it easier to acquire skills in the future (Heckman 2008). Conversely, children who fail to acquire this early foundational knowledge may experience some permanent loss of opportunities to achieve to their full potential. Indeed, scholars have documented a correlation between lack of kindergarten readiness and not reading well at third grade, which is a key point at which failing to read well greatly reduces a child’s odds of completing high school (Fiester 2010; Hernandez 2011).
3. Research by Reardon (2011) had found systematic increases in income gaps among generations. Recent studies by Bassok and Latham (2016) and Reardon and Portilla (2016), however, show narrower achievement gaps at kindergarten entry between a recent cohort and the previous one, and thus a possible discontinuation or interruption of that trend. (Bassok et al. [2016] use an SES construct to compare relative teacher assessments of cognitive and behavioral skills among low-SES children versus all children, adjusted by various other characteristics; Reardon and Portilla [2016] look at relative performance of children in the 90th and 10th income percentiles, and use age-adjusted, standardized, outcome scores.) Research by Carnoy and García (2017) shows persistent social-class gaps, but no solid evidence regarding trends: their findings for students in the fourth and eighth grades, in math and reading, show that achievement gaps neither shrink nor grow consistently (they are a function of the social-class indicator, the grade level, or the subject).
4. Clustering takes into account the fact that children are not randomly distributed, but tend to be concentrated in schools or classrooms with children of the same race, social class, etc. These estimates offer an estimate of gaps within schools. See Appendix B for more details.
5. Results available upon request. See García 2015 for results for all SES-quintiles (the baseline or unadjusted gaps in that report correspond with Model 2 in this paper).
6. The Early Childhood Longitudinal Study asks both parents and teachers to rate children’s abilities across a range of these skills. The specific skills measured may vary between the home and classroom setting. Teachers likely evaluate their students’ skills levels relative to those of other children they teach. Parents, on the other hand, may be basing their expectations on family, community, culture, or other factors.
7. See García 2015 for a discussion of which factors in children’s early lives and their individual and family characteristics (in addition to social class) drive the gaps among children of the 2010 kindergarten class.
8. Note that the SES quintiles are constructed using each year’s distribution, and that changes in the overall and relative distribution may affect the characteristics of children in the different quintiles each year (i.e., there may be some groups who are relatively overrepresented in one or another quintile if changes in the SES components changed over time).
9. The detailed frequency with which parents develop or practice some activities with their children at home and others is available upon request.
10. Literature on expectations and on parental behaviors in the home find that they positively correlate with children’s cognitive development and outcomes (Simpkins, Davis-Kean, and Eccles 2005; Wentzel, Russell, and Baker 2016). This literature acknowledges the multiple pathways through which expectations and behaviors influence educational outcomes, as well as the importance of race, social class, and other factors as moderators of such associations (Davis-Kean 2005; Redd et al. 2004; Wentzel, Russell, and Baker 2016; Yamamoto and Holloway 2010).
11. This may be affected by the fact that the highest number of reported books in 1998 was “more than 200,” while in 2010 parents could choose from more categories, up to “more than 1,000.” We had to use 200 as our cap in order to compare data for the two kindergarten classes.
12. Evidence also points to many other factors that affect children’s school readiness, and these, too, likely changed over this time period. For example, access to prenatal care, health screenings, and nutritional programs could all have affected children’s development differently across these two cohorts, but we do not have access to these data and thus cannot control for them in our study. For links between school readiness, children’s health, and poverty, see AAP COCP 2016; Currie 2009; U.S. HHS and U.S. ED 2016.
13. Models include all quintiles in their specification. Tables that offer a comparison for all quintiles relative to the first quintile are available upon request. We focus the discussion on the gap between the top and bottom.
14. As a result, sample sizes become smaller (see Appendix Table C1). Assuming “missingness” (observations without full information) is completely at random, the findings are representative of the original sample and of the populations they represent. Analytic samples once missingness is accounted for are called the complete case samples. We tested to see whether the unadjusted gaps estimated above with the full sample remained the same when using the complete case samples. For Model 1, we found an average difference of 0.01 sd in the estimates of 1998 SES gaps, and an average difference of 0.02 sd in the estimates of the change in the gaps. For Model 2, the differences were 0.01 sd for the gaps’ estimates and 0.04 for changes in the gaps’ estimates. In terms of statistical significance, there are no significant changes in the estimates associated with the 1998 gaps, but there are two changes in the statistical significance of the estimates associated with the changes in the gaps by 2010 – 2011, and one change in the magnitude of the coefficient. The first change in the statistical significance of the estimates associated with the changes in the gaps by 2010 – 2011 is the change in the gap in approaches to learning as reported by parents, which is statistically significant when using the restricted sample (0.07 sd, at the 10 percent significance level, Model 1); and the second is the change in the gap in math which also becomes statistically significant when using the restricted sample (0.09, at the 10 percent significance level, Model 2). Finally, the one change in the magnitude of the coefficient, in this model, is the estimate of the change in the gap in reading, which increases when using the restricted sample (from 0.12 sd to 0.18 sd). Results are available upon request.
15. These interactions between inputs and time test for whether the influence of inputs in 2010 is smaller than, the same as, or larger than the influence of inputs in 1998. Also, although only the fully specified results are shown, as noted in Appendix B, these sets of controls are entered parsimoniously in order to determine how sensitive gaps and changes in gaps over time are to the inclusion of family characteristics only, to the added inclusion of family investments, and, finally, to the inclusion of parental expectations (for the inclusion of parental expectations, we incorporated interactions of the covariates with time parsimoniously as well). For all outcomes, and focusing on the models without interactions between covariates and time, we find that all gaps in 1998 continuously shrink as we add more controls. For example, in reading, adding family characteristics reduces the gap in 1998 by 11 percent, adding investments further reduces it by 15 percent, and adding expectations further reduces it by 9 percent. In math, these changes equal to 16 percent, 13 percent, and 10 percent. For changes in the gap by 2010–2011, for both reading and math, adding family characteristics and investments shrink the changes in the gaps, but adding expectations slightly increases the estimated coefficients (which are statistically significant for reading, but not for math in these models. For self-control (as reported by teachers) and approaches to learning (by parents), which are the only two noncognitive skills for which the change in the gap is statistically significant, adding family characteristics reduces the change in the “gap [by 2010–2011” coefficient], but adding investments increases it, and adding expectations further increases the changes in the gaps by 2010–2011. These results are not shown in the appendices, but are available upon request.
16. The interactions between parental expectations of children’s educational attainment and the time variable test for whether the influence of expectations in 2010 is smaller, the same, or larger, than the influence of expectations in 1998.
17. The change in the skills gaps by SES in 2010 due to the inclusion of the controls is not directly visible in the tables in this report. To see this, see the comparison of estimates of models MS1–MS3 in García 2015. The change in the skills gaps by SES in 1998 is directly observable in Tables 3 and 4 and is discussed below.
18. The numbers in the “Reduction” column in Table 5 (showing the shares of the SES-based skills gaps that are accounted for by controls) are always higher for 1998 than for 2010.
19. Please note that until this point in the report we have been concerned with SES gaps and not with performance directly (though SES gaps are the result of the influence of SES on performance, which leads to differential performance of children by SES and hence to a performance gap). The paragraphs above emphasize how controls mediate or explain some of the skills gaps by SES, so, in a way, controls inform our analysis of gaps because they reveal how changes in gaps may have been affected by changes in various factors’ capacity to influence performance. Now the focus is on exploring the independent effect of the covariates of interest on performance. In this report, because we address whether the education and selected practices affect outcomes, the main effect is measured for the 1998 cohort, and we measure how it changed between 1998 and 2010. The detailed discussion for the correlation between covariates and outcomes in 2010 is provided in Table 3 in García 2015.
20. This variable indicates whether the child was cared for in a center-based setting during the year prior to the kindergarten year, compared with other options (as explained in García 2015, these alternatives include no nonparental care arrangements; being looked after by a relative, a nonrelative, at home or outside; or a combination of options. Any finding associated with this variable may be interpreted as the association between attending prekindergarten programs, compared with other options, but must be interpreted with caution. In other words, the child may have attended a high-quality prekindergarten program, which could have been either private or public, or a low-quality one, which would have different impacts. He or she might have been placed in (noneducational) child care, either private or public, of high or low quality, for few or many hours per day, with very different implications for his or her development (Barnett 2008; Barnett 2011; Magnuson et al. 2004; Magnuson, Ruhm, and Waldfogel 2007; Nores and Barnett 2010). For the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. 2010, and for a meta-analysis of results, see Duncan and Magnuson 2013. Thus, more detailed information on the characteristics of the nonparental care arrangements (type, quality, and quantity) would help researchers further disentangle the importance of this variable. This additional information would provide a much clearer picture of the effects of early childhood education on the different educational outcomes.
21. Because these associations seemed counterintuitive, we tested whether they were sensitive to the composition of the index. We removed one component of the index at a time and created five alternative measures of other enrichment activities that parents do with their children. The results indicate that the negative association between the index and reading is not sensitive to the components of the index (the coefficients for the main effect, i.e., for the effect in 1998 range between -0.14 and -0.09, are all statistically significant). For math, the associations lose some precision, but retain the negative sign (negative association) in four out of the five cases (minimum coefficient is -0.06). As a caveat, these components do not reflect whether the activities are undertaken by the child or guided by the adult, the time devoted to them, or how much they involve the use of vocabulary or math concepts. The associations could indicate that time spent on nonacademic activities detracts from parents’ time to spend on activities that are intended to boost their reading and math skills, among other possible explanations. These results are available upon request.
22. Note that in this section, “social class” and “socioeconomic status” (SES) are treated as equivalent terms; in the rest of the report, we refer to SES as a construct that is one measure of social class. See Appendices C and D for discussions of two other sensitivity analyses, one based on imputation of missing values for the main analysis in this paper, and the other on the utilization of various metrics of the cognitive variables. Overall, our findings were not sensitive to various multiple imputation tests. In terms of the utilization of different metrics for the cognitive variables, some sensitivity of the point estimates was detected.
