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STEM REASONING AND EVIDENCE WORKBENCH

Misconception Evidence Analyzer

Summarize anonymous item-level correct and target-option rates with Wilson intervals and minimum-cohort review.

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 misconception evidence analyzer

Enter an anonymous analysis label, aggregate cohort label, and minimum cohort size. Add item | teacher-named target misconception | attempted count | correct count | target-option count | review-threshold percent rows. Counts must use the same anonymous denominator and cannot exceed attempts.

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 misconception evidence analyzer works

Correct rate equals correct divided by attempts. Target rate equals named-option selections divided by attempts. A two-sided 95 percent Wilson interval describes binomial sampling uncertainty. A review signal requires both a cohort at or above the declared minimum and an observed target rate at or above its entered threshold. A threshold signal means the item deserves human review and more evidence. It can reflect conceptual reasoning, wording, instruction, language access, response mapping, guessing, or data error. Inspect explanations and another task before planning a response, and discuss the pattern at cohort level without attaching it to learner identity.

  1. 01
    Before generation

    Confirm the question, approved source, units, notation, signs, response coding, model assumptions, and review threshold. Keep an unchanged copy of measurements or learner evidence.

  2. 02
    During review

    Run the worked example, inspect every intermediate row, and compare the result with a hand calculation, diagram, representation, periodic table, or approved laboratory method.

  3. 03
    Before use

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

How to interpret the result

Misconception Evidence Analyzer

Before generation

Use to prioritize follow-up questions or lesson review from aggregate response patterns. It does not diagnose an individual, establish why an option was selected, score open reasoning, adjust for guessing, validate item quality, or justify high-stakes action. Small cohorts are explicitly withheld from threshold decisions.

Worked example

The fraction item has fourteen of thirty selections on the named option and crosses its thirty-five-percent threshold. Two larger items remain below their thresholds. A six-response pilot shows a high proportion but falls below the declared cohort minimum, so the tool reports insufficient aggregate size instead of treating it as evidence.

Formula or methodology

Correct rate equals correct divided by attempts. Target rate equals named-option selections divided by attempts. A two-sided 95 percent Wilson interval describes binomial sampling uncertainty. A review signal requires both a cohort at or above the declared minimum and an observed target rate at or above its entered threshold.

How to interpret the result

A threshold signal means the item deserves human review and more evidence. It can reflect conceptual reasoning, wording, instruction, language access, response mapping, guessing, or data error. Inspect explanations and another task before planning a response, and discuss the pattern at cohort level without attaching it to learner identity.

Common mistakes

Do not call every wrong response a misconception, infer cause from a distractor count, expose small cells, compare groups with different item conditions, ignore missing attempts, or treat an interval as a guarantee. Review item alignment, response coding, accessibility, privacy policy, and qualitative learner reasoning.

CAST assessment guidance

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, establish a chemical structure, or replace teacher, laboratory, and safety judgment.

Review checklist

Human review before educational use

  • Every value, symbol, unit, classification, and response matches the source evidence.
  • The disclosed model matches the course method and intended question.
  • 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

Misconception Evidence 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 school-approved system.

Can this result prove understanding or a scientific law?

No. It describes entered evidence under one disclosed rule. Compare several evidence sources and ask for reasoning before drawing a conclusion.

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 the course method and academic-integrity rules.