Open edX 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 Open edX already gives you
Open edX emits a rich event stream to its tracking log, and Figures or Aspects provide dashboards over it. Video events include play, pause, seek and speed changes.
What it does not
Open edX has the best native event instrumentation of any open-source platform — genuinely good video interaction data. What it lacks is attention state: whether the tab was focused during playback, and dwell on non-video content.
This is not a criticism of Open edX 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
Deploy as an XBlock for per-unit control, or through theme JavaScript for course-wide coverage. Events can be emitted into the existing tracking log rather than a separate pipeline.
The SDK is under 50KB, adds one script tag, and requires no migration or infrastructure change. It runs alongside Open edX's own reporting rather than replacing it.
Details
Fits the existing pipeline
Because Open edX already has an event architecture, engagement signals can flow into your existing tracking log and be analysed alongside native events rather than in a separate tool.
Works with
Video XBlocks, HTML components, problems and any embedded content.
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 Open edX'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 Open edX content
We'll instrument one module and show you the engagement data before you commit to anything.