23. With certain activities that are already so provided to high-SES children, there may be little room for doing more for them. For example, there are only 24 hours per day to read to your child, so there is a cap on reading from a cap on time. But perhaps there is still room to improve the influence of reading, if, for example, the way reading is done changes.
24. Eight of the 12 districts explored in this paper are the subjects of published case studies. Case studies for the other four are in progress and will be published later this year. When citing information from the published case studies, we cite the specific published study. For the four that are not yet published, we refer to the original sources being used to develop the case studies.
25. Missing or incomplete cells in the table indicate that data were not available on that aspect of student demographics or other characteristics. As per the source note, most data came either from the districts’ websites or from NCES.
26. In the country as a whole, poverty rates, which had been rising prior to 2007, sped up rapidly during the recession and in its aftermath (through 2011–2012), and minority students (mainly Hispanic and Asian) grew as a share of the U.S. public school student body. Between 2000 and 2013, even with a decline in the proportion of black students, the share of the student body that is minority (of black or Hispanic origin) increased from 30.0 percent to 40.5 percent, and the proportion of low-income students (those eligible for free or reduced-price lunch) also increased, up from 38.3 percent of all public school students in 2000 to 52.0 percent in 2013 (Carnoy and García 2017). The Southern Education Foundation revealed a troubling tipping point in 2013: for the first time since such data have been collected, over half of all public school students (51 percent) qualified for free or reduced-priced meals (i.e., over half of students were living in households at or below 185 percent of the federal poverty line). Across the South, shares were much higher, with the highest percentage, 71 percent—or nearly three in four students—in Mississippi (Southern Education Foundation 2015).
27. A full cross-cutting analysis of why and how these districts have employed whole-child/comprehensive educational approaches will be published as part of a book that draws on these case studies.
28. The federal Early Head Start (EHS) program includes both a home visiting and a center-based component, with many of the low-income infants and toddlers served benefiting from a combination of the two. Studies of EHS find improved cognitive, behavioral, and emotional skills for children as well as enhanced parenting behaviors.
29. According to one important source for data on access to and quality of state pre-K programs, the State of Preschool yearbook produced annually by the National Institute for Early Education Research (NIEER) at Rutgers University, as of 2015, 42 states and the District of Columbia were funding 57 programs. Moreover, programs continued to recover from cuts made during the Great Recession; enrollment, quality, and per-pupil spending were all up, on average, compared with the year before, albeit with the important caveat that two major states—Texas and Florida—lost ground, and that “[f]or the nation as a whole,…access to a high-quality preschool program remained highly unequal, and this situation is unlikely to change in the foreseeable future unless many more states follow the leaders” (NIEER 2016).
30. Elaine Weiss interview with Joshua Starr, June 2017.
31. Murnane and Levy 1996; Elaine Weiss interview with Joshua Starr, June 2017.
32. In recent years, a growing number of reports have emerged that some charter schools—which are technically public schools and often tout their successes in serving disadvantaged students—keep out students unlikely to succeed through complex application processes, fees, parent participation contracts, and other mechanisms, and then further winnow the student body of such students by pushing them out when they struggle academically or behaviorally. For more on this topic, see Burris 2017, PBS NewsHour 2015, and Simon 2013.
33. See AIR 2011 and Sparks 2017. The federal school improvement models, in order of severity (from lightest to most stringent) are termed “transformation,” “turnaround,” “restart,” and “closure” (AIR 2011, 3).
34. While the cut score on any given assessment/test needed for a student to be considered “proficient” is an arbitrary one, and, in Minnesota and many other states, changes from year to year and from one assessment to another, these gains are a helpful indicator of program effectiveness, as they are comparable over the time period described.
35. Joplin statistics are from internal data produced for the superintendent at that time that are no longer available.
36. Attendance Works , a national campaign to reduce chronic absence, points to a range of studies that document and explain the connections between chronic absenteeism, student physical and mental health, and student achievement. Areas of research include elementary school absenteeism, middle and high school absenteeism, health issues, and state and local data on how these problems play out, among others.
37. Elaine Weiss interview with C.J. Huff, June 2016.
38. See Appendix D for a discussion of results using other metrics for reading and math achievement. Results are not meaningfully different across metrics, though the point estimates differ slightly.
39. This last feature will be explored in a companion paper to this one, as soon as the necessary information is released by NCES. (As Tourangeau et al. [2013] note, the assessment scores for the 2010–2011 cohort are not directly comparable with those for the 1998–1999 cohort. We are waiting on the availability of this data to conduct a companion study that allows us to learn whether starting levels of knowledge rose over these years, and what the relative gains were for different demographic groups.)
40. We acknowledge that there are multiple noneducation public policy and economic policy areas to be called upon to address the problems studied in this report, namely, all the ones that ensure other factors that correlate with low-SES are attended, and, obviously, the ones that lead to fewer low-SES children. These other policies could help ensure that more children grow up in contexts with sufficient resources and healthy surroundings, or would leave fewer children without built-in supports at home that need to be compensated for afterwards. We made these points in two early studies, and in the policy brief companion to this study (García 2015; García and Weiss 2015; García and Weiss 2017). A similar comprehensive approach in terms of policy recommendations was used by Putnam (2015).
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Introduction.
Our research benefits from the existence of two companion studies conducted by the National Center for Education Statistics (NCES), the Early Childhood Longitudinal Study of the Kindergarten Class of 1998–1999 and the Early Childhood Longitudinal Study of the Kindergarten Class of 2010–2011 (hereafter, ECLS-K 1998–1999 and ECLS-K 2010–2011). The data from these studies come with multiple advantages and a few disadvantages.
The studies follow two nationally representative samples of children starting in their kindergarten year and continuing through their elementary school years (eighth grade for 1998–1999 cohort and fifth grade for the 2010–2011 cohort). The tracking of students over time is one of the most valuable features of the data. The studies include assessments of the children’s cognitive performance and knowledge as well as skills that belong in the category of noncognitive, or social and emotional, skills. The studies also include information on teachers and schools (provided by teachers and administrators) and interviews with parents.
Another valuable feature of the data is the availability of two ECLS-K studies (ECLS-K 1998–1999 and ECLS-K 2010–2011), which allows for cross-comparisons “of two nationally representative kindergarten classes experiencing different policy, educational, and demographic environments” (Tourangeau et al. 2013). The two studies are 12 years apart, or a full school cycle apart: when the 2010–2011 kindergarten class was starting school, the 1998–1999 class was starting the grade leading to their graduation. A comparison of the studies thus offers insightful information about the consequences of changes in the system that may have occurred during an entire cohort’s school life. For the 2010 study, the sample included 18,174 children in 968 schools. i The 1998 study sample included 21,409 children in 903 schools. ii
This existence of data from two cohorts is also a limitation to the current study, as explained by Tourangeau et al. (2013), who note that the assessment scores for the 2010–2011 class are not directly comparable with those developed for the class of 1998–1999. Although the IRT (Item Response Theory) procedures used in the analysis of data were similar across the two studies, each study incorporated different items, which means that the resulting scales are different. Tourangeau et al. (2013) state that “a subsequent release of the ECLS-K: 2010–2011 data will include IRT scores that are comparable with the ECLS-K 1998 cohort.” Up to the point of publication of the current study, this information had not yet been released, and we use standardized scores, instead of raw scores, for the outcomes examined. We can assess changes in the relative position in a distribution (i.e., how far apart high- and low-SES children are in 1998 and how far apart high- and low-SES children are in 2010), but not overall changes in their performance (i.e., it is not possible to ascertain whether performance has improved overall, or if gaps are smaller or larger due to an improvement in performance of children at the low end (specifically the lowest fifth) of the distribution or due to a decrease in the performance of children at the high end (highest fifth) of the distribution, etc.). A full comparison remains to be produced, upon data availability.
We use data for the first wave of each study, corresponding with fall kindergarten (or school entry).
For the analyses, we use the by-year standardized scores corresponding to the fall semester. (The 1998 IRT scale scores for reading and mathematics achievement and assessments of noncognitive skills are standardized using the 1998 distribution and its mean and sd; for 2010, we use the mean and sd of the 2010 distribution.)
Cognitive skills are assessed with instruments that measure each child’s:
We use the term “principal” to identify a set of noncognitive skills that are measured by both the ECLS-K 1998–1999 and 2010–2011 surveys, and that have been relatively extensively used in research.
Teachers are asked to assess each child’s:
Parents are asked to assess their child’s:
For the analyses, we use the following set of covariates. The definitions, and the coding used for the covariates, by year, are shown in Appendix Table A1 .
Gaps by socioeconomic status.
The expressions below show the specifications used to estimate the socioeconomic status–based (SES-based) performance gaps. For any achievement outcome A , we estimate four models:
These estimates build on all the available observations (i.e., only those children who have missing values in the outcome variables are eliminated from the analysis).
Because of lack of response in some of the covariates used as predictors of performance, we construct a common sample with observations with no missing information in any of the variables of interest (see information about missing data for each variable in Appendix Table C1 ). We estimate two more models: iii
The equation below shows the equation we estimate for Models 1 through 4.
Following standard approaches in this field, we use multiple imputation to impute missing values in both the independent and dependent variables, for the analysis of skills gaps and changes in them from 1998 to 2010 by socioeconomic status (main analysis). See share of missing data by variable in Appendix Table C1 . We use the mi commands in Stata 14, using chained equations, which jointly model all functional terms. The number of iterations was set up equal to 20. Imputation is performed by year.
Our functional form of the imputation model is specified using SES, gender, race, disability, age, type of family, number of books, educational activities, and parental expectations, as well as the original cognitive and noncognitive variables, as variables to be imputed. We use various specifications, combining different sets of auxiliary variables, mi impute methods, and other parameters, to capture any sensitivity of the results to the characteristics of the model. For example, income, family size, and ELL status are set as auxiliary variables and used in several of the imputation models. Another imputation option that was altered across models is the use of weights, as we ran out of imputation models using weights and not using them.
