When mastery sessions contain unequal opportunities, an unweighted average of session percentages gives each session equal influence, while pooled accuracy gives each opportunity equal influence. Both answer different questions. Show every numerator and denominator, calculate each method transparently, and choose the governing rule before outcomes. Also inspect session context, because pooling can let one high-volume setting dominate the result.

Calculate the session average

Convert each valid session to a percentage, sum those percentages, and divide by the number of included sessions.

Calculate pooled accuracy

Add correct responses across sessions and divide by total eligible opportunities across the same sessions.

Inspect influence

Identify which session or context receives the most weight under each method and whether that matches the decision.

Keep context-specific floors

Add a minimum for a critical setting or response when an overall aggregate could conceal unsafe performance.

Lock the aggregation rule

Document the chosen method, rationale, exclusions, and version before it controls a mastery transition.

Build Dev's unequal-opportunity mastery table

For the unequal opportunities across mastery sessions question, create a versioned unequal-opportunity mastery table. Preserve the target, client-selected purpose, operational response, accepted communication forms, eligible opportunities, observation time, numerator, denominator, independence, prompts, context, partners, materials, criterion, teaching history, generalization, maintenance, access, safety, decision owner, actual transition, and follow-up. Another qualified reviewer should be able to reconstruct Dev's result without guessing what counted.

Work through Dev's mastery example

Dev is correct in 1 of 1 opportunity during a short home visit and 8 of 10 during a longer visit. The unweighted session average is (100% + 80%) / 2 = 90%. Pooled opportunity accuracy is 9 of 11, or 81.8%. Neither calculation is an error; they weight the two sessions differently. Show raw counts and every relevant cell or date before adding percentages. This fictional home kitchen-safety program example illustrates one decision pattern and does not establish a universal mastery level, observation count, probe schedule, treatment plan, or transition rule.

Audit Dev's source evidence

Dev's table keeps 1 of 1 and 8 of 10 in separate rows, shows both calculations, and records task type, independence, opportunity eligibility, and visit length. It identifies the prospectively selected release rule and why it fits the target. The audit also checks definition and criterion versions, data-entry history, invalid and missing states, opportunity selection, observer agreement when useful, graph or table calculations, prompt coding, teaching integrity, and corrections. Unresolved discrepancies stay visible and pause any transition that depends on them.

Address Dev's main decision risk

Pooling can hide a weak low-volume context, while equal session weighting can let one trial count as much as ten. Dev's review always shows session-level results beside the aggregate. A dashboard can calculate and surface evidence. It cannot decide that a skill is meaningful, safe, generalized, maintained, or complete. Those judgments belong to the person and qualified roles under the applicable plan and authority.

Choose Dev's next step

If the skill must work in both contexts, the criterion can include a context-specific floor rather than relying on one pooled number. The rule remains individualized and auditable. Record the decision, rationale, owner, date, support plan, and review trigger. Keep acquisition, generalization, maintenance, and clinical value as separate evidence so one favorable state cannot silently satisfy another.

Protect Dev's access and choice

Keep Dev's AAC, interpreters, mobility, food, water, bathroom use, prescribed care, health support, rest, relationships, and emergency help available. Ordinary communication, sensory, visual, and mobility supports remain present during teaching and probes unless the defined question concerns one support and qualified review approves the safe comparison. Use accessible consent and assent processes when applicable and respond to withdrawal, dissent, or distress.

Apply current sources to Dev's review

Dev's page connects mastery-criterion design with a denominator-safe aggregation decision that the literature rarely standardizes for every applied setting. The BACB ethics hub and CASP public summary provide professional context, while the BCBA Test Content Outline identifies examination content on measurement and skill acquisition. The WWC handbook supplies research-design context. A mastery and maintenance review, a replication of criterion effects, and a preliminary criterion analysis show that criterion details can affect later responding and remain incompletely standardized. A generalization case study and ABA instructional-design paper address purposeful planning. ASHA supports continuous AAC access.

Rehearse Dev's decision rule

Test the unequal-opportunity mastery table with fictional cases containing 0 of 0 opportunities, a perfect 2 of 2, unequal session denominators, prompted success, a missing session, a weak context, an overdue probe, and a system-access failure. Confirm that statuses, denominators, holds, calculations, and review routes behave as intended. Store expected outputs, software version, reviewer notes, and the correction log before the rule touches live data.

Close Dev's mastery review

Review the unequal-opportunity mastery table with Dev, the responsible clinician, and other specialists required by the target. Preserve raw data, active criterion, teaching context, client input, access and safety evidence, generalization, maintenance, qualified decision, actual transition, exceptions, and later outcomes. Keep the page draft and noindex until every manifest-named review is complete.

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