Institution

License

Entitlements

Share these URLs with your institution's LMS administrator when they register IntelliView as an external tool in their LMS:

After the LMS admin registers IntelliView in their LMS, they'll get back a client_id and other platform details. Enter them here so our worker can verify launches from that LMS.

Each row is a course context an LMS has launched at least one student into. Rosters refresh automatically on each launch via Names & Roles Provisioning Service. Click a row to view members; use Refresh on a specific row to re-pull from the LMS now.

Tenant-owned curriculum graph: programs → courses → outcomes. Outcomes can be hierarchical via parent_id. Adapters reference the IRIs of these outcomes when mapping ingested activities to them.

One-click "will this course's dashboards have data?" gauge — checks LMS mapping, enrollments, license entitlement, and whether mastery is rolling up (with the fix for anything red). Pick the tenant above, then a course.

Which mastery frameworks the faculty & program-director dashboards show for this tenant. CBEPT (APTA entry-level PT competencies) fits entry-level PT programs; Topic mastery (per-lecture mastery from quiz & exam performance) is discipline-agnostic and fits any program — e.g. post-professional PT or non-PT health-science programs where CBEPT doesn't apply. Both are on by default.

Upload a CSV of exam scores, clinical evaluations, attendance, or assignment grades. Each row becomes one xAPI statement, resolved through the Activity Registry and forwarded to the LRS. Use Preview (dry run) first to validate the file without writing.

Map ingested activities (Canvas assignments, exam items, attendance sessions, IntelliView videos) to your curriculum outcomes. Each mapping counts as evidence the intelligence layer rolls up to compute mastery per outcome. The list shows this tenant's own activities plus the shared catalog (global videos any licensed tenant can align). IntelliView videos aren't auto-registered — Register watched videos pulls the ones students watched into the shared catalog (registered once, alignable by every licensed university), then map each to its outcome(s).

The AI proposes outcome(s) for each unmapped activity. Nothing is saved until you click Accept — review every suggestion. Accepted mappings are recorded as ai_suggested with the suggestion's confidence as the weight.

Per-student per-outcome rollup. Computed on demand from xAPI statements (LRS) joined with activity-outcome mappings (Activity Registry) and the tenant's curriculum graph. Score is a weighted mean of statement.result.score.scaled using the mapping weight, taken across every statement that ties the student to the outcome.

Per-outcome mean mastery, broken out by cohort. The Delta column is max−min across cohorts — large deltas flag where one cohort is materially behind.

V0 — mastery snapshot: builds on the V0.1 mastery rollup with rule-based flags. A student is flagged for: low average mastery, weakness in a critical-body-system outcome, sparse evidence (few outcomes evidenced), or low confidence. 2+ flags = high risk · 1 flag = medium · 0 flags = low. The thresholds below apply to V0.

V2 — engagement signals: the per-(student × course) model the Program Director dashboard uses. It scores seven decayed signals from raw xAPI — mastery deficit vs the course, overconfidence gap, low completion, rewatch spikes, unusually fast/slow answers, and heavy AI-tutor use. ≥4 points = high · 2–3 = medium. Shows one row per course a student has evidence in, with the signals that fired.

Pulls assignment grades from Canvas via the REST API and converts each graded submission to an xAPI statement. Per-tenant config: Canvas instance URL + admin API token + which course IDs to pull. Use Pull (dry run) first to see rendered statements without writing to LRS.