In the imputation model, in order to impute categorical variables’ missingness, we use the option augment, to prevent the large number of categorical variables to be imputed from causing problems of perfect prediction (StataCorp. 2015). The rest of the variables are first imputed as continuous variables. In a second exercise, we also impute SES and educational expectations as ordinal variables (also using the option augment).
In order to calculate the standardized dependent variables, we use the variables derived from the imputation variables (also known as passive imputation). This “fills in only the underlying imputation variables and computes the respective functional terms from the imputed variables” (StataCorp. 2015). In one case, we imputed the dependent variables directly as continuous variables (though we anticipated that the distribution of the scores imputed this way would not necessarily have a mean of 0 and a standard deviation of 1).
Using the imputed data, we estimate Models 1 through 4 following the specifications explained above (from no regressors to fully specified models).
The main findings of our analysis are not sensitive to missing data imputation. The estimates of the gaps in 1998 and the changes in the gaps from 1998 to 2010 are consistent across models in terms of statistical significance. There are some minor changes in the sizes of the estimated coefficients, especially those associated with the changes in the gaps (though all are statistically not different from 0, as discussed in the report using the results from the analysis with the complete cases). There are also some minor changes in the standard errors, though they are small enough to widen the coefficients’ statistical bandwidth to not include the 0.
Children’s reading and mathematics skills are measured using several different metrics in ECLS-K. Among these, the best-known or more commonly used metrics in research are the IRT-based theta scores and the IRT-based scale scores (IRT stands for Item Response Theory). NCES provides data users with definitions of these metrics and recommendations on how to appropriately choose among the different metrics. NCES explains that both theta and IRT-based scale scores are valid indicators of ability. This makes them suitable for research purposes, even though each is expressed in its own unit of measurement. NCES recommends that analysts “consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience” when choosing the appropriate score for analysis (see Tourangeau et al. 2013).
Although nothing would indicate that this could be the case, our work noted that results of analyses such as the one developed in this study are in some ways sensitive to the metrics used as dependent variables. v Thus, the purpose of this appendix is to illustrate the differences in the results associated with different analytic decisions in terms of the metrics used. As we will see, in essence, point estimates depend on the metric used, but the results do not change in a meaningful way and conclusions and implications remain unchanged. That is, although caution is required when interpreting the results obtained using different combinations of metrics, procedures (including standardization), and data waves, it is important to state that the main conclusions of this study— that social-class gaps in cognitive and noncognitive skills are large and have persisted over time — hold . So do the policy recommendations derived from those findings: sufficient, integrated, and sustained over-time efforts to tackle early gaps in a more effective manner.
NCES makes the following recommendations for researchers who are choosing among scales (see Tourangeau et al. 2013): vi
When choosing scores to use in analysis, researchers should consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience. […] The IRT-based scale scores […] are overall measures of achievement. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or in different rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics. […] Results expressed in terms of scale score points, scale score gains, or an average scale score may be more easily interpretable by a wider audience than results based on the theta scores. The IRT-based theta scores are overall measures of ability. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or across rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics. […] The theta scores may be more desirable than the scale scores for use in a multivariate analysis because generally their distribution tends to be more normal than the distribution of the scale scores. However, for a broader audience of readers unfamiliar with IRT modeling techniques, the metric of the theta scores (from -6 to 6) may be less readily interpretable. […]
The two scores are defined as follows (see Tourangeau et al. 2013, section “3.1 Direct Cognitive Assessment: Reading, Mathematics, Science”):
The IRT-based scale score is an estimate of the number of items a child would have answered correctly in each data collection round if he or she had been administered all of the questions for that domain that were included in the kindergarten and first-grade assessments. To calculate the IRT-based overall scale score for each domain, a child’s theta is used to predict a probability for each assessment item that the child would have gotten that item correct. Then, the probabilities for all the items fielded as part of the domain in every round are summed to create the overall scale score. Because the computed scale scores are sums of probabilities, the scores are not integers. The IRT-based theta score is an estimate of a child’s ability in a particular domain (e.g., reading, mathematics, science, or SERS) based on his or her performance on the items he or she was actually administered. […] The theta scores are reported on a metric ranging from -6 to 6, with lower scores indicating lower ability and higher scores indicating higher ability. Theta scores tend to be normally distributed because they represent a child’s latent ability and are not dependent on the difficulty of the items included within a specific test.
Reardon (2007) describes the calculation of the theta scores in the following manner: vii
For each test [math and reading], a three-parameter IRT model was used to estimate each student’s latent ability…at each wave…. The IRT model assumes that each student’s probability of answering a given test item correctly is a function of the student’s ability and the characteristics [discrimination, difficulty, and guessability] of the item…. Given the pattern of students’ responses to the items on the test that they are given, the IRT model provides estimates of both the person-specific latent abilities at each wave… and the item parameters. (Reardon 2007, 10) viii
He also notes that “[b]ecause the ECLS-K tests contain many more ‘difficult’ items than ‘easy’ items, the relationship between theta and scale scores is not linear (a unit difference in theta corresponds to a larger difference in scale scores at theta=1 than at theta=-1, for example). The scale scores are difficult to interpret as an interval-scale metric (or are an interval-scaled metric only with respect to the specific set of items on the ECLS-K tests),” while he shows that the “theta scores are interval-scale metrics, in a behaviorally-meaningful sense” (Reardon 2007, 11, 13). ix
For the analyses, both the scale and the theta scores need to be standardized by year (the original variables are not directly comparable because they rely on different instruments, as explained by NCES, and the resulting standardized variables have mean 0 and standard deviation 1). This is a common practice in the education field, as it allows researchers to use data that come from different studies and would not have a common scale otherwise. We need to take into consideration that the underlying units of measurement for each variable are different, but after standardization, the metrics are common, expressed in standard deviations and represent the population’s distribution of abilities.
The distributions of the scale and theta scores are shown in Appendix Figures D1 and D2 . In each figure, the plots reflect a more normally distributed pattern for the theta scores (right panel) than for the scale scores (left panel). The companion table, Appendix Table D1 , shows the range of variation for the four outcomes (mean and standard deviations are 0 and 1 as per construction).
We next offer a comparison of the results obtained when using the scale scores versus using the theta scores ( Appendix Table D2 ). We highlight the following main similarities and differences between the results obtained using the scale scores and the results using the theta scores.
In Appendix Table D3 , we compare the results obtained using the different scales and the different proxies of socioeconomic status (our composite SES index, mother’s education, number of books, and household income).
There are two other significant pieces of information affecting the cognitive scores in more recent documentation released by NCES. In 2015, NCES announced in its ECLS-K User’s Manual that a
change in methodology required a re-calibration and re-reporting of the kindergarten reading scores since the release of the base-year file. Therefore, the kindergarten reading theta scores included in the K-1 data file are calculated differently than the previously released kindergarten theta scores and replace the kindergarten reading theta scores included in the base-year data file. The modeling approach stayed the same for mathematics and science, so the recalculation of kindergarten mathematics and science theta scores was not needed. (Tourangeau et al. 2015)
Following up on this, the most recent (2017) data user’s manual explains that
The method used to compute the theta scores allows for the calculation of theta for a given round that will not change based on later administrations of the assessments (which is not true for the scale scores, as described in the next section). Therefore, for any given child, the kindergarten, first-grade, and second-grade theta scores provided in subsequent data files will be the same as theta scores released in earlier data files , with one exception: the reading thetas provided in the base-year data file . After the kindergarten-year data collection, the methodology used to calibrate and compute reading scores changed; therefore, the reading thetas reported in the base-year file are not the same as the kindergarten reading thetas provided in the files with later-round data [emphasis added]. Any analysis involving kindergarten reading theta scores and reading theta scores from later rounds, for example an analysis looking at growth in reading knowledge and skills between the spring of kindergarten and the spring of first grade, should use the kindergarten reading theta scores from a data file released after the base year. The reading theta scores released in the kindergarten-year data file are appropriate for analyses involving only the kindergarten round data; analyses conducted with only data released in the base-year file are not incorrect, since those analyses do not compare kindergarten scores to scores in later rounds that were computed differently. However, now that the recomputed kindergarten theta scores are available in the kindergarten through first-grade and kindergarten through second-grade data files, it is recommended that researchers conduct any new analyses with the recomputed kindergarten reading theta scores. For more information on the methods used to calculate theta scores, see the ECLS-K: 2011 First-Grade and Second-Grade Psychometric Report (Najarian et al. forthcoming). (Tourangeau et al. 2017)
Therefore, because of these changes in NCES methodology and reporting, and in light of the comparisons in this appendix, one could expect additional slight changes in the estimates using the IRT-theta scores for reading for kindergarten if using rounds of data posterior to the first round (and probably if using the IRT-scale scores as well, as these values are derived from the theta scores), relative to the first data file of ECLS-K: 2010-2011 released by NCES in 2013. We would not necessarily expect, though, any changes when using the standardized transformation of those scores, because NCES’s documentation does not mention changes to the distribution of the scores, only to their values. We will explore these issues further upon the release of the scores that are comparable across the two ECLS-K studies without any transformation.
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The needs of children in Austin Independent School District (AISD) schools with the highest concentrations of poor, immigrant, and non-English-speaking families are supported through a combination of parent-organizing (schools with parent-organizing programs, led by the nonprofit Austin Interfaith, form a network of “Alliance Schools”), intensive embedding of social and emotional learning (SEL) in all aspects of school policy and practice, and the transformation of schools into “community schools” (i.e., schools that are hubs for the provision of academic, health, and social services).
The City Connects program provides targeted academic, social, emotional, and health supports to every child in 20 of the city’s schools with the highest shares of low-income, black, Hispanic, and immigrant students.
