The feedback gap

Most educational feedback is write-once, read-once. A teacher spends hours writing comments on student work, the student glances at them, and the insights disappear. There's no aggregation, no pattern recognition, and no way to track whether feedback actually leads to improvement.

We built Ren's tagging system to change that.

From comments to data

Every piece of feedback in Ren is automatically tagged across three dimensions:

Quality signals

These describe what kind of feedback is being given:

Positive signals like good-example, clear-stance, good-structure
Improvement signals like needs-substantiation, missing-units, spelling-errors

Topic hierarchy

Tags that map feedback to curriculum topics, from broad subjects down to specific sub-topics. This allows teachers to see which areas of the syllabus students are struggling with most.

Question type

Whether the question tests recall, application, analysis, or evaluation - following Bloom's taxonomy. This helps identify if students are strong on knowledge but weak on higher-order thinking.

What this enables

Once feedback is structured, powerful things become possible:

Class-level insights: Which topics need revisiting? Where are the common misconceptions?
Student trajectories: Is a student improving in their analytical writing over time?
Intervention planning: Which students are showing early warning signs that need teacher attention?
Department analytics: How consistent is marking across different teachers?

The aggregation layer

Raw tags roll up into dashboards that give teachers and heads of department a birds-eye view without losing the ability to drill down into individual student responses.

Student → Feedback Points → Tags → Class Analytics → Department View

Each layer preserves the connection to the layer below it, so when a head of department sees that "Year 10 Chemistry is struggling with ionic bonding," they can click through to see the specific feedback points that surfaced that pattern.

Privacy by design

All analytics are computed at the school level. Student data never leaves the school's tenant. Teachers see their own classes, heads of department see their department, and no individual student data is exposed at the aggregate level without explicit permission.

What's next

We're working on longitudinal tracking - the ability to see how a student's performance evolves across an entire academic year. Early results suggest that simply making feedback patterns visible to teachers leads to more targeted and effective follow-up.

Interested in bringing structured feedback to your school? Talk to our team.

Building Better Feedback Loops in Education