# Maths results for 10 to 11 year olds

Published

Last updated 4 March 2021 - see all updates

## 1. Main facts and figures

• 79% of 10 to 11 year olds met the expected standard in maths in the 2018 to 2019 school year

• 27% of pupils met the higher standard

• pupils from the Chinese ethnic group were the most likely out of all ethnic groups to meet the expected and higher standards

• White Gypsy and Roma pupils were least likely out of all ethnic groups to meet both the expected and higher standards

• pupils from the Chinese ethnic group made the most progress between 7 and 11 years old

• in every ethnic group except White Irish, girls were more likely than boys to meet the expected standard

• in every ethnic group except Black Caribbean, boys were more likely than girls to meet the higher standard

• Black Caribbean and White Irish Traveller pupils made the least progress between 7 and 11 years old

• boys from the Chinese ethnic group had the highest average scaled score, at 112 compared with an average of 105

## 2. Things you need to know

### What the data measures

The data measures the percentage of pupils who met the expected and higher standards in maths by the end of year 6, when they are usually 10 or 11 years old.

Teachers assess each pupil against the following:

• expected standard – pupils must have a scaled score of 100 or more
• higher standard – pupils must have a scaled score of 110 or more

The progress score measures the progress that each pupil made between the end of year 2 and the end of year 6. This is compared with all other pupils in England with similar key stage 1 results. The data shows an average progress score for each ethnic group – a score of 0 (the national average) means pupils are making the expected amount of progress.

Percentages are rounded to the nearest whole number. Progress scores are rounded to 1 decimal place.

### The ethnic groups used in the data

The data uses the ethnic groups from the 2001 Census, with 2 exceptions:

• White Irish Traveller, and White Gypsy and Roma children have been separated into 2 categories
• pupils in the Chinese ethnic group are in a separate category from Asian pupils

Local authority data is shown for 6 aggregated ethnic groups:

• Asian
• Black
• Chinese
• Mixed
• White
• Other

This means estimates are shown for these groups as a whole. This is to make sure group sizes are big enough to make reliable generalisations.

Ethnicity was known for 99% of pupils.

### Methodology

Read the detailed methodology document (PDF opens in a new window or tab) for the data on this page.

### In the data file

• confidence intervals for each ethnic group’s progress score – find out how we use confidence intervals to determine how reliable estimates are

## 3. By ethnicity

Percentage of pupils meeting the expected and higher standards in maths, and average scaled score and progress score, by ethnicity
Ethnicity Expected standard Higher standard Average scaled score Progress score
All 79 27 105 0.0
Asian 84 36 107 1.8
Indian 89 48 109 2.5
Pakistani 79 27 105 1.1
Asian other 87 43 108 2.6
Black 78 25 105 0.3
Black African 81 28 106 0.9
Black Caribbean 70 15 103 -1.4
Black other 74 22 104 0.0
Chinese 94 64 111 4.4
Mixed 79 28 105 -0.1
Mixed White/Asian 84 36 107 0.7
Mixed White/Black African 78 25 105 -0.2
Mixed White/Black Caribbean 72 18 103 -1.3
Mixed other 80 30 106 0.4
White 78 25 105 -0.3
White British 78 25 105 -0.5
White Irish 82 34 106 0.6
Gypsy/Roma 34 4 96 -0.8
Irish Traveller 40 5 98 -1.4
White other 80 30 106 2.2
Other 79 31 106 2.4
Unknown 62 20 104 -0.4

### Summary of Maths results for 10 to 11 year olds By ethnicity Summary

The data shows that:

• 79% of pupils met the expected standard in maths in year 6 (when they were 10 or 11 years old)
• 27% met the higher standard
• 94% of pupils from the Chinese ethnic group met the expected standard and 64% met the higher standard, the highest percentages out of all ethnic groups
• 34% of pupils from the White Gypsy and Roma ethnic group met the expected standard and 4% met the higher standard, the lowest percentages out of all ethnic groups
• pupils from the Chinese ethnic group had the highest average scaled score (111) compared with the national average (105)
• White Gypsy and Roma pupils had the lowest average scaled score (96)
• pupils from the Chinese ethnic group made the most progress with a score of 4.4 (compared with the national average of 0.0)
• pupils from the Black Caribbean and White Irish Traveller ethnic groups made the least progress with scores of -1.4