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The Northside Achievement Zone (NAZ) is a Promise Neighborhood, a designation awarded by the U.S. Department of Education Promise Neighborhoods program to some of the most distressed neighborhoods in the nation. Through the program, children and families who live in the 13-by-18 block NAZ receive individualized supports.
Through a collaboration between The Children’s Aid Society and the New York City Department of Education, 16 community schools in some of the most disadvantaged neighborhoods in three of the city’s five boroughs provide wraparound health, nutrition, mental health, and other services to students along with enriching in-and-out-of-school experiences, amplified by extensive parental and community engagement.
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Joplin’s Bright Futures initiative (which has spawned dozens of other Bright Futures affiliate districts under a Bright Futures USA umbrella since it launched in 2010) has a rapid response component that addresses children’s basic needs (within 24 hours of a need being reported), while strong school–community partnerships help meet students’ longer-term needs. Bright Futures also provides meaningful service learning opportunities in every school.
The “Kalamazoo Promise,” a guarantee by a group of anonymous local philanthropists to provide full college scholarships in perpetuity for graduates of the district’s public high schools brought Kalamazoo Public Schools (KPS), the city, and the community together to develop a set of comprehensive supports that enable more students to use the scholarships.
All students in Montgomery County Public Schools (MCPS) benefit from zoning laws that advance integration and strong union–district collaboration on an enriching, equity-oriented curriculum. These efforts are bolstered by extra funding and wraparound supports for high-needs schools and communities.
The Pea Ridge School District, a small suburban–rural district outside Fayetteville, Arkansas, is among the newer affiliates of Bright Futures USA, a national umbrella group that grew out of Bright Futures Joplin. As a Bright Futures affiliate, Pea Ridge is making good progress toward identifying and meeting students’ basic needs, engaging the community to meet longer-term needs, and making service learning a core component of school policy and practice.
Family and Community Resource Centers (FCRCs) currently serve 16 of the highest-needs Vancouver Public Schools (VPS) district schools, with mobile and lighter-touch support in other schools and plans to expand districtwide by 2020.
Eastern (appalachian) kentucky.
A federal Promise Neighborhood grant helps Berea College’s Partners for Education provide intensive supports for students and their families in four counties in the Eastern (Appalachian) region of Kentucky and provide lighter-touch supports in an additional 23 surrounding counties. (Berea College, which was established in 1855 by abolitionist education advocates, is unique among U.S. higher-education institutions. It admits only economically disadvantaged, academically promising students, most of whom are the first in their families to obtain postsecondary education, and it charges no tuition, so every student admitted can afford to enroll and graduates debt-free.)
Covariates from these models : ecls-k 1998--1999 and 2010--2011.
ECLS-K 1998–1999 | ECLS-K 2010–2011 |
---|---|
The SES is a composite variable reflecting the socioeconomic status of the household at the time of data collection. SES was created using components such as father/male guardian’s education and occupation; mother/female guardian’s education and occupation; and household income (see Tourangeau et al. 2009, 7-23–7-30). We use five SES quintiles dummies that are available. We use the following labels in the tables and figures: “Low SES” indicates the first or lowest socioeconomic quintile, “Middle-low SES” indicates the second-lowest quintile, “Middle SES” is the third quintile, “High-middle SES” indicates the fourth quintile, and “High SES” represents the highest or fifth quintile. | The construct is based on three different components (five total variables), including the educational attainment of parents or guardians, occupational prestige (determined by a score), and household income (see more details in Tourangeau et al. 2013, 7-56–7-60). We use the quintile indicators based on the continuous SES variable (we construct them). |
Information about whether the child’s household lives in poverty is obtained from a household-level poverty variable. The household’s income is compared with census poverty thresholds for 2006 (which vary by household size) and the household is considered to be in poverty if total household income is below the poverty threshold determined by the U.S. Census Bureau poverty threshold (Tourangeau et al. 2009, 7-24 and 7-25). | Information about whether the child’s household lives in poverty is obtained from a household-level poverty variable. This variable indicates whether the household income is below 200 percent of the U.S. Census Bureau poverty threshold. More details are provided in Tourangeau et al. 2013 (7-53 and 7-54). |
A variable indicates whether the student is a girl or a boy. | A dummy indicator represents whether the child is a boy or a girl. |
A variable indicates the race/ethnicity of the student—whether the child is white, black, Hispanic, Asian, or another ethnicity. Hispanic children are divided into two groups, those whose families speak English at home and those whose families do not. (This latter decomposition was first described and utilized by Nores and Barnett [2014] and Nores and García [2014]). | Our analysis includes dummy indicators of whether the race/ethnicity of the child is white, black, Hispanic, Asian, or “other.” Hispanic children are divided into two groups, those whose families speak English at home and those whose families do not. |
Age of the student calculated in months. | Age of the student is calculated in months. |
A variable indicates whether the language the student speaks at home is a language other than English. | Our analysis includes a dummy indicator that represents whether the language spoken in the child’s home is a language other than English (we call a child in this setting an English language learner, or ELL), versus whether the language spoken at home is English or English and other language(s). |
A variable indicates whether the child has a disability that has been diagnosed by a professional (composite variable). Questions in the parents’ interview about disabilities ask about the child’s ability to pay attention and learn, overall activity level, overall behavior and relationships to adults, overall emotional behavior (such as behaviors indicating anxiety or depression), ability to communicate, difficulty in hearing and understanding speech, and eyesight (Tourangeau et al. 2009, 7-17). | A dummy indicator represents whether the child has been diagnosed with a disability. |
A variable indicates whether the child is living with two parents, or with one parent or in another family structure. | A variable indicates whether the child lives with two parents versus living with one parent or in another family composition. |
A dummy indicator represents whether the child was cared for in a center-based setting or attended Head Start during the year prior to the kindergarten year, compared with other options. These alternatives include no nonparental care arrangements and care provided through other means (by a relative or a nonrelative, at home or outside the home, or a combination of options). | Our analysis includes a dummy indicator of whether the child was cared for in a center-based setting (including Head Start) during the year prior to the kindergarten year, compared with other options. These alternatives include no nonparental care arrangements and care provided through other means (by a relative or a nonrelative, at home or outside the home, or a combination of options). Any finding associated with this variable may be interpreted as the association between attending prekindergarten (pre-K) programs, compared with other options, but must be interpreted with caution. These coefficients should not be interpreted as the impact of pre-K schooling because the variable’s information is limited and the model uses it as a control-only variable. For a review of the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. 2010. |
This index captures the variance on a wide set of family early literacy practices. Using an index of activities instead of the underlying questions the index is composed of overcomes potential problems of multicolinearity and therefore improves the properties of our specifications. (This has an alpha of 0.6716). In particular, parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: reading books; telling stories; singing songs; and talking about nature or doing science projects. Parents are also asked how often the child reads picture books outside of school, and reads to or pretends to read to himself or to others outside of school. | This index captures the variance on a wide set of family early literacy practices. Using an index of activities instead of the underlying questions the index is composed of overcomes potential problems of multicolinearity and therefore improves the properties of our specifications. (This has an alpha of 0.6948.) In particular, parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: reading books; telling stories; singing songs; and talking about nature or doing science projects. Parents are also asked how often the child reads picture books outside of school, and reads to or pretends to read to himself or to others outside of school. |
Parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: playing games or doing puzzles; playing sports; building something or playing with construction toys; doing arts and crafts; or doing science projects. (This has an alpha of 0.5972.) | Parents are asked the frequency (“not at all,” “once or twice a week,” “three to six times a week,” or “every day”) with which they engage with the child in the following activities: playing games or doing puzzles; playing sports; building something or playing with construction toys; doing arts and crafts; or doing science projects. (This has an alpha of 0.5527.) |
This is coded as “below high school (8th–12th grades); high school graduate or equivalent; vocational/technical program/some college; bachelor’s degree/graduate or professional school with no degree; and graduate (master’s, doctorate, or professional) degree.” | This is coded as “below high-school (8th–12th grades); high school graduate or equivalent; vocational/technical program/some college; bachelor’s degree/graduate or professional school with no degree; and graduate (master’s, doctorate, or professional) degree”. |
We adjust the income brackets in 2010 for inflation. We use the continuous variable to construct the 18 categories to make it comparable to the variable in 2010. We calculate a continuous income variable using the midpoint between the minimum and maximum for each category (equal to the values in 2010 adjusted by inflation). We calculate the income quintiles using this variable. | The original income variable comes in 18 categories. We calculate a continuous income variable using the midpoint between the minimum and maximum for each category. We calculate the income quintiles using this variable. |
This is coded as “HS or less; 2 or more years of college; BA; MA; PHD or MD.” Parents are asked, “How far in school do you expect your child to go? Would you say you expect {him/her} to {attend or complete a certain level}?” | This is coded as “HS or less; 2 or more years of college/attend a vocational or technical school; BA; MA; PHD or MD.” |
This is represented by a continuous variable (0–200) and a categorical variable coded as “0 to 25; 26 to 50; 51 to 100; 101 to 199; more than 200.” For the regression analysis, the variable is divided by 10. Parents are asked, “About how many children’s books {does {CHILD} have/are} in your home now, including library books? Please only include books that are for children.” | This is represented by a continuous variable (0–200) and a categorical variable coded as “0 to 25; 26 to 50; 51 to 100; 101 to 199; more than 200.” For the regression analysis, the variable is divided by 10. |
Source: ECLS-K, kindergarten classes of 1998–1999 and 2010–2011 (National Center for Education Statistics)
1998 | 2010 | |
---|---|---|
Variable | Percent missing | Percent missing |
Race/ethnicity | ||
White | 0.2 | 0.5 |
Black | 0.2 | 0.5 |
Hispanic | 0.2 | 0.5 |
Hispanic English language learner (ELL) | 6.6 | 11.8 |
Hispanic English speaker | 6.6 | 11.8 |
Asian | 0.2 | 0.5 |
Others | 0.2 | 0.5 |
Socioeconomic status | 5.9 | 11.9 |
Family composition: Not living with two parents | 15.5 | 26.3 |
Mother’s education | 7.5 | 42.8 |
Pre-K care, center-based | 16.8 | 17.4 |
“Literacy/reading activities” index | 15.6 | 26.4 |
“Other activities” index | 15.6 | 26.5 |
Parents’ expectations for children’s educational attainment | 16.1 | 26.5 |
Number of books | 16.3 | 26.7 |
Outcomes | ||
Reading | 17.7 | 13.8 |
Math | 13.0 | 14.2 |
Self-control (by teachers) | 13.8 | 25.4 |
Approaches to learning (by teachers) | 10.4 | 18.7 |
Self-control (by parents) | 15.8 | 27.3 |
Approaches to learning (by parents) | 15.8 | 27.3 |
Note: For detailed information about the construction of these variables, see Appendix Table A1.
