Curve Grading Bell Adjustment Policy Lookup
Explicit caller affine population SD curvepolicy, unclipped adjusted scores and rank retained; not forced bell-shaped distribution or norm fairness. Caller independently verifies learner/context/evidence and reference edition; not causal learning efficacy, disability eligibility or educational certification.
Description
An affine score adjustment changes a score distribution’s mean and population standard deviation.
An affine score adjustment changes a score distribution’s mean and population standard deviation.1
It preserves order but does not make the distribution bell-shaped or establish a fair grading policy.
When to use Curve Grading Bell Adjustment Policy Lookup
Use this tool to calculate or review the supplied observations under the stated rules. Establish the learning task, observed evidence and applicable framework or policy edition.
How to use Curve Grading Bell Adjustment Policy Lookup and understand its results
- Establish the learning task, observed evidence and applicable framework or policy edition.
- Enter observedRawScores, callerTargetMean, callerTargetPopulationSd, using the field meanings and rules below, then run the tool.
- Read callerAffineAdjustedScores. Inspect nested result fields where present, and review unknown records separately from known matches.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedRawScores | Observed raw scores | list of number | Required |
| callerTargetMean | Caller target mean | number | Required |
| callerTargetPopulationSd | Caller target population sd | number | Required |
Provide nonempty finite scores with positive population standard deviation, a finite target mean and positive target standard deviation. Centre each score on the original mean, multiply by target/original SD, and add the target mean. Return only the unclipped adjusted scores; a constant score list fails.
adjusted_i=targetMean+(score_i−mean)×targetSD/populationSD Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| callerAffineAdjustedScores | Caller affine adjusted scores | list of number |
{
"input": {
"observedRawScores": [
50,
70,
90
],
"callerTargetMean": 80,
"callerTargetPopulationSd": 5
},
"output": {
"callerAffineAdjustedScores": [
73.87627564304205,
80,
86.12372435695795
]
}
}Limitations and assumptions
Supplied annotations and framework choices determine the result. The tool does not measure learning effects, assess learner intent or independently establish policy applicability.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above.
Alternative or Complementary methods
For a related step, use Criterion Norm Referenced Grading Distinction Lookup: Criterion-referenced interpretation compares achievement with defined criteria, while norm-referenced interpretation compares it with a reference group. For a complementary comparison, use Formative vs Summative Purpose Classifier: Formative and summative describe the purpose served by assessment evidence.
References
Similar or alternative tools
- Criterion Norm Referenced Grading Distinction Lookup
Criterion-referenced interpretation compares achievement with defined criteria, while norm-referenced interpretation compares it with a reference group.
- Formative vs Summative Purpose Classifier
Formative and summative describe the purpose served by assessment evidence.