Discover
Upload your data or run synthetic.
Student ProfileCORE
Pattern profile across subjects and sessions.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Teacher ProfileCORE
Per-classroom profile-shift signature, with the principal-ready report card.
Activates when you add NWEA MAP — or run synthetic data from Discover.
School Profile
Grade-level pathway health across the building.
Activates when you add NWEA MAP — or run synthetic data from Discover.
District Profile
Curriculum and pathway patterns across schools.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Cross Subject
Where a failure in one subject originates in another.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Decay Detection
Silent erosion surfaced before the next test confirms it.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Grade-Level Journey
Standards across sessions, stacked by health.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Classroom-Level Journey
Per-teacher averages across sessions with decay markers.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Pathway Topology
Prerequisite graph with declining pathways highlighted.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Parent Report Card
Print-ready plain-language student status.
Activates when you add NWEA MAP — or run synthetic data from Discover.
At-Risk Early Warning
Ranked at-risk list with a 'why now' for every name.
Activates when you add NWEA MAP — or run synthetic data from Discover.
Roster Optimization
Match student profiles to classroom signatures for next year's placement.
Activates when you add NWEA MAP — or run synthetic data from Discover.
School Matching for Parents
Given a student's profile, which school's signature fits their pathway best.
Activates when you add NWEA MAP — or run synthetic data from Discover.

See How They Can Work Together
Chain features and see the combined signal.

Upload your own data →Request a license →

Upload Your Own · Data Requirements

Run the engine on your own data

Synthetic data is the quickest way to see the feature board in motion. Uploading your own data is the credibility demonstration — the engine runs blind on numbers it has never seen before and returns the same profile, pathway, and decay classifications it would surface on real deployment. If you can follow the schema below, you can have every demo on this board running on your own school or district’s data in under ten minutes.

What this gives you

Every feature demo on the board — Student Profile, Decay Detection, Pathway Topology, Teacher Report Card, etc. — becomes live on your data. You see the engine classify your cohort, surface your declining pathways, identify your root-cause chains. The marketing surface becomes a working diagnostic in ten minutes. No commitment, no account creation, no contract — just data in, insight out. Reset or replace the data any time.

Privacy posture

Anonymization at ingest. Student names, teacher names, and other personal identifiers are replaced with stable within-session pseudonyms before any data reaches a feature demo surface. Names never render on the marketing surface, regardless of whether your upload contained them.
Session-scoped retention. Uploaded data lives for the duration of your demo session. Closing the browser, clicking Replace Data, or waiting 14 days rotates the session and expires the data. No data is retained past session boundaries for discovery users.
FERPA compliance by design. De-identification requirements exceed FERPA minimums. Full details in the Constitution under Student Data Governance Policy.
No cross-session tracking. Pseudonyms rotate on each session — a student in today’s demo cannot be matched to the same student in tomorrow’s demo via any downstream artifact.

Data sources

Four sources are supported. Connect as many or as few as you have. MAP alone lights up every feature on the board; adding SBAC and IC deepens the signal.

Why it matters

MAP is the anchor. Every profile, trajectory, and pathway in the engine rolls up from MAP goal-area scores over time. If you only connect one source, connect this.

Required fields

·Student ID (district-internal, hashed on ingest)
·Grade
·Subject (Math, Reading, Language Usage)
·Goal area (e.g. Number Sense, Operations, Fractions)
·RIT score
·Test window (Fall / Winter / Spring)
·Session year

Format

CDF CSV export (standard NWEA report), or rostered export via OneRoster 1.2.

Implementation notes

Goal area naming varies slightly by grade band (K-2 vs 3-5 vs 6-8); the ingestor fuzzy-matches to a canonical goal-area taxonomy. Two prior years improves the projection envelope; three years makes it strong.

Download sample CSV →

Ready to go?

I have data to upload

Go to Upload →

Back to Discover, use any of the three upload zones.

Not today — just exploring

Run synthetic →

Back to Discover, click Run synthetic data. Every feature lights up with a demo cohort.

Not sure your data shape matches?

That’s fine. Send a sample or a schema description and we’ll map it to our ingest. The engine accepts almost any rostered assessment export; the sticking points are usually field naming and format variants, not structure.

Send a data question →