Scale scores, 1998 (left) and 2010 (right).
1998 | 2010 | |||||||
---|---|---|---|---|---|---|---|---|
N | (Mean, sd) | Min | Max | N | (Mean, sd) | Min | Max | |
Scale score–reading | 17,620 | (0,1) | -1.39 | 10.13 | 15,670 | (0,1) | -2.4 | 4.06 |
Theta score–reading | 17,620 | (0,1) | -2.72 | 4.30 | 15,670 | (0,1) | -3.47 | 5.01 |
Scale score–math | 18,640 | (0,1) | -1.69 | 9.86 | 15,600 | (0,1) | -2.22 | 4.23 |
Theta score–math | 18,640 | (0,1) | -3.13 | 4.48 | 15,600 | (0,1) | -5.78 | 6.28 |
Note: N is rounded to the nearest multiple of 10.
Model 1 (unadjusted) | Model 4 (fully adjusted) | |||||||
---|---|---|---|---|---|---|---|---|
Full sample | Restricted sample | |||||||
Scale scores | Theta scores | Scale scores | Theta scores | |||||
Reading | Math | Reading | Math | Reading | Math | Reading | Math | |
Gap in 1998 | 1.071*** | 1.258*** | 1.233*** | 1.330*** | 0.596*** | 0.610*** | 0.684*** | 0.632*** |
(0.024) | (0.022) | (0.024) | (0.022) | (0.031) | (0.031) | (0.032) | (0.031) | |
Change in gap by 2010 | 0.098*** | -0.008 | -0.052 | -0.078** | 0.080 | 0.051 | -0.016 | -0.002 |
(0.033) | (0.032) | (0.033) | (0.032) | (0.052) | (0.048) | (0.054) | (0.050) | |
N | 30,950 | 31,850 | 30,950 | 31,850 | 26,050 | 26,890 | 26,050 | 26,890 |
Adj.R2 | 0.152 | 0.189 | 0.170 | 0.197 | 0.293 | 0.336 | 0.336 | 0.353 |
Notes: Standard errors are in the parentheses. N is rounded to the nearest multiple of 10. Asterisks denote statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: ECLS-K, kindergarten classes of 1998-1999 and 2010–2011 (National Center for Education Statistics)
Model 1 (unadjusted) | Model 4 (fully adjusted) | ||||||||
---|---|---|---|---|---|---|---|---|---|
Full sample | Restricted sample | ||||||||
Scale scores | Theta scores | Scale scores | Theta scores | ||||||
Reading | Math | Reading | Math | Reading | Math | Reading | Math | ||
By SES | Gap in 1998 | 1.071*** | 1.258*** | 1.233*** | 1.330*** | 0.596*** | 0.610*** | 0.684*** | 0.632*** |
(0.024) | (0.022) | (0.024) | (0.022) | (0.031) | (0.031) | (0.032) | (0.031) | ||
Change in gap by 2010 | 0.098*** | -0.008 | -0.052 | -0.078** | 0.080 | 0.051 | -0.016 | -0.002 | |
(0.033) | (0.032) | (0.033) | (0.032) | (0.052) | (0.048) | (0.054) | (0.050) | ||
By mother’s education | Gap in 1998 | 1.294*** | 1.457*** | 1.412*** | 1.502*** | 0.696*** | 0.681*** | 0.739*** | 0.685*** |
(0.038) | (0.036) | (0.038) | (0.035) | (0.058) | (0.050) | (0.048) | (0.044) | ||
Change in gap by 2010 | -0.020 | -0.154*** | -0.135*** | -0.218*** | -0.075 | -0.119* | -0.135* | -0.182*** | |
(0.051) | (0.049) | (0.051) | (0.048) | (0.082) | (0.070) | (0.075) | (0.067) | ||
By number of books | Gap in 1998 | 0.736*** | 0.966*** | 0.847*** | 1.032*** | 0.347*** | 0.424*** | 0.388*** | 0.438*** |
(0.028) | (0.027) | (0.028) | (0.026) | (0.034) | (0.031) | (0.033) | (0.031) | ||
Change in gap by 2010 | 0.083** | -0.019 | -0.015 | -0.088** | -0.540*** | -0.818*** | -0.594*** | -0.829*** | |
(0.039) | (0.038) | (0.039) | (0.038) | (0.184) | (0.188) | (0.181) | (0.174) | ||
By household income | Gap in 1998 | 1.090*** | 1.308*** | 1.214*** | 1.320*** | 0.384*** | 0.443*** | 0.429*** | 0.439*** |
(0.042) | (0.041) | (0.042) | (0.041) | (0.058) | (0.060) | (0.049) | (0.050) | ||
Change in gap by 2010 | -0.127** | -0.230*** | -0.247*** | -0.292*** | -0.006 | -0.060 | -0.058 | -0.099 | |
(0.060) | (0.059) | (0.060) | (0.059) | (0.084) | (0.082) | (0.076) | (0.072) |
Notes: Standard errors are in parentheses. Asterisks denote statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1.
i. The sample design used to select the individuals in the study was a three-stage process that involved using primary sampling units and schools with probabilities proportional to the number of children and the selection of a fixed number of children per school. In the last stage, children enrolled in kindergarten or ungraded schools were selected within each sampled school. A clustered design was used to limit the number of geographic areas and to minimize the number of schools and the costs of the study (Tourangeau et al. 2013, 4-1).
ii. The dataset in the first year followed a stratified design structure (Ready 2010, 274), in which the primary sampling units were geographic areas consisting of counties or groups of counties. About 1,000 schools — 903 for 1998 and 968 for 2010—were selected, and about 24 children per school were surveyed. Assessment of the children was performed by trained evaluators, while parents were surveyed over the telephone. Teachers and school administrators completed the questionnaires in their schools.
iii. As a sensitivity check, we estimate Models 1 and 2 using Models 1’s and Model 2’s specifications but using the restricted sample (these results are not shown here, but are available upon request).
iv. As a sensitivity check, we estimate Model 3 parsimoniously, by including family characteristics only, and then adding family investments (prekindergarten care arrangements, early literacy practices at home, and number of books the child has), and then adding parental expectations (with and without interactions with time); results of the sensitivity check are not shown, but are available upon request).
v. We refer to the fact that we are using the same data and that the scale and theta scores are based on the same instruments and are not independent from each other. Advice on this possibility is found in Reardon (2007), who cites work by Murnane et al. (2006) and Selzer, Frank, and Bryk (1994) that also warn about this option.
vi. From NCES: “IRT uses the pattern of right and wrong responses to the items actually administered in an assessment and the difficulty, discriminating ability, and guess-ability of each item to estimate each child’s ability on the same continuous scale. IRT has several advantages over raw number-right scoring. By using the overall pattern of right and wrong responses and the characteristics of each item to estimate ability, IRT can adjust for the possibility of a low-ability child guessing several difficult items correctly. If answers on several easy items are wrong, the probability of a correct answer on a difficult item would be quite low. Omitted items are also less likely to cause distortion of scores, as long as enough items have been answered to establish a consistent pattern of right and wrong answers. Unlike raw number-right scoring, which treats omitted items as if they had been answered incorrectly, IRT procedures use the pattern of responses to estimate the probability of a child providing a correct response for each assessment question” (Tourangeau et al. 2017, 3-2).
vii. The quoted text is abridged to remove variables and formulas specific to Reardon’s study and not central here.
viii. Also, “the estimated scale score is the estimated number of questions the student would have gotten correct if he or she had been asked all of the items on the test. The estimated scale score is obtained by summing the predicted probabilities of a correct response over all items, given the student’s estimated theta score and the estimated item parameters” (Reardon 2007, 11).
ix. They are equally spaced units along the scale without a predefined zero point.
See related work on Student achievement | Education | Educational inequity | Children | Economic inequality | Inequality and Poverty | Early childhood
See more work by Emma García and Elaine Weiss
Big Ideas is Education Week’s annual special report that brings the expertise of our newsroom to bear on the challenges educators are facing in classrooms, schools, and districts.
In the report , EdWeek reporters ask hard questions about K-12 education’s biggest issues and offer insights based on their extensive coverage and expertise.
The goal is to question the status quo and explore opportunities to help build a better, more just learning environment for all students.
In the 2023 edition , our newsroom sought to dig deeper into new and persistent challenges. Our reporters consider some of the big questions facing the field: Why is teacher pay so stubbornly stalled? What should reading instruction look like? How do we integrate—or even think about—AI? What does it mean for parents to be involved in the decisionmaking around classroom curriculum? And, perhaps the most existential, what does it mean for schools to be “public”?
The reported essays below tackle these vexing and pressing questions. We hope they offer fodder for robust discussions.
To see how your fellow educator peers are feeling about a number of these issues, we invite you to explore the EdWeek Research Center’s survey of more than 1,000 teachers and school and district leaders .
Please connect with us on social media by using #K12BigIdeas or by emailing [email protected].
