Report
Programme analytics for resourcing. Tutor signals for conversation. Never labels.
Programme- and cohort-level analytics show where teaching is landing and where to invest. Where signals appear at individual level, they are framed for human conversation — tutor outreach, support resourcing — never as predictions or labels about named learners. Educators decide what to do with what the data shows.
Most analytics dashboards confuse signals for verdicts.
Assessment data is most useful when it informs a human conversation — a tutor reaching out earlier, a programme lead deciding where to add support, an academic committee revisiting an intended learning outcome. Used that way, it changes outcomes for the better.
Used badly, it labels learners with predicted outcomes, narrows what gets taught to what gets measured, and creates self-fulfilling prophecies that disproportionately affect learners who were already underserved. The difference is in how the analytics are framed and who decides what to do with them.
NUADU's analytics are designed for the conversation — not the verdict.
Different roles. Different views. Same data.
The educator view
Per-learner performance against rubric criteria over time. Class-level patterns by criterion and by standard. Where the rubric is being met, where it isn't. Designed to inform what gets re-taught — not to label learners.
The programme view
Cohort comparisons across departments, courses and years. Where intended learning outcomes are being met across cohorts. Resourcing signals — where support time is going to be needed. Documentation ready for accreditation review.
The institutional view
Aggregated patterns across faculties, sites and years. Equity-aware breakdowns where the institution has chosen to track them. Reform-impact tracking before-and-after a curriculum change. No individual-level forecasting.
Analytics designed for the decisions they support.
Score distribution
Visual distributions across assessments, classes and cohorts.
Pattern analysis
Patterns by rubric criterion, by standard, by item. Where the rubric is being met, where it isn't.
Tutor signals
Individual signals framed for tutor outreach — never as automated predictions or labels about named learners.
Drill-down views
From programme to cohort to individual — surfaced as supporting evidence for a tutor conversation.
Trend analysis
Performance over time. Whether changes in teaching are being reflected in evidence.
Cohort indicators
Aggregated cohort indicators for programme resourcing. Not individual forecasts about named learners.
Analytics for the conversation, not the verdict.
A parent meeting
A teacher prepares for a parent meeting. Pulls up the learner's rubric profile, the criteria where they are progressing well, the criteria where they would benefit from support, and the work that evidences both. The conversation is about teaching and learning — not a number.
- Rubric profile over time
- Specific work as evidence
- Concrete next-step suggestions
Accreditation self-study
Programme-level intended learning outcome reports across three cohorts. Where ILOs are being met, where they aren't, what changed when the curriculum changed. Generated against your accreditation framework.
- Three-cohort ILO comparisons
- Framework-aligned exports
- Documentation-ready
A curriculum review
An academic committee reviews a unit's intended learning outcomes against four years of cohort evidence. Decides where to revise content, where to revise assessment, where the rubric needs updating. Decisions stay with the committee.
- Multi-cohort ILO review
- Evidence for committee decisions
- Decisions stay with humans
A workforce capability picture
An L&D team maps employees against role-specific rubric criteria. Identifies where capability is strong, where it is thin, where targeted programmes are needed. Used to inform programme investment — not individual labelling.
- Role-specific capability mapping
- Aggregated by team and region
- Programme investment evidence
Why this is decision-support, not labelling.
Aggregated by default
The default analytics view is cohort and programme. Individual signals are surfaced for tutor outreach — never as automated predictions about named learners.
Rubric-anchored
Patterns are anchored to your rubric criteria. Not generic engagement scores. Not behavioural inferences. The rubric that authored the assessment is the rubric the analytics use.
Decisions stay with humans
NUADU's analytics inform decisions; they do not make them. There is no automated routing, no automated labelling, no automated escalation of named learners.
Connected to the lifecycle
Analytics draw from authoring, delivery, marking and credential data. The audit trail is intact. Where a number is shown, it can be traced to the rubric criterion, the item, the marker, and the candidate's response.
Equity-aware where you ask
Equity breakdowns are configurable per institution. Where used, they are designed to surface where institutional support is reaching some groups and not others — to inform institutional response.
Questions, answered.
See analytics designed for the conversation.
Walk through the views with your programme lead. See what is aggregated by default, what is configurable, and what NUADU deliberately does not do.