Python Analytics

Nafs Results Analytics — Aseer Region

A Python pipeline over official Nafs results for Aseer schools (1444–1447 AH): it checks the data against 15 quality rules, reconciles its results to the official department averages, derives the 1447 tested counts mathematically from the proficiency shares, then builds trends, gaps and school segments that feed this dashboard.

1Nafs data107,725 rows
2DQ rulesDQ-01…15
3Reconciliation48/48 match
4DashboardJSON → Chart.js

Filters Year Grade Subject Gender Authority

Proficiency trend vs national

Official national benchmark (published to 1446 AH), weighted by the selected grade/subject/gender mix

Key findings

Generated by the pipeline

Aseer proficiency rose from 31.5% (1444 AH) to 37.5% (1446 AH), +6.0 pts — reconciled to the official department averages.
1447 AH proficiency is 32.54% (-4.97 pts vs 1446). The largest drop is Grade 9 Reading (-14.9 pts) and the largest gain Grade 6 Mathematics (+8.2).
Of the -4.97 pt change, -3.57 is real performance change within grade and subject and -1.40 is the shift in who was tested (grade 3 testers -3.8%, grade 6 -16.6% in the same schools). At the 1446 mix, 1447 would be 33.94%.
The gap to the national average is -3.1 pts in 1446 AH vs -3.1 in 1444 AH: the region improved at the national pace without closing the gap.
In 1447 AH the highest proficiency is Grade 6 Science (56.1%) and the lowest Grade 9 Mathematics (17.5%); the weakest sub-domain is Reading comprehension in Grade 3 Reading (21.0%).
In 1447 AH the top governorate is Al-Namas (43.6%) and the lowest Al-Birk (22.2%); most improved since 1446 is Tarib (+5.5).
Boys and girls converge at student level in 1447 AH (32.0% vs 32.9%) after girls led in 1444; at school level the difference is +0.3 pts (not statistically significant).
224 schools flagged for priority intervention: ≥5 pts below the regional average and declining ≥5 pts more than the region (of 1,831 classified).

Proficiency by subject & grade

Performance-level distribution

Share of students per level · proficient = high + medium

Governorate ranking

Dashed line: regional average · governorates with fewer than 3 schools in the selection are hidden

Gender & authority

Sub-domain diagnosis

For the selected year, grade and subjectRegion level · red mark = whole-domain proficiency

School segmentation: level vs change

Each dot is a school · 1447 AH vs 1446 · change measured relative to the region (-5.0) · min. 15 students

Priority schools for intervention

≥5 pts below average, declining ≥5 pts more than the region · ≥25 students · school names withheld in the public version

School	Governorate	1446	1447	Δ
`P-01` · Girls	Rijal Alma	88.7%	15.0%	-73.7
`P-02` · Boys	Bisha	84.4%	23.9%	-60.5
`P-03` · Girls	Muhayil	45.7%	6.8%	-38.8
`P-04` · Girls	Ahad Rufaidah	65.0%	17.5%	-47.5
`P-05` · Girls	Rijal Alma	65.0%	19.8%	-45.2
`P-06` · Girls	Rijal Alma	27.7%	1.5%	-26.2
`P-07` · Girls	Rijal Alma	36.3%	6.7%	-29.6
`P-08` · Girls	Tathlith	52.0%	14.9%	-37.1
`P-09` · Girls	Rijal Alma	62.5%	20.6%	-41.9
`P-10` · Girls	Abha	47.1%	13.5%	-33.5
`P-11` · Girls	Abha	44.7%	12.5%	-32.2
`P-12` · Girls	Bisha	73.3%	26.9%	-46.4

Total flagged: 224 · The named list is kept internally and not published

Data quality before analysis

15 rules and reconciliation to official figures

Before any calculation the Nafs data is checked against rules mapped to DAMA dimensions. The key check is reconciliation: the proficiency the pipeline computes from student counts matches the official department average for every grade, subject and gender.

107,725

Rows checked

2,308

Schools

48/48

Official matches

Rules passed

Remediated

Open with owner

Reconciliation to the official department average

Year	Grade	Subject	Boys: calc / official	Girls: calc / official
1446	Grade 3	Mathematics	50.7 / 50.7 ✓	23.5 / 23.5 ✓
1446	Grade 3	Reading	41.3 / 41.3 ✓	26.3 / 26.3 ✓
1446	Grade 6	Mathematics	38.1 / 38.1 ✓	38.3 / 38.3 ✓
1446	Grade 6	Science	53.2 / 53.2 ✓	61.6 / 61.6 ✓
1446	Grade 6	Reading	39.4 / 39.4 ✓	50.1 / 50.1 ✓
1446	Grade 9	Mathematics	23.7 / 23.7 ✓	24.5 / 24.5 ✓
1446	Grade 9	Science	39.2 / 39.1 ✓	47.0 / 47.0 ✓
1446	Grade 9	Reading	31.7 / 31.7 ✓	45.5 / 45.5 ✓

