Random Interleaved Topic Practice Playlist Generator
Build bounded distinct-item practice playlists with no adjacent equal topic. Uniform eligible topics, not globally uniform feasible playlists; explicit failure if sampled prefix exhausts another topic. No learning-efficacy claim.
Description
Interleaved practice alternates topics so adjacent items do not use the same topic label.
Interleaved practice alternates topics so adjacent items do not use the same topic label.1
This generator selects distinct supplied items by first drawing an eligible topic and then a remaining item within that topic.
When to use Random Interleaved Topic Practice Playlist Generator
Use this tool when you need to produce the reproducible draw or model experiment described below. Prepare a reproducible seed and the inventory, corpus, or experiment parameters stated below.
How to use Random Interleaved Topic Practice Playlist Generator and understand its results
- Prepare a reproducible seed and the inventory, corpus, or experiment parameters stated below.
- Enter seed, count, topicIds, practiceItemIds, itemTopicIds, following the field table and calculation rules below, then run the tool.
- Read result. For result objects, inspect the nested output fields below, including any missing evidence or selected indices.
- Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| seed | Required nonblank deterministic seed; the SDK can replace the source with context.random. | string | Required |
| count | Number of draws, cards, trials, or output entries under the tool-specific rule; safe integer 1–64 with any narrower inventory bound. | number | Required |
| topicIds | Topic ids | list of string | Required |
| practiceItemIds | Practice item ids | list of string | Required |
| itemTopicIds | Item topic ids | list of string | Required |
Require unique nonblank topic and item IDs, aligned itemTopicIds belonging to topicIds, and count from one through min(64,itemCount). At each step collect topics different from the previous topic that still have unused items, choose one uniformly, then choose an unused item of that topic uniformly. Return the playlist and unused IDs. If the sampled prefix leaves no eligible topic, fail without backtracking; a different prefix could still have worked.2, 3
Use a nonblank seed of at most 256 UTF-16 units and a safe-integer count from 1 to 64, subject to the tool’s narrower inventory bound. By default the seed is hashed into a deterministic pseudorandom state; each run restarts that state, so identical full inputs and implementation reproduce the result. The SDK may provide context.random, which replaces the seeded source and advances across the request; the seed remains required. Every source value must be finite in [0,1), with a request limit of 100000 source calls. Draw an index as floor(U×remainingSize). For indexed inventory draws, with replacement draw from the full inventory each time; without replacement remove each chosen position from an ordered pool and reject counts exceeding the inventory size. Uniform-source draws are over positions, not necessarily distinct values. This is not secure randomness.
P(nextItem)=1/eligibleTopicCount×1/unusedItemsInChosenTopic Read these symbols with the field meanings and branch or reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Composite output; read its nested fields and the worked example. | object |
| result.playlist | playlist | list |
| result.unusedItemIds | unused Item Ids | list |
| result.samplingConvention | sampling Convention | string |
{
"input": {
"seed": "tool.io/batch42",
"count": 2,
"topicIds": [
"a",
"b"
],
"practiceItemIds": [
"a-1",
"a-2",
"b-1"
],
"itemTopicIds": [
"a",
"a",
"b"
]
},
"output": {
"result": {
"playlist": [
{
"itemId": "b-1",
"topicId": "b"
},
{
"itemId": "a-1",
"topicId": "a"
}
],
"unusedItemIds": [
"a-2"
],
"samplingConvention": "uniform-eligible-topic-then-uniform-remaining-item"
}
}
}Limitations and assumptions
The topic-first algorithm is not uniform over all feasible playlists or over all remaining items. It can fail after a sampled prefix even when another ordering exists, and it does not establish learning efficacy.
Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Arrays must be dense, with no missing cells. Ordinary string lists allow at most 128 strings of at most 128 UTF-16 units each. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related task, use Random Flashcard Practice Session Generator: Flashcards pair a prompt with a supplied answer for recall practice. This generator samples a session from explicit card records and lets you choose whether positions can repeat, without verifying the answers. For a complementary step, use Random Classroom Choice Board Generator: A classroom choice board places activity options into a grid for review or selection. This generator draws distinct supplied choices and assigns row and column coordinates, including a potentially incomplete final row.
References
Similar or alternative tools
- Random Flashcard Practice Session Generator
Flashcards pair a prompt with a supplied answer for recall practice. This generator samples a session from explicit card records and lets you choose whether positions can repeat, without verifying the answers.
- Random Classroom Choice Board Generator
A classroom choice board places activity options into a grid for review or selection. This generator draws distinct supplied choices and assigns row and column coordinates, including a potentially incomplete final row.