Trainers · Reading results
Reading results
While your class works, the Cohort progress table updates live. This page walks every column so you can read the room at a glance — and shows you where the real teaching signal hides.
The table is in the instructor view under Training → Labs. It refreshes on its own as students start labs, get seeded with telemetry, and submit dispositions — no need to reload.
The columns, one by one
| Column | What it tells you |
|---|---|
| Student | The student's account. One row per student per assigned lab. |
| Cohort | The cohort name you chose at provisioning. Use it to filter a single class out of the list. |
| Status | Where the student is in the lab lifecycle — assigned, seeded, or graded (see below). |
| Incident | A link to the incident the detection opened in that student's sandbox. Click through to see exactly what they're looking at. |
| Disposition | The student's own verdict, marked correct or incorrect against the scenario's ground truth. |
| AI verdict | The platform AI analyst's independent disposition on the same incident, with its confidence. |
| Score | The auto-grade out of 100: 70 for a matching disposition, 30 for a specific rationale. |
Reading the statuses
- assigned — the lab is provisioned but the student hasn't clicked Start lab yet. A row stuck here is a student who hasn't begun.
- seeded — telemetry has been generated and the incident has fired. The student is investigating but hasn't submitted a disposition.
- graded — the student submitted a disposition and received a score. The investigation is complete.
At a glance, a healthy class moves left to right: assigned → seeded → graded. Stragglers in assigned need a nudge to start; a pile-up in seeded often means the investigation step deserves more time or a hint.
The signal that matters most: student vs AI
Read the Disposition and AI verdict columns together — that pairing is the heart of the exercise.
- They agree, and both are correct. Confirmation. The student reasoned to the same place as the AI analyst. Move on quickly.
- They disagree. This is your richest material. Either the student out-reasoned a confident AI, or the AI caught something the student missed. Open the incident, find out which, and make it the centerpiece of your debrief.
- They agree, and both are wrong. Rare, but worth flagging — a shared blind spot is a great class-wide teaching point.
Don't treat the AI verdict as the answer key — the scenario's ground truth is. The AI is a second opinion, and its confidence is part of the lesson: a confident verdict can still be wrong, and students should learn to weigh evidence over assertiveness.
Guessed, or reasoned? Read the score split
A single score hides a lot; the 70/30 split reveals it.
- Got the disposition, weak rationale (≈70/100). The student likely guessed right — or pattern-matched without showing their work. Correct call, thin reasoning. Coach the rationale.
- Wrong disposition, strong rationale. The student reasoned carefully but reached the wrong conclusion. Valuable: the thinking is there, so the fix is targeted.
- Both strong (near 100). Confident analyst. Stretch them with the discussion questions.
- Both weak. The student needs to walk back through the evidence — point them at the walkthrough.
Suggested follow-up
- Sort by disagreement (student vs AI) and pull two or three rows for the debrief.
- Open one correct-disposition, low-rationale incident with the class and ask the student to defend the call out loud.
- Re-provision and reassign for any student who wants a second attempt — a fresh run is a clean labeled judgement.
Next: the facilitation guide turns these readings into a ready-to-run lesson plan, or revisit Running a cohort to add a latecomer.