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Digital Assessment Completion Forecast Estimator

The Digital Assessment Completion Forecast Estimator projects how much of an assigned learner population may finish an assessment by the end of the testing window if the completion pace observed so far continues. It is useful for exam administrators who want an early indication of whether reminders, added sessions, or capacity changes may be needed.

The model uses the current completed percentage divided by elapsed days as a simple average pace, then extends that pace to the full window. It also estimates the corresponding number of completed learners. Assessment participation often clusters near deadlines, so the projection should be refreshed throughout the window and interpreted with known scheduling constraints rather than treated as a fixed outcome.

Digital assessment completion inputs

learners
%
days
days
Result
—
projected assessment completion
Projected completed learners—
Completed learners now—
Observed completion pace—

1. Enter assigned learners
Use the full population expected or required to complete the digital assessment during this window.

2. Enter completed percentage
Use the share already recorded as fully completed, not merely started.

3. Enter elapsed days
Count the days from opening of the testing window through the current measurement point.

4. Enter total window length
Include the entire planned testing period in days.

5. Review the projection
Compare the projected completion percentage and learner count with the required target, then refresh the forecast as the deadline approaches.

Observed daily completion pace = Current completed rate ÷ Days elapsed

Projected completed rate = min(100%, Observed daily completion pace × Total testing-window days)

Projected completed learners = Assigned learners × Projected completed rate

Where:
Current completed rate = percentage fully completed
Days elapsed = days since assessment access opened
Total testing-window days = full assessment window
Assigned learners = population expected to complete

Assumptions: The projection is linear and capped at 100%. It does not explicitly model scheduled test sessions, no-shows, retakes, deadline surges, or capacity constraints.

What the result means

The result is the end-of-window completion percentage implied by the average completion pace recorded to date.

Use the capacity estimator alongside this forecast when testing seats or proctor availability could prevent the projected pace from being achieved.

Given:
1,100 assigned learners
32% completed
5 days elapsed
12 total testing-window days

Calculation:
Observed pace = 32% ÷ 5 = 6.4 percentage points per day
Projected rate = 6.4% × 12 = 76.8%
Projected learners = 1,100 × 0.768 = 844.8 ≈ 845

Result:
76.8% projected completion; about 845 learners

If the average pace continues, roughly 845 of the 1,100 assigned learners would finish by the end of the window.

Should started assessments count as completed?

No. Use learners who satisfy the platform’s completion rule for the assessment. Starts can be tracked separately as an engagement or funnel metric.

What if most learners are scheduled near the deadline?

A linear forecast may understate later completion when sessions are intentionally back-loaded. Interpret the projection with the actual booking schedule or run separate forecasts by session group.

Can the forecast exceed 100%?

The underlying pace can mathematically imply more than 100%, but the displayed completion forecast is capped at 100% because completion cannot exceed the assigned population.

How often should I update the inputs?

Update whenever a meaningful amount of new completion data arrives, such as daily during a short testing window. Frequent refreshes show whether the observed pace is accelerating or slowing.

Does this account for available testing capacity?

No. It extrapolates observed completion pace. If future seat, proctor, or platform capacity is constrained, compare the forecast with an operational capacity estimate.