Over years of covering school finance, Mark Lieberman keep running up against one nagging question: Does the way we pay for public schools inherently contradict what we understand the goal of public education to be? Read more →
School districts are still operating largely as if the labor market for women hasn’t changed in the last half century, writes Alyson Klein. Read more →
Libby Stanford has been covering the parents’ rights groups that have led the charge to limit teaching about race, sexuality, and gender. In her essay, she explores what happens to students who miss out on that instruction. Read more→
When it comes to reading instruction, we keep having the same fights over and over again, writes Sarah Schwartz. That’s because, she says, we have a fundamental divide about what reading is and how to study it. Read more→
Lauraine Langreo makes the case for using AI to benefit teaching and learning while being aware of its potential downsides. Read more→
Edweek top school jobs.
Few would argue that the state of our education system has plenty of room for improvement. However, developing a plan to take schools in the right direction is easier said than done. The first challenge lies in identifying underlying problems keeping students from learning today. This challenge, in part, is due to the fact that the problems may change considerably depending on who is labeling them, whether it is students, parents, educators or lawmakers. Consider this list of 10 major challenges currently facing public schools, based on the perspective of many involved in the world of education today.
Classroom Size
Many areas of the country are facing classrooms that are literally busting out at the seams. A report at NEA Today two years ago discussed how schools in Georgia, in the midst of major funding cuts for schools, had no choice but to lift all class size limits to accommodate students with the faculty the school system could still afford to keep. More recently, Fairfax County in Virginia has been looking into a proposal to increase classroom sizes in the face of significant budget cuts. The Board of Education in South Carolina is also weighing their options in this area.
When money gets tight, classroom numbers are often impacted. Yet, most teachers agree that they cannot effectively teach every student in a classroom, if the class size exceeds about 30. Their statements are backed up by research. Class Size Matters cites a study performed by the Tennessee Star that found classes of 15-17 students in grades K-3 provided both long and short-term benefits to both the students and the teachers in those classrooms. Minority students, those living in poverty and male students appeared to benefit from smaller classroom sizes the most.
Technorati reported last fall that 22 percent of the children in the U.S. live at or below poverty level. American Graduate defines poverty as a family of four with an annual income level of $23,050 or lower. American Graduate also cites a report from the Southern Education Foundation, which shows in 17 states across the U.S., low-income students now comprise the majority of public school students in those states. Some estimates put poverty levels for public school students at 25% in the not-so-distant future.
Students living at or below poverty level tend to have the highest dropout rates. Studies show that students who do not get enough food or sleep are less likely to perform at their full academic potential. Schools know these truths first-hand, and despite efforts to provide students with basic essentials, teachers, administrators and lawmakers know there is simply not enough to go around.
Family Factors
Family factors also play a role in a teacher’s ability to teach students. Principals and teachers agree that what is going on at home will impact a student’s propensity to learn. Divorce , single parents, poverty, violence and many other issues are all challenges a student brings to school every day. While some teachers and administrators try to work with children in less than ideal family environments, they can only do so much – especially when parents are often not willing to partner with the schools to provide for the children.
Kids Health Guide reports that students are more technologically advanced than many teachers today, putting instructors at a decided disadvantage in the classroom. However, a student’s love of technology also tends to distract him from his schoolwork, according to NEA Today. When teachers don’t have the techno-savvy to compete with those devices, by bringing education and technology together , it can be difficult to keep students’ interest and attention to properly teach new concepts.
Technology needs to come into the classroom to keep up with the learning demands of the 21 st century. Schools that are already cash-strapped may find an unsurmountable challenge in coming up with the funding to bring computers and other forms of technology into their classes. Scholastic offers some tips for school districts that want to fit the bill for technology, including everything from asking individuals in the district for “big gifts” to going to Uncle Sam for the funding. The website also suggests negotiating prices on technology when possible and allowing student to bring their own from home.
Photo By Intel Free Press CC-BY-SA-2.0 , via Wikimedia Commons
Bullying is not a new problem, but it is one that has a profound impact on the learning aptitude of many students today. Technology has given bullies even more avenues to torment their victims – through social networking, texting and other virtual interactions. Cyberbullying has become a major issue for schools, as evidenced by the number of suicides that can be directly traced to bullying events . The fact that laws are still fuzzy regarding cyberbullying adds to the challenge – since parents, teachers and administrators are unsure of how to legally handle such issues.
Student Attitudes and Behaviors
Many public school teachers also cite student attitudes, such as apathy and disrespect for teachers, as a major problem facing schools today. A poll from the National Center for Education Statistics cited that problems like apathy, tardiness, disrespect and absenteeism posed significant challenges for teachers. These issues were seen more frequently at the secondary school level, rather than the primary grades.
No Child Left Behind
Many students, parents and teachers see No Child Left Behind as a detriment to the public education environment today. Although the current Obama Administration is working to reform NCLB policies, the focus in education on both the national and state level continues to be on the testing process. Student test scores are now being used by a number of states as a way to evaluate teacher performance, putting even more pressure on faculty in schools to “teach to the tests.”
NEA Today quotes Kansas special educator Shelly Dunham as saying, “Testing, testing, testing, what is the point of testing? Do we use the data to remediate those who do not measure up? No!” Many teachers believe they are forced to teach to the annual standardized tests, and activities like recess and lunch have been cut way down to make more time for academics in light of the new testing procedures.
Parent Involvement
Often teachers find there is no happy medium when it comes to parental involvement , according to the Kids Health Guide. Some parents won’t be seen for the entire school year, no matter what sort of issues might arise. Others never seem to go away, hovering over the child and teacher and interfering with the education process. There are ways parents can become involved and support their child’s education at the same time, but teachers don’t always get that level from parents.
Student Health
Obesity has reached epidemic proportions in the U.S., and the same poor eating habits that led to the obesity problem may also be contributing to lower student achievement. Obesity also increases a student’s risk for other conditions, like diabetes and high blood pressure, which could result in higher absenteeism and more academic issues.
Photo By English: Lance Cpl. Ryan M. Joyner [Public domain], via Wikimedia Commons
The national school lunch movement Let’s Move! has been working to bring healthier options into school lunchrooms across the country. According to the website , the U.S. Department of Agriculture released new guidelines in 2012 to boost the nutritional quality of the meals students get at school. Exercise programs are also coming to schools across the country to promote more physical activity among students of all ages. However, it seems the country as a whole still has a long way to go to get on the road to better health on a large scale.
Budget cuts have created huge problems for most public schools in recent years. Less funding means smaller staffs, fewer resources and a lower number of services for students. While some argue that throwing more money at the education problems won’t make them go away, others assert that lack of funding caused many of the problems in the first place.
There are many problems in public schools today, but identifying those issues is half the battle. With a laundry list of challenges to face, now is the time for educators, parents and lawmakers to come together and begin to find solutions – for the benefit of all students in public schools today. Questions? Contact us on Twitter. @publicschoolreview
First grade students in Pakistan’s Balochistan Province are learning the alphabet through child-friendly flash cards. Their learning materials help educators teach through interactive and engaging activities and are provided free of charge through a student’s first learning backpack. © World Bank
THE NAME OF THE DOG IS PUPPY. This seems like a simple sentence. But did you know that in Kenya, Tanzania, and Uganda, three out of four third grade students do not understand it? The world is facing a learning crisis . Worldwide, hundreds of millions of children reach young adulthood without even the most basic skills like calculating the correct change from a transaction, reading a doctor’s instructions, or understanding a bus schedule—let alone building a fulfilling career or educating their children. Education is at the center of building human capital. The latest World Bank research shows that the productivity of 56 percent of the world’s children will be less than half of what it could be if they enjoyed complete education and full health. For individuals, education raises self-esteem and furthers opportunities for employment and earnings. And for a country, it helps strengthen institutions within societies, drives long-term economic growth, reduces poverty, and spurs innovation.
One of the most interesting, large scale educational technology efforts is being led by EkStep , a philanthropic effort in India. EkStep created an open digital infrastructure which provides access to learning opportunities for 200 million children, as well as professional development opportunities for 12 million teachers and 4.5 million school leaders. Both teachers and children are accessing content which ranges from teaching materials, explanatory videos, interactive content, stories, practice worksheets, and formative assessments. By monitoring which content is used most frequently—and most beneficially—informed decisions can be made around future content.
In the Dominican Republic, a World Bank supported pilot study shows how adaptive technologies can generate great interest among 21st century students and present a path to supporting the learning and teaching of future generations. Yudeisy, a sixth grader participating in the study, says that what she likes doing the most during the day is watching videos and tutorials on her computer and cell phone. Taking childhood curiosity as a starting point, the study aimed to channel it towards math learning in a way that interests Yudeisy and her classmates.
Yudeisy, along with her classmates in a public elementary school in Santo Domingo, is part of a four-month pilot to reinforce mathematics using software that adapts to the math level of each student. © World Bank
Adaptive technology was used to evaluate students’ initial learning level to then walk them through math exercises in a dynamic, personalized way, based on artificial intelligence and what the student is ready to learn. After three months, students with the lowest initial performance achieved substantial improvements. This shows the potential of technology to increase learning outcomes, especially among students lagging behind their peers. In a field that is developing at dizzying speeds, innovative solutions to educational challenges are springing up everywhere. Our challenge is to make technology a driver of equity and inclusion and not a source of greater inequality of opportunity. We are working with partners worldwide to support the effective and appropriate use of educational technologies to strengthen learning.
Successful education reforms require good policy design, strong political commitment, and effective implementation capacity . Of course, this is extremely challenging. Many countries struggle to make efficient use of resources and very often increased education spending does not translate into more learning and improved human capital. Overcoming such challenges involves working at all levels of the system.
At the central level, ministries of education need to attract the best experts to design and implement evidence-based and country-specific programs. District or regional offices need the capacity and the tools to monitor learning and support schools. At the school level, principals need to be trained and prepared to manage and lead schools, from planning the use of resources to supervising and nurturing their teachers. However difficult, change is possible. Supported by the World Bank, public schools across Punjab in Pakistan have been part of major reforms over the past few years to address these challenges. Through improved school-level accountability by monitoring and limiting teacher and student absenteeism, and the introduction of a merit-based teacher recruitment system, where only the most talented and motivated teachers were selected, they were able to increase enrollment and retention of students and significantly improve the quality of education. "The government schools have become very good now, even better than private ones," said Mr. Ahmed, a local villager.
