Random Fair Classroom Calling Sequence Generator

Description

Choose only among supplied least-called learners, randomize ties, increment counts after each turn, and retain eligible IDs and before/after spreads. Prior counts and roster order are preserved; balance is a count-based rule rather than a claim about equitable educational outcomes.

A workflow or scheduling fixture makes ordering, dependencies, capacity, and timing explicit. Returning both the constructed records and their checks helps consumers distinguish a valid arrangement from a simulated failure case.

When to use this tool

Use this generator to create reproducible fair classroom calling sequence cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply studentIds, priorCallCounts, turns in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read trace, finalCallCounts. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

At each turn, use the existing roster calculator to find the current minimum call count, randomly choose among every student at that minimum, and increment only that student's count. Return eligible IDs and before/after count spreads.^[1]

Delegated operations used for this result: cold call equity roster. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
studentIds	Student Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 128.
priorCallCounts	Prior Call Counts	Required. Array of Safe integer; minimum 0; maximum 9007199254740991.; minimum items 1; maximum items 128.
turns	Turns	Required. Safe integer; minimum 1; maximum 512.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
trace	Trace	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 512.
trace[].turn	Turn	Required. Safe integer; minimum 1; maximum 512.
trace[].minimumBefore	Minimum Before	Required. Safe integer; minimum 0; maximum 9007199254740991.
trace[].eligibleIds	Eligible Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 128.
trace[].chosenId	Chosen Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
trace[].countBefore	Count Before	Required. Safe integer; minimum 0; maximum 9007199254740991.
trace[].countAfter	Count After	Required. Safe integer; minimum 0; maximum 9007199254740991.
trace[].spreadBefore	Spread Before	Required. Safe integer; minimum 0; maximum 9007199254740991.
trace[].spreadAfter	Spread After	Required. Safe integer; minimum 0; maximum 9007199254740991.
finalCallCounts	Final Call Counts	Required. Array of Safe integer; minimum 0; maximum 9007199254740991.; minimum items 1; maximum items 128.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "studentIds": [
      "A",
      "B",
      "C"
    ],
    "priorCallCounts": [
      2,
      0,
      1
    ],
    "turns": 6,
    "seed": 42
  },
  "output": {
    "trace": [
      {
        "turn": 1,
        "minimumBefore": 0,
        "eligibleIds": [
          "B"
        ],
        "chosenId": "B",
        "countBefore": 0,
        "countAfter": 1,
        "spreadBefore": 2,
        "spreadAfter": 1
      },
      {
        "turn": 2,
        "minimumBefore": 1,
        "eligibleIds": [
          "B",
          "C"
        ],
        "chosenId": "B",
        "countBefore": 1,
        "countAfter": 2,
        "spreadBefore": 1,
        "spreadAfter": 1
      },
      {
        "turn": 3,
        "minimumBefore": 1,
        "eligibleIds": [
          "C"
        ],
        "chosenId": "C",
        "countBefore": 1,
        "countAfter": 2,
        "spreadBefore": 1,
        "spreadAfter": 0
      },
      {
        "turn": 4,
        "minimumBefore": 2,
        "eligibleIds": [
          "A",
          "B",
          "C"
        ],
        "chosenId": "C",
        "countBefore": 2,
        "countAfter": 3,
        "spreadBefore": 0,
        "spreadAfter": 1
      },
      {
        "turn": 5,
        "minimumBefore": 2,
        "eligibleIds": [
          "A",
          "B"
        ],
        "chosenId": "A",
        "countBefore": 2,
        "countAfter": 3,
        "spreadBefore": 1,
        "spreadAfter": 1
      },
      {
        "turn": 6,
        "minimumBefore": 2,
        "eligibleIds": [
          "B"
        ],
        "chosenId": "B",
        "countBefore": 2,
        "countAfter": 3,
        "spreadBefore": 1,
        "spreadAfter": 0
      }
    ],
    "finalCallCounts": [
      3,
      3,
      3
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

This balances the supplied historical counts, not all aspects of classroom fairness. IDs and count arrays must align and increments remain safe integers; turns can repeat students as minimum counts change.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Fair Classroom Calling Sequence Generator — tool.io
Calculation or validation: pedagogy/cold-call-equity-roster — tool.io

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