Integrations

One script tag, under 50KB. No migration, no infrastructure change, and no replacement of the reporting you already have — engagement data sits underneath it.

The common gap

Every platform reports completion. None report attention.

Learning platforms were built to record navigation and completion, and they do it well. Attention is a different measurement problem — it happens inside a piece of content, which is where the instrumentation has to live.

Engagement analytics for Canvas LMS →

Canvas New Analytics shows page views and participations. It cannot see what happened on the page. How to add content-level attention data to Canvas.

Engagement analytics for Docebo →

Docebo reports completion thoroughly and says nothing about which of a course's twelve lessons cost you the drop-offs. How to close that gap.

Engagement analytics for Moodle →

Moodle's Analytics API predicts at-risk students from logins and submissions. It cannot see attention within an activity. How to add that layer.

Engagement analytics for Cornerstone OnDemand →

Cornerstone Reporting 2.0 proves compliance completion definitively and effectiveness not at all. How to add engagement data via a SCORM wrapper.

Engagement analytics for SCORM →

SCORM reports completion, score and session time — and session time is elapsed, not focused. How to add real engagement data inside the package.

Engagement analytics for TalentLMS →

TalentLMS reports time spent as elapsed wall-clock time, which overstates engagement badly. How to measure focused attention instead.

Engagement analytics for xAPI (Tin Can) →

xAPI can express any learning experience, but most content emits statements only at coarse checkpoints. How to send genuine engagement data to your LRS.

Engagement analytics for Absorb LMS →

Absorb's engagement score measures platform activity, not attention to content. How to add genuine content-level engagement data alongside it.

Engagement analytics for Articulate Rise 360 →

Rise 360 lessons scroll, so reaching the end proves nothing. How to add per-block dwell data and tell reading from scrolling past.

Engagement analytics for Blackboard Learn →

Blackboard's Retention Centre flags students on access patterns. It misses learners who log in reliably and absorb nothing. How to catch those.

Engagement analytics for LTI 1.3 / LTI Advantage →

LTI 1.3 gives you trustworthy learner and course context, and no engagement data of its own. The cleanest route to adding attention analytics.

Engagement analytics for Open edX →

Open edX has the best native event tracking of any open platform, and still no attention state. How to add focus and dwell to your event stream.

Engagement analytics for Articulate Storyline 360 →

Storyline knows exactly which buttons were clicked and nothing about whether anyone was watching. How to add attention data via a JavaScript trigger.

Engagement analytics for Thinkific →

Thinkific video watch time records that the player was playing, not that anyone watched. For paid courses that distinction has revenue attached.

## Three ways to deploy **Platform JavaScript.** Most platforms expose a custom JS or theme setting — Canvas Theme Editor, Moodle additional HTML, Docebo Custom CSS/JS, Thinkific footer code. One line, applies everywhere, easiest to maintain. **Content package.** Where script injection is restricted — common in large enterprise tenancies like Cornerstone — the SDK ships inside the SCORM or xAPI package instead. No platform configuration at all, and it survives platform upgrades. **LTI 1.3.** The cleanest route for education institutions. Register once, works identically across Canvas, Moodle, Blackboard and Brightspace, and provides trustworthy course and learner context out of the box. ## One decision to make first Emotuit runs in two configurations and, for EU deployments, the choice is not cosmetic. **Signals** — behavioural telemetry only. No camera, no biometric data, no emotion inference anywhere in the pipeline. It sits outside the [EU AI Act Article 5(1)(f) prohibition](/guides/eu-ai-act-engagement-analytics) on inferring emotions in workplace and education contexts. **This is the default recommendation for corporate L&D and education.** **Learn** — adds a client-side facial expression layer. Should not be deployed to EU employees or students; intended for market research, UX and media testing. If you are not sure which applies, the [applicability checker](/tools/ai-act-applicability-checker) walks the test in a couple of minutes.