1446 AH shown; 1444 and 1445 also match (difference 0.0 in every case).

Rule	Dimension	Check	Checked	Failed	Status	Action
`DQ-01`	Completeness	Ministerial school number present	107,725	0	Pass	Quarantine & return to source
`DQ-02`	Uniqueness	One row per year/school/grade/sub-domain	107,725	0	Pass	Remove duplicates after review
`DQ-03`	Validity	Tested ≤ enrolled and proficient ≤ tested	77,076	0	Pass	Verify against source file
`DQ-04`	Accuracy	Published rate equals proficient ÷ tested	77,076	0	Pass	Recompute from counts
`DQ-05`	Consistency	Four performance levels sum to 100%	77,076	0	Pass	Review level distribution
`DQ-06`	Reconciliation	Computed proficiency matches the official department average (per grade, subject, gender)	48	0	Pass	Investigate with data owner
`DQ-07`	Conformity	Proficiency on a common 0–100 scale	30,649	30,649	Remediated	Convert 1447 export from 0–1 fraction to %
`DQ-08`	Comparability	Average-score scale stable across years	4	1	Remediated	Do not compare scores across years (≈500 in 1444–46 vs 0–100 in 1447); use proficiency
`DQ-09`	Completeness	Education office present on every row	77,076	30,205	Open	Use governorate reference data; raise with data owner
`DQ-10`	Consistency	Office label matches reference list	46,871	1,328	Remediated	Standardise 7 short labels (e.g. 'Bariq' → 'Bariq Education Office')
`DQ-11`	Master data	One name per ministerial number across years	2,308	313	Remediated	Adopt latest-year name as golden record
`DQ-12`	Reference data	Governorate known for every school	2,308	145	Open	Join on ministerial number; unmatched shown as 'Unmapped'
`DQ-13`	Completeness	Tested / proficient counts available	30,649	30,649	Remediated	Tested count rebuilt per school-grade from the denominators of its proficiency shares (LCM) — 2,962 school-grades, 1 without an informative share
`DQ-14`	Reconciliation	Consolidated 1447 file matches the earlier exports row by row	14,671	0	Pass	Adopt the consolidated file as the primary source
`DQ-15`	Consistency	One tested count per school-grade across all subjects and sub-domains (1444–1446)	7,378	0	Pass	Basis for the 1447 count reconstruction

Method & statistics

Behind the numbers

Change 1446–1447 AH-4.97 pts performance -3.57 · tested mix -1.40

Private & intl. vs public+4.1 pts Welch t = 2.57 · p = 0.012 · n = 88 / 1,743

Girls vs boys schools+0.3 pts Welch t = 0.38 · p = 0.705 — not significant

School size vs proficiencyρ = -0.042 Spearman · p = 0.071 — no material relationship

Accuracy of derived 1447 AH counts (back-test)99.8% exact Tested counts 1445 & 1446 · regional rate error < 0.01

Pipeline excerpt

nafs_pipeline.py

# DQ-06 reconciliation: computed proficiency vs the official department average
for (year, grade, subject, gender), x in main.groupby([...]):
    calc = 100 * x["proficient"].sum() / x["tested"].sum()
    diff = calc - x["ref_dept"].mode()[0]          # 48 / 48 → 0.0

# 1447 has no counts: every result of a school-grade shares one tested count,
# so the count is the LCM of the reduced denominators of its 7–12 shares
q = Fraction(share).limit_denominator(5000).denominator
n[school, grade] = math.lcm(*q_of_all_results)    # 99.8% exact on 1446

# Priority = below average AND declining more than the region
wide["priority"] = (rate_1447 <= region - 5) \
                 & (change - region_change <= -5) & (n >= 25)

DataAseer school-level Nafs report 1444–1446 AH and the consolidated 1447 AH Nafs results file (all schools and sub-domains): 77,076 + 30,649 rows for 2,308 schools.
MeasureProficiency = proficient ÷ tested, where proficient means the medium or high level. Average scores are not compared across years because their scale changed in 1447 AH.
1447 AH countsThe published proficiency is an exact fraction (proficient ÷ tested). Because all results of a school-grade share one tested count, that count is the LCM of the denominators of its shares. Back-test: 99.79% exact in 1446 and 99.91% in 1445.
PrivacyThe public version contains no school names or ministerial numbers, and any slice with fewer than 3 schools is suppressed.

View notebook nafs_pipeline.py

Static Python outputs for reports

Source: Aseer school-level Nafs report 1444–1446 AH and the consolidated 1447 AH Nafs results file (all schools and sub-domains). 1444–1446 AH figures are actual and reconciled to official averages; 1447 AH figures are computed from school shares with mathematically derived tested counts (99.8% exact in back-testing).