xAPI (Tin Can) reports completion, progress and assessment results, but has no visibility into attention within a piece of content. Emotuit adds content-level engagement data — dwell, focus, tab-switching and drop-off position — through a sub-50KB script, alongside existing reporting rather than replacing it.
What xAPI (Tin Can) already gives you
xAPI can express essentially any learning experience as actor-verb-object statements, stored in a Learning Record Store.
What it does not
xAPI is a transport format, not a measurement strategy. It will happily carry engagement data — the constraint is that most content emits statements only at coarse checkpoints: launched, completed, answered. The vocabulary is capable; the instrumentation usually is not.
This is not a criticism of xAPI specifically — it is true of essentially every standard on the market. Completion and navigation data is what these systems were built to record, and they record it well. Attention is a different measurement problem, solved in the content layer rather than the platform layer.
How Emotuit fits
Emotuit emits engagement statements to your existing LRS using standard verbs, at whatever granularity you configure. Nothing new to host; the data joins your existing learning record.
The SDK is under 50KB, adds one script tag, and requires no migration or infrastructure change. It runs alongside xAPI's own reporting rather than replacing it.
Details
Statement design
We emit against experienced, progressed and terminated with engagement in the result extensions, so statements remain queryable with standard LRS tooling rather than requiring a bespoke reader.
Works with
Any conformant LRS — Learning Locker, Watershed, Veracity, SCORM Cloud, Yet Analytics.
What you get
| Metric | Detail |
|---|---|
| Content-level engagement | A score for every section, not one per course |
| Drop-off position | Where sessions actually end |
| Focus-adjusted dwell | Real attention, discounting background tabs |
| Tab-switch rate | The strongest single predictor of disengagement |
| Replay behaviour | Where learners went back — a confusion signal |
| Cohort comparison | A/B test content versions on real attention |
Deployment options
Emotuit runs in two configurations, and for standard deployments in the EU the choice matters.
Signals uses behavioural telemetry only — no camera, no biometric data, no emotion inference. It sits outside the EU AI Act Article 5(1)(f) prohibition on inferring emotions in workplace and education contexts, which makes it the default recommendation for corporate L&D and education.
Learn adds a client-side facial expression layer. It should not be deployed to EU employees or students, and is intended for market research, UX and media testing contexts.
Frequently asked questions
Does this replace xAPI's built-in reporting?
Does it need a webcam?
How long does integration take?
Will it slow the platform down?
See it on your own xAPI content
We'll instrument one module and show you the engagement data before you commit to anything.