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school_compare/nextjs-app/components/compare/CompareAcademics.tsx
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fix(compare): per-school P8 explanation; disadvantaged cohort from the same yearly row
Review findings: (1) the 'no KS2 baseline (COVID)' explanation was derived
from the basket-wide max year, so a school with an unrelated data gap
borrowed it from a neighbour with 2024/25 data — now judged per school on
its own latest year. (2) The '~N disadvantaged pupils' cohort multiplied
eligible_pupils and disadvantaged_pct resolved independently across years
— now both come from the same yearly row that supplies the displayed
percentage.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
2026-07-17 17:53:10 +01:00

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/**
* How children do academically — tier-1 dot strips anchored on official
* England averages, tier-2 "More measures" one tap away, equity row against
* the computed state-school benchmark. Copy verbatim from the reviewed
* mockups; teacher-assessed measures are labelled as such.
*/
'use client';
import { latestValues, verdict } from '@/lib/compareLogic';
import type { Benchmarks, ComparisonData, NationalAverages, School } from '@/lib/types';
import { DotStrip } from '@/components/DotStrip';
import { Cell, Chip, RowLabel, Section, SectionGrid, sectionStyles as s } from './sectionShared';
import styles from './CompareAcademics.module.css';
interface StripSpec {
label: string;
metric: string;
anchorKey?: string;
tip?: string;
min?: number;
max?: number;
unit?: string;
}
const TIER1_PRIMARY: StripSpec[] = [
{
label: 'Reading, writing & maths — expected standard',
metric: 'rwm_expected_pct',
anchorKey: 'rwm_expected_pct',
tip: '% of Year 6 pupils reaching the expected standard in reading, writing and maths.',
},
{ label: 'Reading', metric: 'reading_expected_pct', anchorKey: 'reading_expected_pct' },
{
label: 'Writing (teacher-assessed)',
metric: 'writing_expected_pct',
anchorKey: 'writing_expected_pct',
tip: 'Writing is assessed by teachers, not tested.',
},
{ label: 'Maths', metric: 'maths_expected_pct', anchorKey: 'maths_expected_pct' },
{
label: 'Working at a higher standard than expected',
metric: 'rwm_high_pct',
anchorKey: 'rwm_high_pct',
tip: 'A high score in the reading and maths tests plus “greater depth” in teacher-assessed writing.',
},
];
const TIER2_PRIMARY: StripSpec[] = [
{
label: 'Grammar, punctuation & spelling — expected standard',
metric: 'gps_expected_pct',
anchorKey: 'gps_expected_pct',
},
{
label: 'Science — expected standard (teacher-assessed)',
metric: 'science_expected_pct',
anchorKey: 'science_expected_pct',
tip: 'Teacher-assessed, like writing — there has been no KS2 science test since 2009, so comparisons are indicative.',
},
{
label: 'Average scaled score — reading',
metric: 'reading_avg_score',
anchorKey: 'reading_avg_score',
min: 100,
max: 120,
unit: '',
},
{
label: 'Average scaled score — maths',
metric: 'maths_avg_score',
anchorKey: 'maths_avg_score',
min: 100,
max: 120,
unit: '',
},
{
label: 'Average scaled score — grammar, punctuation & spelling',
metric: 'gps_avg_score',
anchorKey: 'gps_avg_score',
min: 100,
max: 120,
unit: '',
},
];
function Strip({
spec,
data,
urns,
schoolNames,
national,
}: {
spec: StripSpec;
data: Record<string, ComparisonData>;
urns: number[];
schoolNames: string[];
national: Record<string, number> | undefined;
}) {
const values = latestValues(data, urns, spec.metric).map((v) =>
v != null ? Math.round(v) : null,
);
const anchorValue = spec.anchorKey ? national?.[spec.anchorKey] : undefined;
const anchor =
anchorValue != null
? { value: anchorValue, label: `England ${Math.round(anchorValue)}${spec.unit ?? '%'}` }
: null;
if (values.every((v) => v == null)) return null;
return (
<DotStrip
label={spec.label}
values={values}
schoolNames={schoolNames}
anchor={anchor}
min={spec.min ?? 0}
max={spec.max ?? 100}
unit={spec.unit ?? '%'}
tip={spec.tip}
/>
);
}
export function CompareAcademics({
schools,
data,
nationalAverages,
benchmarks,
isSecondary: propIsSecondary,
