Product tour
Everything a QA office needs to run quality assurance, not just collect surveys.
OneRubric is deliberately narrow: not an LMS, not a student-information system, not a research-analytics platform. It is the system that takes an evaluation from submission to a verified improvement.
7
Role tiers
Student, lecturer, head, dean, executive, QA office, IT operator
≈250
Purpose-built screens
Each scoped to one role’s data
±0.20
Verification threshold
Improved, regressed or no change, stamped next period
By role
Pick a seat at the table.
Access is enforced by scope rows binding a person to specific schools and programmes. A hand-typed link to a course outside your scope returns an error, not a partial page.
A guided survey, and proof that feedback changed something
Students rate each course on a five-point scale across six areas of teaching, add anonymous comments, and can later see what their lecturer did in response.
- Multi-step evaluation wizard with progress saved automatically
- Anonymity reminders at every step; one evaluation per course
- Course expectations set at the start of term and answered at the end
- A “you said, we did” feed of concrete changes lecturers made
- Personal evaluation history and in-app notifications
Example comments from a demo institution.
Private AI
Reads every comment. Cites its sources.
Four AI surfaces, all built on aggregate-only digests and an anonymised comment array. No student identifier reaches the model, whichever provider runs it.
- Chat with retrieval. Ask what students say about pacing; the answer quotes the matching anonymous comments, with a sources strip.
- Briefings on demand per scope: institution, programme, faculty or course, streamed as they are written.
- Period reports generated when a period closes, replacing the hand-written cycle.
- Outline checker: compares an uploaded course outline with the institution’s required structure and explains pass or fail.
- Flag summaries speed up human review of flagged comments; they never act on their own.
Self-hosted: Mistral 7B via Ollama on your hardware. Hosted: Together.ai by default, OpenAI optional, each under a no-training agreement. Swappable from the operator screens, metered monthly, and shown as “AI unavailable” rather than an error when the model is down.
The action loop
A commitment, a baseline, and a verdict next period.
The feature that turns evaluation from a compliance chore into a measurable improvement programme.
- 1
Flag
An aspect is flagged for a course: an outlier, a decline, or an AI red flag.
- 2
Commit
The lecturer writes concrete action items and submits the plan.
- 3
Baseline
The aspect’s current average is recorded, keyed to lecturer and course so it survives roster changes.
- 4
Approve
The head of department reviews: draft → submitted → approved → completed.
- 5
Verify
A scheduled job measures the same aspect in a later period: +0.20 improved, −0.20 regressed, otherwise no change.
- 6
Report
Leadership sees the closure rate: how many flagged aspects have a plan, and how many verifiably improved.
Example action plan.
Early warning
Signals before the term is over.
End-of-term evaluation becomes confirmation, not surprise.
Mid-course pulse
A short check-in mid-semester, private to the lecturer and their head or dean. Pulse results never feed the leaderboard.
Course expectations
Students say what they hope for at term start; lecturers record met, partly met or unmet at term end; students see the response.
Outline compliance
Which lecturers have uploaded a compliant course outline, checked against the required structure, tracked per period.
Anomaly detection
Courses whose rating dropped sharply against their cohort, with a small-cohort fallback, so a hard semester for everyone is a trend rather than a wall of alarms.
Example board pack page.
Reporting and exports
One click to a board-ready PDF.
Every document carries the institution’s branding from the same theme as the live app, so exports never drift from the screen.
- Executive board pack: headline figures against targets, biggest movers, programme accountability, an equity lens, exemplars, action-loop closure and a student-voice section, with a warning when the sample is thin.
- Per-course and comments-only PDFs; comments from upheld disputes are left out automatically.
- Whole-period export: a background job packages summary spreadsheets, the roster and a PDF per course into a ZIP and emails the link.
- Audit-log CSV and a full institution export on demand.
Runs itself
The system tells you when to look.
An in-app bell is the source of truth; email is best-effort with per-person opt-out and a logged outbox with retry. Eleven scheduled jobs keep the institution’s tempo, each with a heartbeat.
Scheduled automation
- Close elapsed periods automatically
- Period-close alerts to the QA office
- Reminders to students and faculty
- Weekly digest
- Nudges to close open expectations
- Verify action plans against new data
- Send period briefings
- Process bulk exports
- Purge expired trials
Identity and integrations
- Google Workspace and Microsoft Entra sign-in
- SAML 2.0, started from either side, with replay protection
- SCIM 2.0 user and group provisioning from Okta or Entra
- Magic-link invitations
- Canvas and Moodle sync of terms, courses, sections, enrolments
- Roster and people import from spreadsheets
Your branding, from admin screens
- One primary colour sets the whole palette; no rebuild
- Logo, crest, product name, hero style, density
- Schools, programmes, campuses, period and role labels
- Custom evaluation questions per programme
- Timezone, locale, email domains, AI persona
- A guided setup wizard brings a new install online
SAML + SCIM
Enterprise identity
Canvas · Moodle
LMS sync
5
Cloud deploy targets
DigitalOcean, AWS, GCP, Azure, Railway; or Docker Compose for evaluation
A detailed feature catalogue is available for procurement on request.