Blackboard Learn 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 Blackboard Learn already gives you
Course Reports cover access patterns and activity. The Retention Centre flags students against rules for missed deadlines, low grades and infrequent access.
What it does not
The Retention Centre is a genuinely useful early-warning system built entirely on access and submission events. It will not flag a student who logs in reliably, opens every item, and absorbs none of it — which is a common failure mode.
This is not a criticism of Blackboard 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 LTI 1.3 tool registered in the Developer Portal, or via theme-level JavaScript on self-hosted instances.
The SDK is under 50KB, adds one script tag, and requires no migration or infrastructure change. It runs alongside Blackboard's own reporting rather than replacing it.
Details
Complements the Retention Centre
Access-based rules catch disengaged behaviour; content-level attention data catches disengaged learning. Feeding both into the same intervention is stronger than either alone.
Works with
Content items, Ultra documents, SCORM packages and embedded media.
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.
Frequently asked questions
Does this replace Blackboard'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 Blackboard content
We'll instrument one module and show you the engagement data before you commit to anything.