}: {
schools: School[];
data: Record<string, ComparisonData>;
nationalAverages?: NationalAverages;
benchmarks?: Benchmarks;
isSecondary?: boolean;
}) {
const urns = schools.map((school) => school.urn);
const schoolNames = schools.map((school) => school.school_name);
const isSecondary = propIsSecondary !== undefined ? propIsSecondary : schools.some(
(school) => data[String(school.urn)]?.school_info?.attainment_8_score != null,
);
if (isSecondary) {
const att8 = latestValues(data, urns, 'attainment_8_score');
const banding = urns.map((urn) => {
const rows = data[String(urn)]?.yearly_data ?? [];
for (let i = rows.length - 1; i >= 0; i--) {
if (rows[i].progress_8_banding) return rows[i].progress_8_banding as string;
}
return null;
});
// DfE stopped publishing Progress 8 from 2024/25: those GCSE year groups
// sat no KS2 tests (COVID), so there is no baseline to measure progress
// from. A bare "No data" reads as a gap on our side — say why. Judged
// PER SCHOOL on its own latest data year: a school whose data simply
// stops earlier (an unrelated gap) must not borrow the COVID explanation
// from a neighbour that does have 2024/25 data.
const p8NotPublished = urns.map((urn) => {
const rows = data[String(urn)]?.yearly_data ?? [];
const y = rows.length ? Math.trunc(rows[rows.length - 1].year) : 0;
return y >= 202425;
});
const grade5 = latestValues(data, urns, 'english_maths_strong_pass_pct');
const ebacc = latestValues(data, urns, 'ebacc_entry_pct');
const att8Anchor = nationalAverages?.secondary?.attainment_8_score;
return (
<Section
title="How students do academically"
how="GCSE results (latest year). Attainment 8 averages performance across eight subjects; Progress 8 shows how much progress students make compared with similar students nationally — the wording is DfE's own banding."
>
<SectionGrid schools={schools}>
<RowLabel tip="Average Attainment 8 score across eight GCSE subjects.">Attainment 8</RowLabel>
{schools.map((school, i) => (
<Cell key={school.urn} school={school} index={i}>
{att8[i] != null ? (
<>
<span className={s.big}>{(att8[i] as number).toFixed(1)}</span>
{att8Anchor != null && (
<span className={s.small}>England average {att8Anchor.toFixed(1)}</span>
)}
</>
) : (
<span className={s.small}>No data</span>
)}
</Cell>
))}
<RowLabel tip="DfE's own plain-English Progress 8 label.">Progress 8</RowLabel>
{schools.map((school, i) => (
<Cell key={school.urn} school={school} index={i}>
{banding[i] ? (
<Chip
tone={
/well above|above/i.test(banding[i] as string)
? 'good'
: /well below|below/i.test(banding[i] as string)
? 'warn'
: 'neutral'
}
>
{banding[i]}
</Chip>
) : p8NotPublished[i] ? (
<span className={s.small}>
Not published this GCSE year group sat no KS2 tests (COVID), so DfE has no
baseline to measure progress from
</span>
) : (
<span className={s.small}>No data</span>
)}
</Cell>
))}
<RowLabel tip="% achieving grade 5 or above in both English and maths GCSEs.">
Grade 5+ in English &amp; maths
</RowLabel>
{schools.map((school, i) => (
<Cell key={school.urn} school={school} index={i}>
{grade5[i] != null ? `${Math.round(grade5[i] as number)}%` : <span className={s.small}>No data</span>}
</Cell>
))}
<RowLabel tip="% entering the English Baccalaureate subject combination.">EBacc entry</RowLabel>
{schools.map((school, i) => (
<Cell key={school.urn} school={school} index={i}>
{ebacc[i] != null ? `${Math.round(ebacc[i] as number)}%` : <span className={s.small}>No data</span>}
</Cell>
))}
</SectionGrid>
</Section>
);
}
const national = nationalAverages?.primary;
const disadvantaged = latestValues(data, urns, 'rwm_expected_disadvantaged_pct');
const disadvantagedAnchor = benchmarks?.primary?.disadvantaged_rwm_expected_pct ?? null;
// Cohort size behind the disadvantaged figure (spec §8.5): these are small
// groups where single pupils move the percentage — show roughly how many
// pupils the figure rests on. Taken from the SAME yearly row that supplies
// the displayed percentage: resolving eligible_pupils and the
// disadvantaged share independently could mix years and misstate the
// cohort behind the figure.
