Moodle 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 Moodle already gives you
Moodle logs every action, offers activity completion tracking, and the Analytics API ships prediction models including "Students at risk of dropping out".
What it does not
Moodle's analytics are the most capable of any open-source LMS, and they still operate on events, not attention. The at-risk model infers from login frequency, forum posts and submission timing. It cannot see that a student had the activity open in a background tab.
This is not a criticism of Moodle specifically — it is true of essentially every LMS 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 additional HTML block in theme settings for site-wide coverage, or as a local plugin if you want configuration in the admin UI. Content positions map to course module IDs (cmid).
The SDK is under 50KB, adds one script tag, and requires no migration or infrastructure change. It runs alongside Moodle's own reporting rather than replacing it.
Details
Where the data lands
Engagement maps to cmid so it joins cleanly to Moodle's own reporting tables.
Works with
Page, Book, Lesson, SCORM and H5P activities, plus any embedded video.
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 LMS 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.
Moodle's existing Analytics API is a good complement rather than a competitor — it predicts at course level, we diagnose at content level. Both can feed the same intervention.
Frequently asked questions
Does this replace Moodle'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 Moodle content
We'll instrument one module and show you the engagement data before you commit to anything.