## 4. By ethnicity and gender

 Boys Expected standard Boys Higher standard Boys Average scaled score Boys Progress score Girls Expected standard Boys Girls Ethnicity 78 29 105 0.7 79 24 105 -0.7 84 39 107 2.6 84 34 106 1.1 83 37 107 2.7 85 32 106 1.1 89 51 109 3.2 90 45 108 1.8 79 29 105 1.9 79 24 105 0.2 87 45 108 3.3 87 40 107 1.9 76 25 105 0.7 80 24 105 -0.1 79 28 106 1.3 83 27 105 0.4 67 15 102 -1.1 73 16 103 -1.6 71 23 104 0.3 78 21 104 -0.4 93 68 112 5.1 95 60 111 3.7 78 29 105 0.5 80 26 105 -0.7 84 39 107 1.4 85 34 106 0.0 76 26 105 0.3 80 24 105 -0.8 70 19 103 -0.8 74 16 103 -1.8 80 32 106 1.0 81 28 105 -0.2 78 28 105 0.4 79 22 104 -1.1 78 27 105 0.2 79 22 104 -1.3 82 36 107 1.3 81 31 106 -0.2 33 5 97 0.0 35 4 96 -1.5 39 6 98 -1.0 41 3 98 -1.7 79 33 106 3.0 80 27 105 1.4 78 34 106 3.1 79 28 105 1.6 61 21 104 0.2 63 19 103 -1.1

### Summary of Maths results for 10 to 11 year olds By ethnicity and gender Summary

The data shows that:

• 79% of girls and 78% of boys met the expected standard in maths in year 6 (when they were 10 or 11 years old)

• 29% of boys and 24% of girls met the higher standard

• in every ethnic group except White Irish, girls were more likely than boys to meet the expected standard

• in every ethnic group except Black Caribbean, boys were more likely than girls to meet the higher standard

• girls from the Chinese ethnic group were the most likely to meet the expected standard (95%)

• boys from the Chinese ethnic group were most likely to meet the higher standard (68%)

• boys from the White Gypsy and Roma ethnic group were least likely to meet the expected standard (33%)

• girls from the White Irish Traveller ethnic group were least likely to meet the higher standard (3%)

• the smallest gap between boys (82%) and girls (81%) meeting the expected standard was in the White Irish ethnic group

• the biggest gap between girls and boys meeting the expected standard was in the Black Other (78% compared with 71%) and the Black Caribbean (73% compared with 67%) ethnic groups

• boys from the Chinese ethnic group had the highest average scaled score (112)

• girls from the White Gypsy and Roma ethnic group had the lowest average scaled score (96)

• boys from the Chinese ethnic group had the highest average progress score (5.1)

• girls from the Mixed White and Black Caribbean ethnic groups had the lowest average progress score (-1.8)

