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EDUCATION DECISION WORKBENCH

Standards Evidence Coverage Dashboard

Rank standards by planned, assessed, and mastery-evidence gaps using anonymous aggregate opportunity counts.

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 standards evidence coverage dashboard

Name the curriculum review, choose a target number of credible mastery-evidence opportunities per standard, and enter standard | planned opportunities | assessed opportunities | mastery-evidence count. Use anonymous aggregate counts, consistent time windows, and the same definition of an opportunity across rows. Assessed and mastery-evidence counts cannot exceed their containing opportunity counts.

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, policy-approved information. Results remain in this browser tab until you copy or download them.

Transparent method

How the standards evidence coverage dashboard works

Assessment coverage equals assessed opportunities divided by planned opportunities times 100. Evidence gap equals max(0, target evidence count minus entered mastery-evidence count). Rows sort by the largest gap, then lower coverage, then label. The overall coverage percentage uses total assessed divided by total planned, which weights standards by their opportunity counts rather than averaging row percentages. A high-priority row signals missing or insufficient evidence under the chosen definitions. It does not show that learners failed the standard. The team should inspect alignment, task quality, accessibility, timing, scoring reliability, and whether planned opportunities actually occurred. A target is a planning rule and may differ by standard, assessment system, or local policy.

  1. 01
    Before generation

    Confirm the question, variables, units, source data, measurement precision, and the model expected by the current course or laboratory. Keep an untouched copy of observations before excluding or transforming any value.

  2. 02
    During review

    Run the worked example first, inspect every intermediate row, compare units and signs, and look for a result that conflicts with the graph, physical system, chemical equation, or learning objective.

  3. 03
    Before use

    Record the method, assumptions, units, rounding, data limitations, and any excluded observations. Compare the exported report with an independent calculation or approved reference before assessment or laboratory use.

How to interpret the result

Standards Evidence Coverage Dashboard

Before generation

Use this dashboard during unit planning, common assessment review, or curriculum mapping when a team needs to find standards that were planned but not yet assessed or lack enough usable evidence. It separates delivery plans from assessment checkpoints and evidence, which helps prevent a lesson mention from being counted as proof of learning. It does not evaluate individual learners, teacher performance, standards importance, or instructional quality.

Worked example

The fictional Grade 8 review sets a target of three evidence opportunities. Linear equations meet the target, slope interpretation has an evidence gap, comparing functions was planned but never assessed, and bivariate modeling has only one assessed opportunity. The table ranks the largest evidence gaps first while retaining planned and assessed denominators so the team can see why each row is prioritized.

Formula or methodology

Assessment coverage equals assessed opportunities divided by planned opportunities times 100. Evidence gap equals max(0, target evidence count minus entered mastery-evidence count). Rows sort by the largest gap, then lower coverage, then label. The overall coverage percentage uses total assessed divided by total planned, which weights standards by their opportunity counts rather than averaging row percentages.

How to interpret the result

A high-priority row signals missing or insufficient evidence under the chosen definitions. It does not show that learners failed the standard. The team should inspect alignment, task quality, accessibility, timing, scoring reliability, and whether planned opportunities actually occurred. A target is a planning rule and may differ by standard, assessment system, or local policy.

Common mistakes

Do not enter names, count the same task differently across standards, treat coverage as mastery, hide a zero denominator, average percentages with unequal denominators, or choose a target after seeing results only to improve the headline. Document the counting rules, retain the source map, inspect actual tasks, and pair aggregate gaps with authorized classroom evidence before changing instruction.

CAST UDL on Campus: assessment and multiple forms of evidence

Learning boundary

A transparent model still needs scientific review

This browser-local workbench calculates only from the entered model and observations. It cannot validate an experiment, balance an equation, establish causation, certify laboratory safety, diagnose learning, or replace the method required by a teacher, curriculum, laboratory, or institution.

Review checklist

Human review before educational use

  • Every value has the correct unit, sign, precision, and source.
  • The selected equation or statistical model matches the question.
  • Intermediate rows, assumptions, residuals, and warnings have been inspected.
  • A qualified teacher or learner verifies the report against the approved method.

Practical questions

Standards Evidence Coverage Dashboard questions

Does Nirmion upload these observations?

No. The calculation runs in the current browser tab. Avoid personal information and retain laboratory data in the system authorized by the school.

Can I submit the generated report as final work?

Treat it as an editable analysis aid. Show your own reasoning, cite the required sources, follow academic-integrity rules, and use the method requested by your course.

Does a plausible number prove the model is correct?

No. Check dimensions, graph shape, assumptions, measurement uncertainty, significant figures, and whether another model explains the observations better.