Random Differentiated Activity Assignment Generator

Description

Delegate tier classification from the actual supplied readiness scores, then independently sample an activity ID only from each learner's matching bank. Intermediate and advanced cutoffs are inclusive lower bounds; scores are never randomized, activities may repeat, and tier labels do not establish instructional suitability.

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 differentiated activity assignment 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 students, intermediateCutoff, advancedCutoff, activities 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 assignments, tiers. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Delegate readiness-tier classification from supplied student scores and cutoffs, then draw an activity from each classified tier's bank. Return assignments in student order and the calculator's tier partitions.^[1]

Delegated operations used for this result: readiness tier assignments. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
students	Students	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
students[].id	Literal identifier used independently of display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
students[].readinessScore	Readiness Score	Required. Finite number.
intermediateCutoff	Intermediate Cutoff	Required. Finite number.
advancedCutoff	Advanced Cutoff	Required. Finite number.
activities	Activities	Required. Object; no unknown keys.
activities.foundation	Foundation	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; maximum items 64.
activities.intermediate	Intermediate	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; maximum items 64.
activities.advanced	Advanced	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; maximum items 64.
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
assignments	Assignments	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
assignments[].studentId	Student Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
assignments[].readinessScore	Readiness Score	Required. Finite number.
assignments[].tier	Tier	Required. Text; one of "foundation", "intermediate", "advanced".
assignments[].activityId	Activity Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
tiers	Tiers	Required. Object; no unknown keys.
tiers.foundationIds	Foundation Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; maximum items 128.
tiers.intermediateIds	Intermediate Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; maximum items 128.
tiers.advancedIds	Advanced Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; 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": {
    "students": [
      {
        "id": "A",
        "readinessScore": 20
      },
      {
        "id": "B",
        "readinessScore": 40
      },
      {
        "id": "C",
        "readinessScore": 80
      }
    ],
    "intermediateCutoff": 40,
    "advancedCutoff": 80,
    "activities": {
      "foundation": [
        "F1",
        "F2"
      ],
      "intermediate": [
        "I1",
        "I2"
      ],
      "advanced": [
        "A1",
        "A2"
      ]
    },
    "seed": 42
  },
  "output": {
    "assignments": [
      {
        "studentId": "A",
        "readinessScore": 20,
        "tier": "foundation",
        "activityId": "F2"
      },
      {
        "studentId": "B",
        "readinessScore": 40,
        "tier": "intermediate",
        "activityId": "I1"
      },
      {
        "studentId": "C",
        "readinessScore": 80,
        "tier": "advanced",
        "activityId": "A2"
      }
    ],
    "tiers": {
      "foundationIds": [
        "A"
      ],
      "intermediateIds": [
        "B"
      ],
      "advancedIds": [
        "C"
      ]
    },
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Every populated tier needs at least one activity. Scores are supplied rather than measured, and tier matching does not establish educational benefit or appropriate individualized assessment.
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 Differentiated Activity Assignment Generator — tool.io
Calculation or validation: pedagogy/readiness-tier-assignments — tool.io

Similar or alternative tools

Calendar Overlap and Common Gap Finder
Compare bounded supplied busy calendars over an explicit half-open epoch-millisecond window, deduplicating same-calendar overlap and delegating interval cells to report common gaps, overlaps and per-calendar occupied duration.
Random Assessment Item Sampler Generator
Sample distinct existing question records without replacement within each requested objective count, shuffle the combined paper, and delegate coverage auditing against a caller-selected minimum. Entire supplied prompts and answers are retained unchanged; extra bank objectives are ignored, shortages fail explicitly, and no assessment-validity or difficulty claim is made.