## 5. By ethnicity and area

 All % All Pupils Asian % Asian Pupils Black % Black Pupils All Asian Black Chinese Mixed White Other Local authority 80 2,813 88 673 82 892 100 10 75 232 76 962 65 31 86 3,613 90 421 81 401 100 55 85 364 87 1,929 86 397 81 2,285 96 24 82 18 100 2 76 54 81 2,159 100 10 80 1,444 93 26 64 7 80 4 74 70 80 1,319 67 6 71 1,533 71 232 73 91 83 5 63 161 72 1,018 64 18 81 2,729 93 252 86 519 98 55 82 192 78 1,650 73 43 77 12,487 79 4,820 76 1,604 94 82 73 1,016 75 4,139 79 674 82 1,793 83 820 88 29 100 5 82 59 80 854 100 21 82 1,378 91 32 60 3 75 3 75 45 83 1,288 75 6 79 3,124 80 779 67 167 100 6 81 126 80 1,981 70 52 81 3,120 95 125 81 13 93 13 84 190 81 2,722 78 31 79 1,123 87 68 79 38 90 9 84 85 78 911 86 6 78 6,150 82 2,771 79 103 100 7 76 310 74 2,852 76 77 84 3,070 87 963 80 705 100 9 83 236 85 785 82 353 79 2,124 84 71 79 46 89 17 84 237 78 1,669 82 46 78 3,786 78 303 72 393 100 23 72 318 80 2,698 65 32 85 3,189 95 203 81 298 100 28 84 344 85 2,211 81 55 79 4,926 81 849 69 115 96 24 78 381 78 3,482 83 35 80 1,900 79 286 71 41 83 10 76 88 81 1,444 77 24 79 2,169 81 391 64 16 100 9 81 89 79 1,646 73 11 77 5,372 86 255 69 59 96 45 77 291 76 4,611 85 52 86 1,320 85 294 87 253 94 16 86 146 86 527 83 67 77 2,672 89 66 83 74 100 4 75 194 76 2,300 78 14 80 3,387 84 72 81 13 93 13 79 139 80 3,128 63 12 78 3,111 85 51 58 11 88 15 80 85 78 2,919 81 17 96 27 100 11 100 2 N/A* N/A* 100 4 100 9 50 1 76 4,435 80 12 33 1 89 8 74 106 76 4,240 86 19 80 4,564 88 30 89 8 89 16 88 88 79 4,397 79 15 77 3,400 81 774 77 411 90 19 75 220 76 1,901 68 48 79 3,687 90 655 76 1,004 94 30 74 543 80 1,360 80 55 78 4,076 80 35 56 5 88 7 82 63 78 3,905 79 15 83 1,056 90 28 86 6 100 1 73 22 82 981 82 9 76 2,515 83 518 75 97 100 10 77 191 74 1,619 76 56 79 6,918 91 96 75 18 94 31 82 215 79 6,483 91 20 77 6,113 94 58 91 10 96 22 79 153 77 5,788 74 39 78 2,911 84 92 84 46 86 18 73 80 77 2,647 77 17 76 2,963 89 24 67 8 100 12 76 93 76 2,810 67 2 74 2,861 79 323 64 60 71 10 67 174 74 2,229 66 54 85 3,671 87 1,094 78 538 100 18 83 299 86 1,127 84 558 79 2,877 94 16 71 5 50 1 81 63 79 2,757 89 16 75 4,144 82 94 83 34 93 13 77 246 75 3,696 53 17 81 3,753 91 351 79 917 97 28 84 405 79 1,785 86 213 80 13,248 94 424 85 443 100 54 83 750 79 11,330 86 113 82 1,716 83 38 89 33 100 9 84 32 81 1,567 79 26 78 5,345 86 176 70 78 94 16 79 267 78 4,734 73 16 85 2,855 92 259 87 1,064 92 44 86 294 80 1,115 88 71 78 2,026 81 252 75 705 100 11 78 208 79 711 83 127 75 1,208 86 6 100 1 N/A* N/A* 73 24 75 1,164 20 1 83 1,102 86 69 78 289 100 3 77 142 89 451 85 127 80 11,917 88 422 87 161 91 48 85 495 80 10,663 83 80 80 2,444 87 163 75 601 93 28 80 274 82 1,227 72 135 86 2,528 90 1,257 79 223 100 8 77 197 86 708 83 101 82 956 79 19 100 3 100 9 87 13 81 907 40 2 84 2,543 92 225 85 309 89 8 84 176 83 1,779 85 17 81 1,515 90 9 86 6 100 3 83 50 80 1,435 86 6 80 11,398 86 1,051 76 498 91 62 79 802 79 8,775 81 137 83 3,234 89 963 78 345 100 6 82 381 80 1,220 84 272 83 2,766 90 972 79 312 94 17 81 236 79 875 86 321 74 952 70 7 67 2 100 5 78 39 74 893 100 1 47 9 N/A* N/A* N/A* N/A* N/A* N/A* N/A* N/A* 44 8 N/A* N/A* 80 1,532 84 127 76 370 100 10 79 240 82 686 85 83 87 829 88 37 85 156 100 3 89 167 88 304 88 144 79 14,093 91 663 85 449 96 49 80 787 78 11,895 86 136 80 2,567 98 52 78 51 100 5 81 95 80 2,295 85 63 84 1,590 90 299 81 29 100 30 84 179 81 919 89 116 78 4,339 78 1,182 72 74 94 15 78 288 78 2,721 70 35 78 1,411 89 34 92 11 50 1 67 26 78 1,326 67 6 84 2,539 93 146 81 1,086 94 16 84 353 85 785 88 134 78 11,051 78 1,246 77 44 95 39 78 375 79 9,250 80 51 77 7,166 77 858 72 552 90 47 74 467 78 5,062 74 136 80 3,804 86 1,741 81 387 100 17 76 253 74 1,280 84 90 81 6,173 89 510 82 75 96 27 82 310 80 5,187 83 25 81 2,818 96 196 78 1,065 94 44 80 446 83 933 83 93 75 6,201 91 79 79 33 84 21 80 200 74 5,784 83 