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Learning Gain Pretest Ceiling Gap Tool

Calculate learning gain pretest ceiling gap from explicit caller-supplied inputs.

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Description

rawGain = posttest-pretest; availableGain = ceiling-pretest; normalizedGain = rawGain/availableGain.

A change score can be expressed as raw difference, retention ratio or fraction of remaining headroom. These denominators answer different questions, and descriptive change does not by itself establish a causal learning effect.

rawGain = posttest-pretest; availableGain = ceiling-pretest; normalizedGain = rawGain/availableGain.

When to use this tool

Use Learning Gain Pretest Ceiling Gap when checking a specified retention or ceiling-relative gain convention.

How to use the tool and how it works

  1. Enter pretest, posttest, ceiling, floor. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read rawGain, availableGain, normalizedGain. Use the output convention below.

Require ceiling > floor, pretest in [floor,ceiling) and posttest in [floor,ceiling]. rawGain = posttest-pretest; availableGain = ceiling-pretest; normalizedGain = rawGain/availableGain.1

Input fieldMeaning and unitsAccepted input
pretestPretestRequired. Finite number.
posttestPosttestRequired. Finite number.
ceilingCeilingRequired. Finite number.
floorFloorRequired. Finite number.
Output fieldInterpretationReturned shape
rawGainRaw GainRequired. Finite number.
availableGainAvailable GainRequired. Finite number.
normalizedGainNormalized GainRequired. Finite number.
json
{
  "input": {
    "pretest": 40,
    "posttest": 70,
    "ceiling": 100,
    "floor": 0
  },
  "output": {
    "rawGain": 30,
    "availableGain": 60,
    "normalizedGain": 0.5
  }
}

Limitations and assumptions

  • Negative normalized gains are allowed. This descriptive ceiling-relative change does not correct measurement error, regression to the mean or establish an instructional effect.
  • Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
  • Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.

Alternative or Complementary methods

Use an appropriate study design and measurement-error analysis when assessing instructional effectiveness or individual change.

References

  1. Calculation and input contract: educational-psychology/learning-gain-pretest-ceiling-gap — tool.io

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