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STEM PRACTICE AND EVIDENCE WORKBENCH

Mathematical Discourse Prompt Analyzer

Audit structured prompt evidence for reasoning, strategy comparison, open response, and connection to peer contributions.

Runs in this browser tabEditable downloadUp to 500 structured rows and 30,000 characters per field. Use anonymous or fictional labels.

Learning inputs

Build the mathematical discourse prompt analyzer

Enter a fictional analysis label, a minimum feature count from one through four, and prompt label | open response yes or no | requires reasoning yes or no | invites multiple strategies yes or no | connects to a peer contribution yes or no | strong or review claim rows. Code features from the actual prompt and facilitation plan rather than from a title alone.

Generation uses only the information entered in this tab. It does not fetch sources or make student decisions.

Review output

Your learning artifact appears here

Load the worked example to inspect realistic inputs, or enter anonymous classroom or laboratory evidence. Results remain in this browser tab until you copy or download them.

Transparent method

How the mathematical discourse prompt analyzer works

Feature score equals the number of yes entries among open response, reasoning demand, multiple strategies, and peer connection. Strong means score is at least the entered minimum; otherwise the local model says review. Every feature remains visible, and prompt text is never silently interpreted by a hidden language model or keyword list. Supported means the entered strong-or-review claim matches the disclosed feature count. The result describes planned opportunity, not actual discourse quality. Review the mathematical goal, expected reasoning, teacher moves, language access, wait time, norms, representation options, response patterns, and whether all learners can enter and extend the conversation.

  1. 01
    Before generation

    Confirm the question, source, units, notation, response coding, model assumptions, and review threshold. Keep an unchanged copy of the original evidence.

  2. 02
    During review

    Run the worked example, inspect every intermediate row, and compare it with a hand calculation, diagram, representation, or approved subject method.

  3. 03
    Before use

    Record the method, assumptions, units, classifications, and flagged rows. Verify the editable report independently before educational or laboratory use.

How to interpret the result

Mathematical Discourse Prompt Analyzer

Before generation

Use while reviewing a set of planned mathematics prompts for varied opportunities to explain, compare, justify, and respond. The analyzer applies structured reviewer coding rather than a keyword score. It does not listen to classroom talk, assess mathematical correctness, measure participation equity, infer cognitive demand, or judge an individual learner.

Worked example

The compare-methods prompt contains all four entered features and the explanation prompt contains three, so both strong claims meet a threshold of three. A calculate-only item has no listed discourse features and its review claim is supported. The final closed prompt contains only reasoning and strategy flags, so its strong claim is flagged.

Formula or methodology

Feature score equals the number of yes entries among open response, reasoning demand, multiple strategies, and peer connection. Strong means score is at least the entered minimum; otherwise the local model says review. Every feature remains visible, and prompt text is never silently interpreted by a hidden language model or keyword list.

How to interpret the result

Supported means the entered strong-or-review claim matches the disclosed feature count. The result describes planned opportunity, not actual discourse quality. Review the mathematical goal, expected reasoning, teacher moves, language access, wait time, norms, representation options, response patterns, and whether all learners can enter and extend the conversation.

Common mistakes

Do not mark a prompt open merely because it is long, count a request to show work as genuine reasoning automatically, assume different answers imply different strategies, require public speech as the only participation route, or use a high feature count as proof of productive discourse. Inspect enactment and evidence from learner thinking.

NCTM Principles to Actions resources

Learning boundary

A calculated signal still requires subject review

This browser-local workbench applies only the disclosed rules. It cannot diagnose a learner, certify an experiment, or replace teacher, laboratory, and safety judgment.

Review checklist

Human review before educational use

  • Every entered value, unit, label, and response matches the approved source evidence.
  • The disclosed model matches the intended curriculum question and evidence type.
  • Intermediate rows and conflicts have been inspected rather than using only the headline.
  • A teacher or learner verifies the report independently and records corrections.

Practical questions

Mathematical Discourse Prompt Analyzer questions

Does Nirmion upload these inputs?

No. Processing occurs in the current browser tab. Avoid personal information and retain authoritative records only in the approved school system.

Can the result prove mastery or scientific validity?

No. It describes entered evidence under one disclosed rule. Review reasoning, uncertainty, access, and several evidence sources.

Can I submit the downloaded report as finished work?

Treat it as an editable calculation aid. Show your own reasoning, cite required sources, and follow course and academic-integrity rules.