30 77 3,996 84 198 78 193 95 75 78 231 76 3,132 75 158 76 2,482 76 1,166 75 259 100 10 80 223 75 761 83 43 78 5,239 82 1,257 79 952 92 59 77 438 75 2,116 77 381 77 2,608 90 167 83 202 86 6 81 183 75 2,003 82 28 83 1,912 91 449 73 252 93 14 76 174 83 944 92 66 79 1,450 82 162 83 15 N/A* N/A* 71 80 79 1,148 83 39 81 3,031 91 446 79 423 88 23 81 253 79 1,794 93 28 81 2,393 88 358 81 153 97 35 82 111 79 1,659 81 65 87 4,139 90 1,938 85 949 100 20 83 259 84 700 86 243 75 6,878 90 128 74 61 95 36 76 244 74 6,288 80 63 81 1,551 94 17 71 5 100 1 90 52 81 1,448 90 19 81 1,589 87 66 83 10 100 3 71 24 81 1,464 79 11 79 1,902 94 31 83 10 100 8 80 74 79 1,769 100 4 80 1,952 86 50 63 12 94 16 87 66 79 1,794 88 7 77 4,861 86 72 55 17 100 10 75 129 77 4,579 68 17 75 7,131 83 370 78 412 97 32 71 368 75 5,851 69 48 79 2,826 89 51 40 2 100 7 83 55 79 2,692 83 10 79 2,820 85 496 80 353 92 11 79 386 76 1,473 83 87 79 7,344 86 247 75 83 92 24 80 335 79 6,551 76 45 78 2,731 79 923 82 106 92 12 79 141 78 1,519 59 16 79 5,864 79 368 74 155 100 20 75 384 79 4,787 73 73 73 2,186 77 438 73 99 100 8 72 137 71 1,444 83 30 78 2,297 93 25 88 21 100 10 76 68 78 2,132 65 26 75 1,657 83 99 71 54 89 8 92 110 73 1,335 74 32 78 1,478 87 331 74 141 100 11 75 163 76 783 96 23 85 3,453 89 1,934 75 352 100 21 82 251 81 803 83 45 85 1,384 80 8 80 4 100 1 92 46 84 1,311 71 5 91 2,096 93 194 71 41 88 21 86 196 92 1,568 89 48 76 2,280 75 592 73 92 100 12 73 84 77 1,464 73 27 75 2,531 67 176 79 52 92 11 63 69 76 2,188 86 24 80 318 100 5 67 4 N/A* N/A* 92 11 81 294 50 2 79 2,368 87 89 84 144 100 1 80 193 79 1,847 76 75 78 3,701 85 1,189 72 352 92 22 74 326 75 1,701 83 95 79 2,321 93 28 71 10 100 7 82 63 79 2,196 62 8 78 4,908 81 636 76 290 96 46 77 372 77 3,355 82 180 79 2,413 88 21 71 5 100 4 80 74 79 2,292 100 4 83 1,951 91 1,029 79 167 100 3 76 161 72 494 92 85 83 2,174 89 306 91 52 97 29 77 171 81 1,560 91 29 75 4,389 86 69 94 15 92 12 78 138 74 4,115 50 9 79 2,619 88 92 78 35 91 10 85 145 79 2,306 73 19 80 1,335 82 47 90 9 100 3 81 35 80 1,228 100 9 78 2,185 80 297 77 69 100 16 80 141 77 1,613 85 44 80 1,766 92 130 83 53 100 23 77 113 79 1,414 81 13 81 2,683 84 151 79 1,177 97 29 80 301 83 767 83 210 78 1,724 95 19 75 9 80 8 74 32 78 1,648 75 6 79 7,725 81 361 66 51 95 35 73 257 79 6,943 78 36 80 2,750 87 218 73 33 91 20 70 127 80 2,313 78 25 83 2,069 85 116 85 23 100 7 87 58 82 1,850 86 6 75 2,495 77 387 77 100 100 12 79 126 74 1,829 68 38 77 6,104 88 128 70 60 75 3 77 359 77 5,419 78 58 80 2,595 88 116 86 24 85 11 85 47 79 2,388 67 2 83 10,255 87 758 80 185 97 68 82 643 82 8,392 86 114 85 2,031 94 397 84 141 100 41 86 202 82 1,197 85 29 77 2,238 83 285 81 74 93 14 75 135 76 1,692 67 16 79 2,335 85 264 82 69 100 13 79 111 78 1,844 71 17 79 1,806 82 122 78 68 100 13 84 98 78 1,491 67 4 84 2,020 92 124 89 354 94 15 89 127 81 1,354 88 23 79 1,142 96 24 100 1 75 3 76 32 79 1,068 64 7 85 2,837 87 1,894 83 277 100 11 80 171 81 371 91 99 87 2,621 92 410 80 109 98 61 87 201 86 1,740 91 74 78 3,137 77 127 80 45 95 21 73 90 78 2,834 65 17 77 2,916 82 775 74 145 93 13 72 169 76 1,767 65 33 84 2,791 88 762 79 507 97 28 85 341 84 1,033 80 104 83 1,981 87 385 75 417 100 10 77 230 86 841 85 76 85 2,209 92 81 83 19 100 6 92 70 84 2,009 60 12 78 4,992 88 337 77 61 95 21 81 256 77 4,254 87 26 77 1,492 83 45 74 17 100 6 77 80 76 1,324 70 7 77 7,077 83 359 75 94 82 18 76 349 76 6,149 81 42 84 1,197 81 155 82 210 100 12 84 125 85 317 85 366 80 3,063 79 34 75 43 83 5 86 84 80 2,864 76 29 76 4,103 94 62 67 60 94 15 84 158 76 3,741 77 37 79 1,257 80 203 45 10 100 6 78 107 80 898 71 15 75 2,821 89 80 78 14 95 19 79 77 74 2,608 78 7 84 1,803 93 286 94 58 100 14 86 143 82 1,269 83 15 76 2,562 83 650 77 304 88 7 71 280 74 1,275 74 35 77 4,733 80 202 83 24 100 10 79 190 77 4,252 79 19 82 1,624 93 38 86 6 100 4 79 48 82 1,506 100 11

