Cohort Analytics · xAPI-backed · Learning Engineering L1
What the cohort
actually did.
A focused view of progress, evidence coverage, rubric reliability, and the next program decision.
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Learners
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Session completions
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Checklist events
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Exit-ticket events
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Rubric assessments
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AI touchpoint calls
Rubric criterion pass-rate
Proficient-floor pass rate per criterion. <70% cohort-wide signals a session gap, not a learner gap.Cohort-over-cohort comparison
Snapshot the current cohort, then diff it against a prior one. Per-criterion deltas show where the program is gaining and where it's losing ground.Take a snapshot at the end of each cohort — it freezes the criterion pass-rates, funnel, and skills-growth into an immutable record. Diffs surface only when you have two snapshots (or one + live).
No snapshots yet.
Session completion funnel
Share of cohort who have marked each session complete.LO evidence coverage
Green = cohort has produced evidence against this LO this week. Gaps here flag evaluation-plan triggers.AI touchpoint uptake
BYOK Gemini calls — per-session usage, success rate, average latency. No institutional budget; each learner runs on their own key, so low uptake is a scaffolding signal, not a cost signal.Skills growth · pre vs post self-assessment
0–4 Likert on 8 skills. Left bar = S1 pre, right bar = S12 post. Negative deltas are real: they usually mean calibration, not regression.Inter-rater reliability
Two-grader sample across D1–D5. Cohen's κ tracked over time. Target κ ≥ 0.70.
Live κ is computed from paired reviewed statements distinguished by
context.extensions.rater. A κ below 0.70 on a criterion is the signal to
re-calibrate raters against the written descriptor — not to re-grade the cohort.