The World Bank, along with the Bill and Melinda Gates Foundation, and the UK’s Department for International Development, is developing the Global Education Policy Dashboard . This new initiative will provide governments with a system for monitoring how their education systems are functioning, from learning data to policy plans, so they are better able to make timely and evidence-based decisions.
In fact, it will take a generation to realize the full benefits of high-quality teachers, the effective use of technology, improved management of education systems, and engaged and prepared learners. However, global experience shows us that countries that have rapidly accelerated development and prosperity all share the common characteristic of taking education seriously and investing appropriately. As we mark the first-ever International Day of Education on January 24, we must do all we can to equip our youth with the skills to keep learning, adapt to changing realities, and thrive in an increasingly competitive global economy and a rapidly changing world of work.
The schools of the future are being built today. These are schools where all teachers have the right competencies and motivation, where technology empowers them to deliver quality learning, and where all students learn fundamental skills, including socio-emotional, and digital skills. These schools are safe and affordable to everyone and are places where children and young people learn with joy, rigor, and purpose. Governments, teachers, parents, and the international community must do their homework to realize the promise of education for all students, in every village, in every city, and in every country.
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https://educationhorizons.com/blog/top-reporting-challenges-for-school-leaders-in-2022-and-how-to-solve-them/
So why is it still so hard to create timely, accurate, and insightful reports – reports that provide information to help school teachers and leaders make informed decisions?
There’s certainly no lack of information to work with. In recent years, the amount of information available on students, learning and schools has exploded.
It comes down to the tools.
Without the right system, reporting is not only a laborious and frustrating task; it can be a real-time sink. What should be a simple task turns into a full-time job of extracting the data from various systems and then presenting it in a way that others can understand and use to take action.
Let’s dive into the key challenges in reporting, and what you can do to solve the problems.
Wrestling records from multiple systems
In our survey of more than 1,000 Australian education professionals, 70% identified maintaining accurate student records during the whole student lifecycle as a key challenge.
It’s no wonder when we see that schools with around 1200 students can have 14 or more different software systems running.
That’s 14 systems your staff use to find the information needed on any given day. And the average number of systems a school uses increases proportionately as its student numbers increase.
That’s where the problems start. Spending hours or days hunting down data, then confirming its accuracy and consistency drains staff productivity.
The more steps it takes to gather information, the more inefficient, error-prone, and time-consuming your reporting process will be. This also means there can be a lower level of trust in the information. How can you know it’s the latest and most accurate information on students?
With many schools saying they are aggregating data from disparate systems, this is a struggle that’s all too relatable for too many school admin staff.
Use one tech solution partner
Instead of having multiple disparate systems that do not integrate or talk to each other, one solution partner can help manage school records and systems as a single source of truth.
For example, when a school uses the SEQTA Learning Management System and Synergetic , a school MIS system, the two seamlessly connect. While SEQTA provides integrated wellbeing, learning and attendance management, Synergetic offers a whole-school solution for administration, fundraising, finance and student management.
Together, schools have a single source of truth for their essential student and community information. Data is easy to capture and access, which saves time for administrators, teachers and leaders.
Not only does a single source of truth vastly reduce the amount of time it takes to gather information, it greatly reduces the number of errors. With a single source of truth, schools can refocus the conversation around insights and action instead of debating accountability and accuracy.
Massive aggregation effort required for compliance reporting
School leaders are facing increasing complexities in managing compliance for their schools – and reporting is at the top of the list.
With countless legal obligations to comply with, government boxes to tick and ongoing changes to keep up with, compliance reporting needs to be seamless.
Yet multiple systems make reporting far more complex than it should be. School administrators can spend hours or even days hunting down data across systems and confirming accuracy.
Choose systems with powerful compliance reporting engines
To keep pace with the continuous compliance demands, you need systems with powerful reporting engines that can streamline the process.
With the new Zunia student information platform, compliance reporting is made quick and easy.
Whether it’s NAPLAN, a board report, attendance statistics or student residential reports, Zunia has pre-built reports that mean you can address standard compliance requirements in minutes, not days.
Not every school has the same compliance obligations, which is why the expert team at Education Horizons can also work with schools to build customised compliance reports and dashboards.
Finding time for teachers to complete academic reporting
The top challenge for 52% of teachers in 2021 was the lack of time for non-classroom work, according to our survey . The reality is teachers are burdened with many administrative tasks and paperwork, which takes time away from preparing and delivering high-quality teaching.
One of these burdens is academic reports.
According to an ACER report , school leaders report the number of teachers taking personal or sick leave spikes at the end of each semester at report-writing times.
What’s more, teachers report that their attention at these times is diverted from the core aspects of their job such as planning high-quality instruction and continuing the delivery of curriculum.
This can impact students too. As teachers feel pressured to meet reporting deadlines, they rush to assign final assessments in preparation for reporting, leaving students feeling overwhelmed.
Academic reports are a budget burden too. The cost of one academic reporting cycle for an average primary school (345 students) is the equivalent of a salary for 1 FTE teacher for a year.
Meanwhile, parents would actually prefer more frequent communication about their child’s learning with continuous reporting throughout the semester. That way they can address any learning struggles as they arise, rather than waiting until the end of semester.
Choose a learning management system with robust feedback tools for continuous reporting
Look for a learning management system that supports a continuous reporting framework, so you can provide timely and targeted feedback to students and parents about their learning progress for each subject.
For example, SEQTA provides a range of feedback tools that provide opportunities for students to predict and reflect on the assessment of their work, and set learning goals.
This data is collected in a single system that captures all assessment and reporting data in an intuitive design, so teachers and school leaders can feel confident that their primary roles are not going to be hijacked by endless reporting admin.
To overcome these top reporting challenges, you need to go beyond yesterday’s inadequate information access and disparate reporting systems.
You need an Education Management Information System that provides a single source of truth, streamlines reporting processes and frees your staff to do what they do best.
Are you ready to unlock the benefits.
Our portfolio of class leading EdTech solutions can help your school today — please contact us to learn more.
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Eduardo velez bustillo, harry a. patrinos.
In 2022, we published, Lessons for the education sector from the COVID-19 pandemic , which was a follow up to, Four Education Trends that Countries Everywhere Should Know About , which summarized views of education experts around the world on how to handle the most pressing issues facing the education sector then. We focused on neuroscience, the role of the private sector, education technology, inequality, and pedagogy.
Unfortunately, we think the four biggest problems facing education today in developing countries are the same ones we have identified in the last decades .
1. The learning crisis was made worse by COVID-19 school closures
Low quality instruction is a major constraint and prior to COVID-19, the learning poverty rate in low- and middle-income countries was 57% (6 out of 10 children could not read and understand basic texts by age 10). More dramatic is the case of Sub-Saharan Africa with a rate even higher at 86%. Several analyses show that the impact of the pandemic on student learning was significant, leaving students in low- and middle-income countries way behind in mathematics, reading and other subjects. Some argue that learning poverty may be close to 70% after the pandemic , with a substantial long-term negative effect in future earnings. This generation could lose around $21 trillion in future salaries, with the vulnerable students affected the most.
2. Countries are not paying enough attention to early childhood care and education (ECCE)
At the pre-school level about two-thirds of countries do not have a proper legal framework to provide free and compulsory pre-primary education. According to UNESCO, only a minority of countries, mostly high-income, were making timely progress towards SDG4 benchmarks on early childhood indicators prior to the onset of COVID-19. And remember that ECCE is not only preparation for primary school. It can be the foundation for emotional wellbeing and learning throughout life; one of the best investments a country can make.
3. There is an inadequate supply of high-quality teachers
Low quality teaching is a huge problem and getting worse in many low- and middle-income countries. In Sub-Saharan Africa, for example, the percentage of trained teachers fell from 84% in 2000 to 69% in 2019 . In addition, in many countries teachers are formally trained and as such qualified, but do not have the minimum pedagogical training. Globally, teachers for science, technology, engineering, and mathematics (STEM) subjects are the biggest shortfalls.
4. Decision-makers are not implementing evidence-based or pro-equity policies that guarantee solid foundations
It is difficult to understand the continued focus on non-evidence-based policies when there is so much that we know now about what works. Two factors contribute to this problem. One is the short tenure that top officials have when leading education systems. Examples of countries where ministers last less than one year on average are plentiful. The second and more worrisome deals with the fact that there is little attention given to empirical evidence when designing education policies.
To help improve on these four fronts, we see four supporting trends:
1. Neuroscience should be integrated into education policies
Policies considering neuroscience can help ensure that students get proper attention early to support brain development in the first 2-3 years of life. It can also help ensure that children learn to read at the proper age so that they will be able to acquire foundational skills to learn during the primary education cycle and from there on. Inputs like micronutrients, early child stimulation for gross and fine motor skills, speech and language and playing with other children before the age of three are cost-effective ways to get proper development. Early grade reading, using the pedagogical suggestion by the Early Grade Reading Assessment model, has improved learning outcomes in many low- and middle-income countries. We now have the tools to incorporate these advances into the teaching and learning system with AI , ChatGPT , MOOCs and online tutoring.
2. Reversing learning losses at home and at school
There is a real need to address the remaining and lingering losses due to school closures because of COVID-19. Most students living in households with incomes under the poverty line in the developing world, roughly the bottom 80% in low-income countries and the bottom 50% in middle-income countries, do not have the minimum conditions to learn at home . These students do not have access to the internet, and, often, their parents or guardians do not have the necessary schooling level or the time to help them in their learning process. Connectivity for poor households is a priority. But learning continuity also requires the presence of an adult as a facilitator—a parent, guardian, instructor, or community worker assisting the student during the learning process while schools are closed or e-learning is used.
