Curriculum Evaluation: Pre/Post Effect Size Simulator

Curriculum Design

Curriculum quality (gain potential) 18 pts

Implementation consistency 1.0×

Class size (students) 140

Passing threshold 60

Evaluation Results

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Mean pre-test

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Mean post-test

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Mean gain

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Cohen's d

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Pass rate (pre)

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Pass rate (post)

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How it works

Each of N students has a latent ability a ~ 𝒩(60, 15). The pre-test score is a noisy read of ability; the curriculum's effect is added before the post-test:

pre_i  = clamp(a_i + ε1_i · 8, 0, 100)
post_i = clamp(a_i + G·Q + ε2_i · 8·C, 0, 100)

G = curriculum quality (gain potential)
Q = 1  (full effect applied to every student)
C = implementation consistency scale
    (low C → tight, uniform delivery; high C → noisy rollout)

The evaluation compares the two score distributions with the standard effect-size statistic used in education research (What Works Clearinghouse, Hattie's visible-learning synthesis):

Cohen's d = (mean_post − mean_pre) / pooled_SD
pooled_SD = √[(SD_pre² + SD_post²) / 2]

d ≈ 0.2 small · 0.5 medium · 0.8 large effect

Curriculum quality — how much genuine learning gain the material can deliver under ideal conditions.
Implementation consistency — how reliably teachers actually deliver it; low consistency widens the post-test spread and can erase a real gain from the aggregate effect size even when the curriculum itself is strong.
Passing threshold — the cutoff score; the vertical marker on the histogram shows where it falls, and the pass-rate stats update live.
Blue bars (back row) are the pre-test distribution; green bars (front row) are the post-test distribution — the whole curriculum-evaluation question is whether the green row has shifted meaningfully to the right of the blue row.

Pre-test distribution Post-test distribution Passing threshold

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