Concept — Time-to-Apply Metrics (not completion)

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The metrics that anchor every dashboard. v3 explicitly replaces generic “completion rate” with behavioral measures, because completion measures task-doing while time-to-apply measures the value prop: reducing the time between learning and improved practice.

The core metric set (verbatim intent from v3)

% reporting use of a strategy within 24 hours / 48 hours / 5 days
% who apply a strategy more than once (repeat-application rate)
Average & median time from learning to first classroom use
Average & median number of applications needed for effective mastery (the “mastery curve”)

Where each surfaces

Metric	Participant	IS	LA	PM
Time-to-first-application	own	per-cohort	cohort	roll-up
% applied 24h/48h/5d	—	dashboard threshold view	cohort outcomes	roll-up, filterable
Repeat-application rate	—	gap detection	cohort outcomes	roll-up
Applications-to-mastery	profile competencies	recommendation engine	impact evidence	roll-up

Filterability is the differentiator

Because the context layer stores context as structured signals, the PM roll-up is filterable by readiness, change load, and priority area. That answers questions the current platform cannot:

“Do schools with high accountability pressure apply faster?”
“Which instructional strategies are applied fastest / slowest?”
“How many nudges do people typically need before first application?”
“Which coaching intervention actually changes classroom behavior?”

Why this matters commercially

“The behavioral-change value prop is asserted, not evidenced” → time-to-apply + repeat-application + mastery-curve metrics produce the evidence to defend pricing and renewals. See business loop.

Dependencies

Mark-complete timestamp (IS action) opens the application-tracking window — the clock the 24h/48h/5d windows run against.
Application evidence capture in the Apply phase (artifact / observation / self-report) — see open question on evidence.
Feeds the intervention layer thresholds directly.