### Summary of Maths results for 10 to 11 year olds By ethnicity and area Summary

The data shows that:

• out of all local authorities, White pupils were most likely to meet the expected standard in Richmond upon Thames (92%) and least likely to in Peterborough (71%) – this doesn't include City of London and Isles of Scilly which only have one school each, and whose results should be treated with caution
• figures for other ethnic groups are based on small numbers of pupils, so reliable generalisations can't be made

## 6. Data sources

### Type of statistic

National Statistics

### Publisher

Department for Education

Yearly

### Purpose of data source

The main purpose is to measure schools' and pupils' progress and performance from key stage 1 to key stage 2, in order to monitor and improve standards and inform parental choice when applying to local schools.

Maths results for 10 to 11 year olds 2018 to 2019 National - Spreadsheet (csv) 126 KB

This file contains the following variables: Measure, Ethnicity, Ethnicity_type, Time, Time_type, Geography, Geography_type, Geography_code, Gender, Age, FSM, Value, Value_type, Denominator, Numerator

Maths results for 10 to 11 year olds 2018 to 2019 Progress - Spreadsheet (csv) 48 KB

This file contains the following variables: Measure, Ethnicity, Ethnicity_type, Time, Time_type, Geography, Geography_type, Geography_code, Gender, Age, FSM, Value, Value_type, Denominator, Numerator, Lower_95_CI, Upper_95_CI

Maths results for 10 to 11 year olds 2018 to 2019 LA - Spreadsheet (csv) 744 KB

This file contains the following variables: Measure, Ethnicity, Ethnicity_type, Time, Time_type, Geography, Geography_type, Geography_code, Gender, Age, Value, Value_type, Denominator, Numerator