Thinkific 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 Thinkific already gives you
Thinkific reports enrolments, completion percentages, video watch time and quiz results.
What it does not
Video watch time is Thinkific's strongest metric and it is still player-level: it records that the player was playing, not that anyone was watching. For a course business where completion drives refunds and reviews, that distinction has revenue attached.
This is not a criticism of Thinkific specifically — it is true of essentially every platform 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
Add via Settings → Code & Analytics → Site Footer Code, or per-course through the custom code block. Content positions map to Thinkific lesson IDs.
The SDK is under 50KB, adds one script tag, and requires no migration or infrastructure change. It runs alongside Thinkific's own reporting rather than replacing it.
Details
Commercially relevant
For paid courses, drop-off position predicts refund requests and poor reviews better than completion rate does. Knowing which lesson loses people is directly a revenue question.
Works with
Video, text, quiz, assignment and multimedia lessons.
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 platform 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 Thinkific'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 Thinkific content
We'll instrument one module and show you the engagement data before you commit to anything.