To recover from the negative impact of the pandemic, the school system will need to develop at the student level: (i) active and reflective learning; (ii) analytical and applied skills; (iii) strong self-esteem; (iv) attitudes supportive of cooperation and solidarity; and (v) a good knowledge of the curriculum areas. At the teacher (instructor, facilitator, parent) level, the system should aim to develop a new disposition toward the role of teacher as a guide and facilitator. And finally, the system also needs to increase parental involvement in the education of their children and be active part in the solution of the children’s problems. The Escuela Nueva Learning Circles or the Pratham Teaching at the Right Level (TaRL) are models that can be used.
3. Use of evidence to improve teaching and learning
We now know more about what works at scale to address the learning crisis. To help countries improve teaching and learning and make teaching an attractive profession, based on available empirical world-wide evidence , we need to improve its status, compensation policies and career progression structures; ensure pre-service education includes a strong practicum component so teachers are well equipped to transition and perform effectively in the classroom; and provide high-quality in-service professional development to ensure they keep teaching in an effective way. We also have the tools to address learning issues cost-effectively. The returns to schooling are high and increasing post-pandemic. But we also have the cost-benefit tools to make good decisions, and these suggest that structured pedagogy, teaching according to learning levels (with and without technology use) are proven effective and cost-effective .
4. The role of the private sector
When properly regulated the private sector can be an effective education provider, and it can help address the specific needs of countries. Most of the pedagogical models that have received international recognition come from the private sector. For example, the recipients of the Yidan Prize on education development are from the non-state sector experiences (Escuela Nueva, BRAC, edX, Pratham, CAMFED and New Education Initiative). In the context of the Artificial Intelligence movement, most of the tools that will revolutionize teaching and learning come from the private sector (i.e., big data, machine learning, electronic pedagogies like OER-Open Educational Resources, MOOCs, etc.). Around the world education technology start-ups are developing AI tools that may have a good potential to help improve quality of education .
After decades asking the same questions on how to improve the education systems of countries, we, finally, are finding answers that are very promising. Governments need to be aware of this fact.
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Consultant, Education Sector, World Bank
Senior Adviser, Education
Alice albright alice albright chief executive officer - global partnership for education.
July 25, 2019
The following is one of eight briefs commissioned for the 16th annual Brookings Blum Roundtable, “2020 and beyond: Maintaining the bipartisan narrative on US global development.”
Addressing today’s massive global education crisis requires some disruption and the development of a new 21st-century aid delivery model built on a strong operational public-private partnership and results-based financing model that rewards political leadership and progress on overcoming priority obstacles to equitable access and learning in least developed countries (LDCs) and lower-middle-income countries (LMICs). Success will also require a more efficient and unified global education architecture. More money alone will not fix the problem. Addressing this global challenge requires new champions at the highest level and new approaches.
In an era when youth are the fastest-growing segment of the population in many parts of the world, new data from the UNESCO Institute for Statistics (UIS) reveals that an estimated 263 million children and young people are out of school, overwhelmingly in LDCs and LMICs. 1 On current trends, the International Commission on Financing Education Opportunity reported in 2016 that, a far larger number—825 million young people—will not have the basic literacy, numeracy, and digital skills to compete for the jobs of 2030. 2 Absent a significant political and financial investment in their education, beginning with basic education, there is a serious risk that this youth “bulge” will drive instability and constrain economic growth.
Despite progress in gender parity, it will take about 100 years to reach true gender equality at secondary school level in LDCs and LMICs. Lack of education and related employment opportunities in these countries presents national, regional, and global security risks.
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Among global education’s most urgent challenges is a severe lack of trained teachers, particularly female teachers. An additional 9 million trained teachers are needed in sub-Saharan Africa by 2030.
Refugees and internally displaced people, now numbering over 70 million, constitute a global crisis. Two-thirds of the people in this group are women and children; host countries, many fragile themselves, struggle to provide access to education to such people.
Highlighted below are actions and reforms that could lead the way toward solving the crisis:
Annual high-level stock take at the G-7. The next U.S. administration can work with G-7 partners to secure agreement on an annual stocktaking of progress against this new global education agenda at the upcoming G-7 summits. This also will help ensure sustained focus and pressure to deliver especially on equity and inclusion. Global Partnership for Education’s participation at the G-7 Gender Equality Advisory Council is helping ensure that momentum is maintained to mobilize the necessary political leadership and expertise at country level to rapidly step up progress in gender equality, in and through education. 3 Also consider a role for the G-20, given participation by some developing country partners.
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COMMENTS
In 2022, about 59 percent of 3- to 5-year-olds in the United States were enrolled in school overall,28 including. 39 percent enrolled in public schools and 20 percent who were receiving a private education.29 The total enrollment rate was higher for 5-year-olds than for 3- to 4-year-olds (84 vs. 47 percent; fgure 2).
problems of practice and promote school improvement. This process is designed to be feasible for a wide variety of districts, including small, rural districts. While this process may be used in any type of school or district, the examples throughout are also focused on common problems of practice seen in rural schools across the country.
Achieve has developed sample student-level score reports for families and educators, as well as a sample score report at the school level. These reports are intended to illuminate effective practices in reporting assessment data to families, teachers and school leaders. As such, the reports are completely open to states to adapt for their own ...
Differences by poverty level. Teachers' views on problems students face at their school also vary by school poverty level. Majorities of teachers in high- and medium-poverty schools say chronic absenteeism is a major problem where they teach (66% and 58%, respectively). A much smaller share of teachers in low-poverty schools say this (34% ...
May 2023. On behalf of the National Center for Education Statistics (NCES), I am pleased to present the 2023 edition of the Condition of Education. The Condition is an annual report mandated by the U.S. Congress that summarizes the latest data on education in the United States, including international comparisons.
In the American education system, problems are not limited to elementary schools, middle schools, and high schools. Here are details on a few of the current education issues at the college level: 1. Student loan forgiveness. Here's how the American public education system works: Students attend primary and secondary school at no cost.
1 Choose a topic based on the assignment. Before you start writing, you need to pick the topic of your report. Often, the topic is assigned for you, as with most business reports, or predetermined by the nature of your work, as with scientific reports. If that's the case, you can ignore this step and move on.
Tweet your comments with #K12BigIdeas . No. 1: Kids are right. School is boring. Daryn Ray for Education Week. Out-of-school learning is often more meaningful than anything that happens in a ...
Model 3 also includes the interactions between the early education variables with time.15 In the fourth data column (Model 4), we control for the same factors as in Model 3 but add controls for parental expectations of children's educational attainment (whether they expect their children's highest level of education attained will be high ...
9. Parent engagement. When school went remote, families got a better sense of what their children were learning. It's something schools can build on, if they can make key cultural shifts. Read ...
Big Ideas is Education Week's annual special report that brings the expertise of our newsroom to bear on the challenges educators are facing in classrooms, schools, and districts. In the report ...
The Report on the Condition of Education 2021 encompasses key findings from the Condition of Education Indicator System. The Indicator System for 2021 presents 86 indicators, including 22 indicators on crime and safety topics, and can be accessed online through the website or by downloading PDFs for the individual indicators.
Poverty. Technorati reported last fall that 22 percent of the children in the U.S. live at or below poverty level. American Graduate defines poverty as a family of four with an annual income level of $23,050 or lower. American Graduate also cites a report from the Southern Education Foundation, which shows in 17 states across the U.S., low-income students now comprise the majority of public ...
In rural India, nearly three-quarters of third graders cannot solve a two-digit subtraction problem such as 46 minus 17, and by grade five — half still cannot do so. The world is facing a learning crisis. While countries have significantly increased access to education, being in school isn't the same thing as learning.
Photo credit: Shutterstock In our recent The State of the Global Education Crisis: A Path to Recovery report (produced jointly by UNESCO, UNICEF, and the World Bank), we sounded the alarm: this generation of students now risks losing $17 trillion in lifetime earnings in present value, or about 14 percent of today's global GDP, because of COVID-19-related school closures and economic shocks.
Reporting Challenge 3: Finding time for teachers to complete academic reporting. The top challenge for 52% of teachers in 2021 was the lack of time for non-classroom work, according to our survey. The reality is teachers are burdened with many administrative tasks and paperwork, which takes time away from preparing and delivering high-quality ...
We focused on neuroscience, the role of the private sector, education technology, inequality, and pedagogy. Unfortunately, we think the four biggest problems facing education today in developing countries are the same ones we have identified in the last decades. 1. The learning crisis was made worse by COVID-19 school closures.
More than a quarter of a billion children and young people have been "left behind" and are totally excluded from education systems around the world, and the pandemic has made the problem worse, UNESCO's 2020 Global Education Monitoring Report shows. While most young people in developed countries treat going to school as a given, many of the world's most vulnerable and disadvantaged ...
Here are five issues that elementary educators commonly face—and how to overcome them. 1. Providing equal educational opportunities for all students. The issue: Every classroom holds students with a range of learning needs. If you focus on high-achieving students, you risk leaving other students behind.
Action research at the school level: possibilities and problems. Action Research at the School Level: possibilities and problems. SHIRLEY GRUNDY. Murdoch University, Australia. ABSTRACT In this paper it is argued that it is necessary to understand the improvement of the quality of education as a responsibility for school communities as a whole.
Report on the Condition of Education 2022 Publication . Report on the Condition of Education 2022 Publication . [email protected] . May 2022. ... Enrollment in public elementary and secondary schools, by level: Fall 2009 through fall 2020 ..... 10 4. School enrollment, by school type: Selected years, fall 2009 through fall 2019 .....
When educational leaders think about how to solve problems, we expect them to identify a problem, think about causes and a theory of action, implement changes, and reflect on effects. This straightforward sequence is actually quite challenging. Through writings and interviews collected over 2 years within a doctor of education program, this study examines leaders' problem solving in real ...
Among global education's most urgent challenges is a severe lack of trained teachers, particularly female teachers. An additional 9 million trained teachers are needed in sub-Saharan Africa by ...