SCORM 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 SCORM already gives you
SCORM reports a small, fixed set of values: cmi.completion_status, cmi.success_status, cmi.score.raw, cmi.session_time and location bookmarks.
What it does not
SCORM was designed in 2001 to answer "did they finish and did they pass". It does that reliably. cmi.session_time is the closest thing to an engagement signal and it is wall-clock time with the SCO open — it cannot distinguish study from a background tab.
This is not a criticism of SCORM 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
The SDK runs inside the SCORM package alongside your content. It uses the SCORM API for completion as normal, and reports engagement separately, so nothing about your existing LMS reporting changes.
The SDK is under 50KB, adds one script tag, and requires no migration or infrastructure change. It runs alongside SCORM's own reporting rather than replacing it.
Details
Why session_time misleads
Because it is elapsed rather than focused time, cmi.session_time systematically overstates engagement — and the overstatement is largest exactly where content is worst, since disengaged learners are the ones most likely to leave a tab open.
Works with
SCORM 1.2 and SCORM 2004 (all editions), in any conformant LMS.
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 SCORM'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 SCORM content
We'll instrument one module and show you the engagement data before you commit to anything.