const cohorts = urns.map((urn) => {
const rows = data[String(urn)]?.yearly_data ?? [];
for (let i = rows.length - 1; i >= 0; i--) {
const row = rows[i];
if (row.rwm_expected_disadvantaged_pct != null) {
if (row.eligible_pupils == null || row.disadvantaged_pct == null) return null;
const cohort = Math.round((row.eligible_pupils * row.disadvantaged_pct) / 100);
return cohort > 0 ? cohort : null;
}
}
return null;
});
return (
<Section
title="How children do academically"
how="Results from national tests and teacher assessments at the end of Year 6 — writing is assessed by teachers, not tested. Each line runs from 0100%; the grey tick marks the England average, so dots to its right are above average."
>
<div className={s.card}>
{TIER1_PRIMARY.map((spec) => (
<Strip
key={spec.metric}
spec={spec}
data={data}
urns={urns}
schoolNames={schoolNames}
national={national}
/>
))}
<details className={styles.moreMeasures}>
<summary>More measures grammar, punctuation &amp; spelling, science, average scaled scores</summary>
{TIER2_PRIMARY.map((spec) => (
<Strip
key={spec.metric}
spec={spec}
data={data}
urns={urns}
schoolNames={schoolNames}
national={national}
/>
))}
<p className={styles.stripNote}>
The scaled-score strips show the 100120 window of the full 80120 range; 100 is the
expected standard. Where an England tick is missing, the official figure isn&apos;t in
our dataset yet.
</p>
</details>
</div>
{disadvantaged.some((v) => v != null) && (
<SectionGrid schools={schools}>
<RowLabel tip="% of disadvantaged pupils (free school meals in the last 6 years, or looked after by the local authority) reaching the expected standard. Benchmark computed across state schools in our dataset. Based on smaller pupil groups, so a single pupil can move a school's figure noticeably.">
Children from lower-income families
</RowLabel>
{schools.map((school, i) => {
const value = disadvantaged[i];
return (
<Cell key={school.urn} school={school} index={i}>
{value != null ? (
<>
<span className={s.big} style={{ fontSize: '1.1rem' }}>
{Math.round(value)}%
</span>{' '}
{cohorts[i] != null && (
<span className={s.small}>of ~{cohorts[i]} disadvantaged pupils</span>
)}{' '}
{disadvantagedAnchor != null && (
<Chip tone={verdict(value, disadvantagedAnchor, 5) === 'below' ? 'warn' : 'good'}>
{verdict(value, disadvantagedAnchor, 5) === 'above' &&
`Well above the ${Math.round(disadvantagedAnchor)}% state-school average`}
{verdict(value, disadvantagedAnchor, 5) === 'close' &&
`Around the ${Math.round(disadvantagedAnchor)}% state-school average`}
{verdict(value, disadvantagedAnchor, 5) === 'below' &&
`Below the ${Math.round(disadvantagedAnchor)}% state-school average`}
</Chip>
)}
</>
) : (
<span className={s.small}>No data</span>
)}
</Cell>
);
})}
</SectionGrid>
)}
</Section>
);
}