Four numbers a head teacher can act on

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What this lesson covers

• Count the denominators first
• The district report
• The four numbers for a head teacher
• The distinction the whole course was for
• Module 4, in one paragraph
• What comes next

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Count the denominators first

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Count the denominators first

• Six different denominators and none of them is "students" — Two are projected populations, two are marks, three are…

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Count the denominators first — In Python

import pandas as pd

denominators = pd.DataFrame([
    ("gross enrolment", "children aged 6-11, projected", 963),
    ("attendance, ADA", "marks made", 69974),
    ("attendance, regular", "students with 20+ marks", 1200),
    ("dropout", "2023 students, transfers excluded", 1255),
    ("learning change", "students who sat both rounds", 585),
], columns=["indicator", "denominator", "n"])
print(denominators)

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Count the denominators first — In R

# Build it in code, print it above the results, every time.

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The district report — Example (cont.)

Education, four districts, school year 2023-24

ENROLMENT                                   denominator: 963 projected 6-11
  Gross enrolment ratio           109.2%    1,052 primary enrolees
  Net enrolment ratio              97.4%      938 aged 6-11
  Over-age for grade               36.4%    grade 1 17.0%, grade 6 50.8%

  Denominator is a 2015 census projected at 2.1% a year; about a sixth of it
  is an assumption. 12 student-years were recorded twice and deduplicated.

ATTENDANCE                                  February to April 2024
  Average daily attendance         88.4%    69,974 marks
  Students attending 90%+ of days  62.1%    1,200 students with 20+ marks
  Students below 75%                9.5%    114 students

  SCH07 and SCH18 were closed 11-29 March, fifteen school days. Their rates

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The district report — Example (cont.)

  are computed on the 45 days they opened. Counting the closure as absence
  would report them at 66.1% and 63.9% and rank them worst.

RETENTION                                   2023 cohort, transfers excluded
  Promotion (incl. completion)     78.4%    n=1,255
  Repetition                       10.1%
  Dropout                          10.5%
  One-year transition observed     88.5%    n=1,103 in both years

  Dropout is 19.5% among over-age students against 4.8% in-age.
  Centre reports repetition at 7.4% against 12.0-12.3% elsewhere while
  holding the highest over-age share; treat as a recording difference.

LEARNING                                    literacy
  Below minimum band        19.3% -> 11.1%
  Advanced band             11.9% -> 26.1%

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The district report — Example (cont.)

  Items correct, panel      56.8% -> 63.7%   +6.9 points, n=585
  Items correct, all comers 53.1% -> 61.8%   +8.7 points

  Instruments were 40 and 50 items; raw scores are not comparable and are not
  reported. 156 baseline students did not sit the endline and scored 39.3%
  against 56.8% for those who did.

NOT REPORTED
  Survival to the final grade. A reconstructed cohort gives 19.3% on
  promotion and 40.3% on retention; neither is a completion rate. A true
  cohort needs six years of the register or a household survey.
  Safely managed anything. This is an enrolment register, not a survey of
  children out of school — every denominator above counts children the
  system already has.

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The four numbers for a head teacher

• Four numbers, each attached to something a school can change

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The four numbers for a head teacher

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The four numbers for a head teacher — In Python

follow_up = (attendance_rates < 0.75).sum()
print(f"students to visit this term: {follow_up}")

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The four numbers for a head teacher — In R

# One number, one list, one action.

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The four numbers for a head teacher

• Notice what is not on that list — The gross enrolment ratio is a district planning number and a head teacher cannot…
• Match the indicator to the decision-maker's actual span of control — which is the same rule the WASH course applied to…

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The distinction the whole course was for — In Python

funnel = pd.DataFrame([
    ("enrolled in primary", 1052),
    ("attending 90%+ of days", int(1200 * 0.621)),
    ("proficient or advanced in literacy", int(658 * 0.65)),
], columns=["stage", "students"])
print(funnel)

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The distinction the whole course was for — In R

# Three stages, three denominators. Do not draw them as one funnel without
# saying so.

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The distinction the whole course was for

• Those three cannot be chained into a funnel — because they are measured on different denominators over different…

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Module 4, in one paragraph

• What did I count, over what? — Person-time in epidemiology, visits in WASH, consenting cases in protection, marks in…
• What else could explain it? — Age structure in a cholera outbreak, a closed road in a water district, a service opening…
• What should someone do differently? — The question that decides whether the first two were worth asking, and the one…

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What comes next

• Module 4 is complete.

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Where this goes next

Read the full lesson, with runnable code Back to the lesson