Random Jigsaw Learning Assignment Generator

Description

Shuffle the supplied distinct learners and topics, delegate complete jigsaw home/expert grouping, and associate each expert group with its shuffled topic. There must be one topic per home-group member and complete home groups; comma-free learner IDs keep the core's serialized group format unambiguous.

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 jigsaw learning 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 studentIds, topicIds 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 studentOrder, topicOrder, homeGroups, expertGroups. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Shuffle students and topics independently, then delegate jigsaw allocation with home-group size equal to the topic count. Decode home groups and expert groups and attach the shuffled topic IDs to expert groups.^[1]

Delegated operations used for this result: jigsaw rotation schedule. 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.
topicIds	Topic Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 16.
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
studentOrder	Student Order	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 128.
topicOrder	Topic Order	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 16.
homeGroups	Home Groups	Required. Array of Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 16.; minimum items 1; maximum items 128.
expertGroups	Expert Groups	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
expertGroups[].topicId	Topic Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
expertGroups[].studentIds	Student Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 128.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric 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",
      "D",
      "E",
      "F"
    ],
    "topicIds": [
      "T1",
      "T2",
      "T3"
    ],
    "seed": 42
  },
  "output": {
    "studentOrder": [
      "B",
      "A",
      "E",
      "F",
      "C",
      "D"
    ],
    "topicOrder": [
      "T3",
      "T1",
      "T2"
    ],
    "homeGroups": [
      [
        "B",
        "A",
        "E"
      ],
      [
        "F",
        "C",
        "D"
      ]
    ],
    "expertGroups": [
      {
        "topicId": "T3",
        "studentIds": [
          "B",
          "F"
        ]
      },
      {
        "topicId": "T1",
        "studentIds": [
          "A",
          "C"
        ]
      },
      {
        "topicId": "T2",
        "studentIds": [
          "E",
          "D"
        ]
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Student count must be divisible by topic count, IDs must be distinct, and student IDs cannot contain commas because the delegated representation uses that separator. No learning outcome or group fairness beyond construction is assessed.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
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 Jigsaw Learning Assignment Generator — tool.io
Calculation or validation: pedagogy/jigsaw-rotation-schedule — tool.io

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